Sensible – Deliverable
Analysis of ICT Storage Integration
Architectures
This project has received funding from the European Union’s Seventh Framework Programme for
research, technological development and demonstrations under Grant Agreement No. 645963.
Deliverable number: D1.2
Due date: 30.06.2015
Nature1: R
Dissemination Level1: PU
Work Package: WP1
Lead Beneficiary: 7 – INDRA
Contributing Beneficiaries: EDP, UoN, EMPOWER, ARMINES, USE, THN, INESC, SIEMENS, USE, GPTECH, ADEVICE, SIEMENS SA
Editor(s): Catherine Murphy-O'Connor, INDRA
Reviewer(s): Lee Empringham, UoN
1
Nature:
Dissemination level
R = Report, P = Prototype, D = Demonstrator, O = Other
PU = Public
PP = Restricted to other programme participants (including the Commission Services)
RE = Restricted to a group specified by the consortium (including the Commission Services)
CO = Confidential, only for members of the consortium (including the Commission Services)
Restreint UE = Classified with the classification level "Restreint UE" according to Commission Decision 2001/844 and amendments
Confidentiel UE = Classified with the mention of the classification level "Confidentiel UE" according
to Commission Decision 2001/844 and amendments
Secret UE = Classified with the mention of the classification level "Secret UE" according to Commission Decision 2001/844 and amendments
DOCUMENT HISTORY
Version
Date
Description
0.1
16.02.2015
First draft template (INDRA)
1.0
24.02.2015
New components (Ricardo Bessa, INESC)
1.1
11.05.2015
Nuremberg Generation (THN)
1.2
29.05.2015
Partner components integration (INDRA)
1.3
12.06.2015
Component description update (EMPOWER, SIEMENS, UON, THN)
1.4
19.06.2015
Updated dependencies with partners (SIEMENS, INDRA, EDP, INESC)
1.5
22.06.2015
Updated component description and dependencies and final architecture diagram (UoN, INDRA, ARMINES, GPTECH)
1.6
22.06.2015
Updated component description and dependencies and final architecture diagram (USE, INDRA)
1.7
23.06.2015
Updated component description and dependencies and final architecture diagram (SIEMENS, INDRA)
1.8
10.07.2016
Updated Évora architecture and THN components (EDP, THN, INDRA)
1.9
13.07.2015
Update & preparation for review by M. Depenbusch (K&S)
1.10
22.07.2015
Updated Évora and Nuremberg architecture and components from
(USE, ARMINES, THN)
1.11
22.07.2015
Adjustments by H. Cornet (K&S)
1.12
27.07.2015
Review, comments from L. Empringham (UoN)
1.13
27.07.2015
Adjustments from C. Murphy-O’Connor (INDRA)
1.14
30.07.2015
Updated GPTech components, removed Storage Aggregator component from Nuremberg and updated Nuremberg architecture diagram (GPTECH, INDRA)
TABLE OF CONTENT
1
Introduction
6
1.1 Purpose and Scope of the Deliverable....................................................... 6
1.2 References ................................................................................................... 6
1.2.1 Internal documents
6
1.2.2 External documents
6
1.3 Acronyms..................................................................................................... 7
2
Évora Architecture
8
2.1 Intelligent Electronic devices ................................................................... 10
2.1.1 DSO devices
10
2.1.1.1
2.1.1.2
2.1.1.3
2.1.1.4
MV Storage System
Low voltage storage grid support
EB: Energy Box
Battery management system
2.1.2 Client/Retailer Devices
2.1.2.1 Low voltage storage residential support
2.1.2.2 Residential Photovoltaic System
10
12
16
18
20
20
21
2.2 Low-level Control Systems ....................................................................... 22
2.2.1 DTC
22
2.2.2 HEMS
23
2.3 Real Time Integration platform ................................................................. 25
2.4 Analytics .................................................................................................... 26
2.4.1 DSO Operation Analytics
26
2.4.1.1
2.4.1.2
2.4.1.3
2.4.1.4
MG Emergency Balance
Real Time MV Analytics platform
MV Storage Optimization tool
LV Storage Optimization tool
2.4.2 DSO Planning Analytics
2.4.2.1
2.4.2.2
2.4.2.3
2.4.2.4
Monte Carlo Simulation for Life-Cycle Analysis
MV Network Planning with Storage
Microgrid Dynamic Simulation Tools
Planning tool with storage
2.4.3 Service Provider Analytics
2.4.3.1 Demand Forecast
2.4.3.2 PV Production Forecast
2.4.3.3 Storage aggregator
26
28
29
30
31
31
32
33
34
35
35
36
38
2.5 High-level Applications ............................................................................. 39
2.5.1 DSO Applications
39
2.5.1.1 DMS Data Base
2.5.1.2 DMS
2.5.1.3 Real Time Network Simulator (OTS)
2.5.2 Market Applications
2.5.2.1 Energy Market Service Platform
2.5.2.2 Energy Markets
3
Nottingham Architecture
39
41
43
45
45
46
47
3.1 Residential/Community devices ............................................................... 49
3.1.1 Thermal storage
49
3.1.2 Electrical to thermal controller ImmserSUN
50
3.1.3 Storage inverter
50
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TABLE OF CONTENT
3.1.4
3.1.5
3.1.6
3.1.7
3.1.8
PV panels
Three port converter
3 Phase Inverter
Smart Meter SM
Smart Meter SM3
58
58
59
61
62
3.2 Low-level Control Systems ....................................................................... 63
3.2.1 Meadows Auxiliary Data Collector
63
3.2.2 EBroker
65
3.2.3 Integration Gateway
66
3.3 Real Time Integration platform ................................................................. 68
3.4 Service provider Analytics ....................................................................... 69
3.4.1 Demand Forecast
69
3.4.2 PV Forecast
70
3.4.3 Storage aggregator
72
3.5 High-level Applications ............................................................................. 73
3.5.1 Community applications
73
3.5.1.1 Meadows Weather Station
3.5.1.2 Meadows Data Manager
3.5.1.3 Visualization tool
3.5.2 Market Applications
3.5.2.1 Energy Market Service Platform
3.5.2.2 Energy Markets
4
Nuremberg Architecture
73
73
75
76
76
77
79
4.1 Hardware devices ...................................................................................... 80
4.1.1 Energy storage
80
4.1.1.1 Power to Heat
4.1.1.2 Heat Power Coupling
4.1.1.3 Electrical power and building response
4.1.2 Energy generation
4.1.2.1 Heat Pump
4.1.2.2 CHP-Unit
80
82
83
84
85
85
4.2 Low-level Control Systems ....................................................................... 87
4.2.1 Battery Storage System
87
4.2.2 PV Emulation System
88
4.2.3 Building Uncontrollable Load
89
4.2.4 BEMS
89
4.3 Real Time Integration platform ................................................................. 91
4.4 Analytics .................................................................................................... 92
4.4.1 PV Production Forecast
92
4.4.2 Demand Forecast
93
4.5 Market Applications .................................................................................. 94
4.5.1 Energy Market Service Platform
94
4.5.2 Energy Markets
96
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EXECUTIVE SUMMARY
Executive Summary
This deliverable presents the ICT architectures of the 3 SENSIBLE demonstrators: Évora, Nottingham and Nuremberg as well as the detail of each integrated component. These architectures must meet the requirements of the use cases to be developed in the project [1].
In the case of Évora, the architecture will allow the demonstration of the application of storage
elements to the electrical network. In this case, some of the common key business features are:
storage used for MV and LV operation optimization, participation of independent actors (DER
aggregators, services provider), integration of new markets (ancillary services) or Power Quality
and Continuity of Service. In addition, this demonstrator will also target LV customer flexibility
usage within the wholesale market and grid usage optimization regarding investment deferral.
The Nottingham demonstrator will focus on storage-enabled energy management and energy
market participation of buildings (homes) and communities. At residential level, a number of
customers will have controllable loads or batteries. In order to take full advantage of the potentiality of the flexibility offered, they subscribe to the services of an aggregator. On the other hand,
an independent energy community will be created to enable the gathering of data and practical
demonstration of the benefits that can be delivered to the community. This demonstrator will
make use of a customer’s available flexibility, community based storage and power flow control
capabilities. In addition, several situations will be tested on a implemented micro-grid where the
system has to deal with the limitation of PV penetration or with stability issues in the internal grid
due to the over production of PV or with the lack of PV generation.
With regard to Nuremberg, this demonstrator will focus on multi-modal energy storage in commercial buildings, considering electro-thermal and electro-chemical storage, CHP, and different
energy vectors (electricity, gas). The architecture will allow the demonstration of an increased
percentage of self-consumption, optimized energy procurement from the power grid and managing energy flexibly.
For each architecture, component descriptions have been defined; from intelligent electronic
devices for last mile communications to high level applications integrated in the demonstrator.
For each component, the use case that they fulfil has been included, a description of the functionality, the inputs and outputs of the component and the input and output dependencies with
other components available in the architecture.
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INTRODUCTION
1
1.1
Introduction
Purpose and Scope of the Deliverable
The objective of this document is to provide the ICT architectures of the 3 SENSIBLE demonstrators: Évora, Nottingham and Nuremberg in order to know how the different components will interact to fulfil the use cases described in Deliverable D1.3 [1].
The components integrated in each architecture will be later developed in WP2 Integrationrelated improvements of storage components and WP3 - Energy management.
The document is structured as follows: the Évora architecture is described in Section 2, which
includes the subsections for the component descriptions which are classified as intelligent electronic devices of DSOs and Clients/Retailers, low level control systems, the real time integration
platform, DSO and Service provider analytics and finally the high level systems for DSOs and
market applications. The Nottingham architecture is presented in Section 3, which incorporates
the residential, and community devices, low level control systems of the buildings, the real time
integration platform, service provider analytics and high level applications for communities and
market applications. Finally, the Nuremberg architecture is described in Section 4 containing the
storage and generation devices to be installed in the building, the low level control systems to
manage the resources, the real time integration platform which connects the analytics provided
by an aggregator and the market applications.
For each component, the use case that they fulfil has also been included, a description of the
functionality, the inputs and outputs of the component and together with the input and output dependencies with other components available in the architecture.
1.2
References
1.2.1
Internal documents
[1] SENSIBLE Deliverable D1.3 Use cases and requirements
[2] SENSIBLE Deliverable D3.1 EMS setups and test environments
1.2.2
External documents
No external documents have been consulted for this deliverable.
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INTRODUCTION
1.3
Acronyms
AHU
Air Handling Unit
AMI
Advanced Metering Infrastructure
BEMS
Building Energy Management System
CHP
Combined Head and Power
DDS
Data Distribution Service
DER
Distributed Energy Resources
DMS
Distribution Management System
DSO
Distribution System Operator
DTC
Distribution Transformer Controller
ESB
Enterprise Service Bus
HAN
Home Area Network
IED
Intelligent Electronic Device
SOAP
Simple Object Access Protocol
HEMS
Home Energy Management System
LV
Low Voltage
MV
Medium Voltage
ICT
Information Communication Technology
OTS
Operator Training Simulator
PV
Photovoltaic
RTP
Real Time Platform
SCADA
Supervisory Control And Data Acquisition
UDP
User Datagram Protocol
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EVORA ARCHITECTURE
2
Évora Architecture
This chapter describes SENSIBLE’s technical approach to fulfil the requirements for the Évora
demonstrator which will demonstrate the application of storage elements to the electrical network.
In this case, some of the common business key features are: storage used for MV and LV operation optimization, participation of independent actors (DER aggregators, services provider, etc.),
integration of new markets (ancillary services) or Power Quality and Continuity of Service. In addition, this demonstrator will also target LV customer flexibility usage within the wholesale market
and grid usage optimization regarding investment deferral.
The figure below shows Évora’s SENSIBLE components that will result in a flexible platform. The
DSO infrastructure facilitates the continuous monitoring of the power distribution network with real
time data processing capabilities, including a wide range of devices, protocols and technologies.
The Client and Retailer infrastructure includes the behind the meter components which allow the
management of the generation, storage and loads of the client. The architecture also provides the
mechanisms to integrate the participation of independent actors such as DER aggregators or new
markets.
This overall platform will provide desirable mechanisms on a Smart Grid such as events, triggers
and alarms which take place on the whole system.
Fig. 1: Évora’s architecture schematic
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Different IEDs for last mile communications are located at the different nodes of the network and
are built with different protocols and technologies and therefore, the platform has to provide a
high degree of interoperability and support open standard communication protocols typically used
in the industry.
DSO devices: In the case of the DSO Infrastructure, Energy Box (EB) smart meters located at the LV customers will be integrated with the DTC (Distribution Transformer Controller). These devices will be installed at the secondary substations and will act as LV Network
Controllers and Data concentrators. Apart from the smart meters, DTCs will also integrate
the LV Storage for the Évora demonstrator which correspond to the units owned by the
DSO and will be installed in two main locations: (i) LV bus of the Medium Voltage (MV) / LV
substation (flywheels and electrochemical storage) and (ii) LV feeders (electrochemical
storage). The DMS Data Base collects all the data communicated via DTC and acts as a
middleware between the downstream Smart Grid (and all the grid connected devices) and
the upstream Real Time Platform. On the other hand, in the case of the MV network, a MV
Storage System will be in charge of the power backup of a university campus. The surplus
capacity of the storage can be used for grid support. This storage system, SIESTORAGE,
will be integrated in the SCADA system which is responsible for the operation of the HV and
MV distribution grid. In figure 1, the SCADA system is included inside of DMS Data Base
Component.
Client/Retailer devices: In the case of the Client and Retailer infrastructure, storage and
PV panels will be installed at residential level. The residential storage for the Évora demonstrator consists of electrochemical storage units connected behind the meter and the PV
Residential System consists of a Photovoltaic system connected behind the meter too.
Both, storage and PV residential system can either be owned by the customer or supplied
by a retailer and operated by that player according to a contract agreed between both parts.
The storage and PV System are connected via the HEMS which is a system designed to
monitor, control and manage the energy devices of a household. In the scope of the Évora
Demonstrator, the HEMS will act as a flexibility hub that manages residential loads (controllable or non-controllable), storage devices or renewable energy sources installed in the customer’s house. The HEMS could be also connected to the EB smart meter in order to be
able to have access to the real energy consumption. The HEMS will be integrated with the
Real Time Platform as it can be seen in figure 1.
The Real Time Integration bus provides real time connectivity, through publish-subscribe or request-response mechanisms, among the software packages that will monitor and control the
overall Smart Grid, such as the DSO Analytics module which carries out real-time analysis of the
network current and near-future forecasted status to ensure network quality of service, reliability
and optimal performance in real time.
DSO tools
At the top part of the architecture, DSO tools which are integrated with the Real Time Platform
appear on the left. The Distribution Management System (DMS) allows the coordinated operation
of distribution grids and the Operator Training Simulator (OTS) provides real-time dynamic simulation of the distribution network behaviour (in steady state). In addition, Operation analytics are
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also integrated in the Real Time Platform in order to provide MV and LV Storage Optimization or
micro grid emergency balance. Moreover, Planning Analytics such as MV planning with storage
or micro grid dynamic simulation will be also available in the demonstrator.
Independent actors
At the right top part of the architecture diagram the Real Time Platform integrates the participation of independent actors (DER aggregators, services provider) and new markets (ancillary services). On the one hand, the storage aggregator will calculate the optimal plan for distributed energy storages taking into account global and local constraints and objectives from multiple actors,
such as storage owners and grid operators. Moreover, forecasting analytics such as PV production and demand forecast will be used by the DSO analytics modules.
New market tools
Last, the Energy Market Service Platform combines the enabled storage and resource control
with market signals like price and individual contract constraints, creating new business opportunities for end customers, energy suppliers and DSOs. The Energy Market Service Platform will be
connected to the Energy Markets component that will simulate retail and wholesale markets in
the demonstrator.
In the following sections, the descriptions of the Évora architecture components are explained
including the inputs and outputs of each component as well as the dependencies with the rest of
the components in the architecture.
2.1
Intelligent Electronic devices
2.1.1
DSO devices
2.1.1.1 MV Storage System
COMPONENT INFORMATION
TITLE
MV Storage System - SIESTORAGE
USE CASE
The MV storage system is mainly intended to power backup of a university
campus.
The surplus capacity of the storage can be used for grid support, through P/Q
control.
CONTACT PERSON
Dinis Bucho, dinis.bucho@siemens.com, Siemens SA, EM MS
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
Modular stationary energy storage and power flow management system that
combines fast-acting power regulation function and high performance lithiumion batteries.
The components of the storage system are integrated into a 40´container EHouse. The batteries and power electronics have an optimum and maximum
operating temperature range. Thus, a HVAC system is part of the container to
ensure safety and promote the best asset utilization. The container is also
equipped with fire detection and extinguishing system.
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Storage system data:
Rated Power: 472 kW
Capacity: 360 kWh (at BOL)
Primary Voltages: 30 kV
The SIESTORAGE can be locally operated via the HMI interface (Touch Panel) or remotely operated via standardized bus-interface via protocol (IEC61850,
IEC60870-5-104) or via internet server.
The SIESTORAGE has black-start capability. Black start refers to the initial
power supply required to rebuild a power grid after a full blackout.
The system allows the following grid level control modes:
P/Q
grid tied operation
V/f
islanding operation or synchronization process
The P/Q control functionality allows the control of both the active (P) and reactive power (Q) of the SIESTORAGE system. When synchronized to the grid,
the charge or discharge of batteries and regulation of the inverters are executed to produce the required reactive or inductive power.
The SIESTORAGE will provide P (active power) and Q (reactive power) according to the set point values given locally or remotely.
During start up the SIESTORAGE will synchronize with grid frequency and use
default settings for active power P=0 and reactive power Q=0. After the start,
the SIESTORAGE can receive new set values.
INPUTS
Information to SIESTORAGE
Command Types
Turn On Storage (switch on converters)
Turn Off Storage (switch off converters)
P/Q mode – activation command
Boolean SP
Activate P/Q Set Points
P – Set Point for Active Power
Q – Set Point for Reactive Power
OUTPUTS
Information from SIESTORAGE
P/Q mode is possible
P/Q mode is active
Real
Type
Boolean SP
P – Actual Active Power
Q – Actual Reactive Power
U12 – Actual 1-2 ph-ph Voltage in 30 kV level
U23 – Actual 2-3 ph-ph Voltage in 30 kV level
U31 – Actual 3-1 ph-ph Voltage in 30 kV level
I1 – Actual phase 1 current in 30 kV level
I2 – Actual phase 2 current in 30 kV level
Real
I3 – Actual phase 3 current in 30 kV level
f – Actual Frequency
Actual set point value for P
Actual set point value for Q
SOC – State of charge
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EVORA ARCHITECTURE
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
DMS DATABASE (EDP), MV Storage Optimization Tool (INESC), Optimizing
the MV Distribution Network Operation using available storage resources (INDRA), Integration of a day-ahead storage market to optimize MV Distribution
Network Operation (INDRA)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
DMS DATABASE (EDP), MV Storage Optimization Tool (INESC), Optimizing
the MV Distribution Network Operation using available storage resources (INDRA), Integration of a day-ahead storage market to optimize MV Distribution
Network Operation (INDRA)
2.1.1.2 Low voltage storage grid support
COMPONENT INFORMATION
TITLE
Grid Support Low Voltage Storage for Évora Demonstrator
USE CASE
Low Voltage (LV) Storage for Évora demonstrator corresponds to the units
owned by the DSO and to be installed in two main locations:
LV bus of the Medium Voltage (MV) / LV substation (flywheels and electrochemical storage)
LV feeders (electrochemical storage)
Grid support storage is operated in order to:
Improve the network voltage profile,
Promote voltage balancing in LV feeders
Minimize energy losses
Enable islanded operation through the provision of frequency/voltage regulation.
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
A medium scale storage unit connected will be installed at the MV/LV substation (LV bus) in order to enable islanding operation of the LV network and to
participate in the optimization of LV network operation. The capacity of the
storage unit will depend on the LV network considered for the demonstrator.
The grid-coupling inverter of the grid support storage connected to the secondary substation is expected to:
Provide frequency/voltage regulation automatically after islanding is detected.
Maintain frequency close to nominal value during islanded operation.
Include synchronization algorithm to reconnect the system.
After reconnection the inverter control should be changed to PQ / current
control, where the charging/discharging power can be remotely changed.
Smaller storage units (tens of kW) are expected to be connected to LV feeders,
in order to help manage LV distribution network. Storage grid-coupling inverter
should enable the remote change of the charging/discharging power. Additional power quality functionalities such as voltage balancing can be considered.
Communication type will be through ModbusRTU/TCP (Ethernet/GPRS)
The unit is expected to interact with the DSO LV network management and
control system through the InovGrid smart metering infrastructure, providing
relevant information for the LV network operation tools developed under SENSIBLE Project.
INPUTS
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Reference power (charging/discharging); Enable / Disable local control function; Switch On/Off control
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EVORA ARCHITECTURE
OUTPUTS
The storage unit should provide the following measurements: Charging
/discharging power; State-of-charge; Control mode; Alarms and errors
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
DMS DATABASE [EDP], DMS [Indra, EDP], Real Time Platform [Indra], LV
Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV network islanded operation [INESC], DTC [EDP].
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
LV Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV
network islanded operation [INESC], DTC [EDP]
2.1.1.2.1 3phase Inverter
COMPONENT INFORMATION
TITLE
3phase inverter- Battery power converter
USE CASE
A medium scale storage unit connected will be installed at the LV substation in
order to enable islanding operation of the LV network and to participate in the
optimization of LV network operation. The grid-coupling inverter of the grid
support storage connected to the secondary substation is expected to:
Provide frequency/voltage regulation automatically after islanding is detected.
Maintain frequency close to nominal value during islanded operation.
Include synchronization algorithm to reconnect the system.
After reconnection the inverter control should be changed to PQ / current
control, where the charging/discharging power can be remotely changed.
Smaller storage units (tens of kW) are expected to be connected to LV feeders,
in order to help manage LV distribution network. Storage grid-coupling inverter
should enable the remote change of the charging/discharging power. Additional power quality functionalities such as voltage balancing can be considered.
The unit is expected to interact with the DSO LV network management and
control system through the InovGrid smart metering infrastructure, providing
relevant information for the LV network operation tools developed under SENSIBLE Project.
CONTACT PERSON
Salvador Rodríguez – srodriguez@greenpower.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
The 3phase Inverter is a battery power conversion system from 30 kW and 100
kW. As shown, the power module is coupled to the DC and AC side through a
disconnection module. The disconnection module consists AC and DC switches into a compact single enclosure separated from the power module
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The power converter will be implemented with Sotf-switching, direct paralleling
and transformerless technology
The 3Phase Inverter is able to provide a wide range of grid support services by
means on its Advanced Power Control System so called Software Controller.
Services and functionalities:
Remote control of active and reactive power
Controlled ramp rate
Three modes of operation for Low Voltage Right Through:
1. Maximum reactive power injection.
2. Constant power factor.
3. No current injection.
Automatic power regulation according to frequency variations.
Setting charging and discharging times to suit the needs of the grid and
improve the life cycle of the storage cells
Direct management of the battery via the communication bus
Configurable for multiple targets:
o Frequency regulation.
o Spinning reserve
o Peak shifting
o Black start
o VAR support
o Voltage regulation
o Load levelling
o Droop Control
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Electrochemical Storage [EDP]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
DTC [EDP]
2.1.1.2.2 1 Phase Inverter
COMPONENT INFORMATION
TITLE
D1.2_SENSIBLE_Deliverable_final
1phase inverter- Battery power converter
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The inverter control should be changed to PQ / current control, where the
USE CASE
charging/discharging power can be remotely changed.
The unit is expected to interact with the HEMS management and control system.3
CONTACT PERSON
Salvador Rodríguez – srodriguez@greenpower.es
COMPONENT DESCRIPTION
COMPONENT
DE-
SCRIPTION
The 1phase Inverter is a battery power conversion system of 5 kW. The power
module is coupled to the DC and AC side through a disconnection module. The
disconnection module consists AC and DC switches into a compact single
enclosure separated from the power module
The 1Phase Inverter is able to provide a wide range of grid support services by
means on its Advanced Power Control System so called Software Controller.
Services and functionalities:
•
Remote control of active and reactive power
•
Controlled ramp rate
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES WITH
OTHER
Electrochemical Storage [EDP]
COMPO-
NENTS (INPUTS)
DEPENDENCIES WITH
OTHER
HEMS [EDP]
COMPO-
NENTS (OUTPUTS)
2.1.1.2.3 Flywheel grid storage
COMPONENT INFORMATION PONENT INFORMATION
TITLE
Flywheel storage
USE CASE
Short time power storage system for island operation of low voltage networks
CONTACT PERSON
Matthias Gerlich; Siemens AG; matthias.gerlich@siemens.com
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
Stored Energy: 0.90kWh
Maximum Power: The actual version has a maximum power Pmax of
125kW, given by the used motor
Usable Energy at Pmax: From the actual motor characteristic curve the usable Energy is 0.55kWh; This is the energy amount between maximum rotation speed (9000rpm) and the speed from which the motor is not able to deliver Pmax anymore (5000rpm)
Discharge characteristic: From the points above you get a discharge time at
Pmax of 16s; with less power of course the discharging time will increase
Electrical/Communication architecture:
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o Sinamics120 inverter with CU 320 ( Profinet and Profibus-Interface) as
power electronic
S7 as system automation unit (Profinet-, Profibus- and RS485-Interface)
INPUTS
Island operation signal
Enable/Disable signal
OUTPUTS
Internal measurements (actual power, capacity...) and status (error/warnings,
charge/discharge capability...)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Battery inverter [GPTech]
DEPENDENCIES
Battery inverter [GPTech]
WITH OTHER COMPONENTS (OUTPUTS)
OTHER COMMENTS
Other information about the dependencies management of the component
2.1.1.3 EB: Energy Box
COMPONENT INFORMATION
TITLE
Energy Box: Smart Meter device
USE CASE
The EB (Energy Box) is an energy smart meter device designed according to
the Portuguese specifications. It is a static technology network monitoring
equipment, single-phase or three-phase, prepared for direct connection and
aimed to measure and record the most relevant energy related quantities.
This equipment has remote communication capabilities for network management and AMI (advanced metering infrastructure). It has a set of different functionalities – metering, QoS, load profile, among many others – but it is also
prepared for future new or modified function upgrades, if needed, and it can
support a connection to customer devices, through an adequate HAN communication module connection. It is also equipped with a local optical port for
management purposes.
For the upstream communication, the EB is prepared to communicate using
PLC Narrowband (CENELEC A Band) or GPRS technologies, using the
DLMS/COSEM protocol, a widely used protocol in metering devices.
Regarding the connection to the HAN module, it was selected the “masterslave” ModBUS protocol over an RS485 (RJ12 connector) interface.
CONTACT PERSON
migueljorge.marques@edp.pt
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The EB is prepared to capture and store a wide range of data and transmit it to
remote management central systems, which, in turn, are able to configure and
control the EB functionalities.
Main energy data collected:
Active and reactive energy and power measurements – imported and exported. Three-phase meters also measure the referred quantities per
phase;
Instantaneous measurements – voltage, current, active imported and exported power, power factor and frequency. Three-phase meters also meas-
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ure the referred quantities per phase;
Load profile – capability of recording 6 different energy quantities in a configurable time interval (from 5 to 60 minutes) for at least 92 days with 15min
resolution and 2 channels;
QoS measurements – record of the number and duration of power failures,
under voltage and over voltages events, power up and power down detection;
Billing data – capability to record 6 monthly billings with timestamp, energy
calendar registers configured, power maximum demands and Energy total
registers.
EB also has the following main additional capabilities that aim to endorse the
smart grid challenges:
Events and alarm logs – fraud detection, low battery;
Local and remote relay actuation, for power limit control and power supply
management;
EB has an RTC (real time clock).
Dimensionally, the single phase equipment has the following maximum size (in
mm):
For three phase equipment the dimensions are as follows (in mm):
The EB is compliant with the following main standards:
EN50470, EN60255, EN62052 to EN6205, EN62059 (electricity metering
equipment)
EN60068 (environmental tests)
EN60085 (electrical insulation)
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EN60529 (IP codes)
EN60695 (fire hazard testing)
EN 61000 (electromagnetic compatibility – EMC)
The EB also integrates the following input functionalities:
Smart meters are directly connected to the phases to be measured (power
terminals)
Can receive via remote communication port (PLC Narrowband or GPRS
communication modules), or via local optical port, requests of data readings
or configuration writings.
Can receive via local HAN communication port requests of data readings.
Local button for scrolling through the displayed information or local limited
actions on the equipment (power control relay reconnection, messages
acknowledge)
The outputs of an EB are:
Response to upstream requests of data or configuration writings – wireless
(GPRS) or through the power terminals (PLC Narrowband).
Response to requests of information by the HAN communication port.
LCD display, with the possibility of navigation through the information displayed on the menu structure by clicking on the local button.
INPUTS
Client flexibility data (from the DTC)
OUTPUTS
Client power and voltage
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
DTC [EDP]; DMS Data Base [EDP]; Real Time Platform [EDP]; HEMS [EDP]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
DTC [EDP]; DMS Data Base [EDP]; Real Time Platform [EDP]; HEMS [EDP]
2.1.1.4 Battery management system
COMPONENT INFORMATION
TITLE
Battery integration and management system
USE CASE
Since storage units are expected to be connected to LV feeders, in order to
help manage LV distribution network. Storage grid-coupling inverter will enable
the remote change of the charging/discharging power, while the BMS will be in
charge of the proper operation, integration and balance within the battery stack
series and parallel connection to achieve the power and capacity specified,
monitoring its state and ensuring the operation within the safe operation area.
Thus, in order to control battery performance and safety, a deep understanding
of the performance characteristics and battery failure modes particularly Lithium battery failures is required.
CONTACT PERSON
D1.2_SENSIBLE_Deliverable_final
Joaquin Álvarez – jalvarez@gte.esi.us.es
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COMPONENT DESCRIPTION
COMPONENT
DE-
SCRIPTION
For the application proposed, BMS encompass not only the monitoring and
protection of the battery but also methods for keeping it ready to deliver full
power when called upon and methods for prolonging its life. This includes everything from controlling the charging regime to planned maintenance, such as:
•
BMS board HW: Specification and HW design and manufacturing according to storage system characteristics
•
BMS algorithm implementation and integration in microprocessor with the
aim to monitor and control:
o
Voltage: total voltage, voltages of cells packs in paralleling connection, minimum and maximum cell voltage or voltage of periodic
taps
o
Temperature: average temperature, coolant intake temperature,
coolant output temperature, or temperatures of individual cells
o
State of charge (SOC) or depth of discharge (DOD): to indicate the
charge level of the battery
o
State of health (SOH), a variously-defined measurement of the
overall condition of the battery
o
Current: current in or out of the battery
o
Remaining useful life (RUL): estimation and prediction of the battery life according to obtained parameters
o
Charge/discharge control strategy: This is a key feature of BMS to
avoid any damage to cell batteries
•
Electric connection and switchgear between battery and power converter.
Battery stack protection is included to avoid operation out of safe conditions
•
Communications between BMS and power converter control (Modbus, Can
Bus, Can Open, to be determined with inputs/outputs dependencies)
•
Energy demands management of the power converter to battery in the
expected applications. The aim is to minimize the current losses on the
battery by designing power saving techniques into the applications circuitry
and thus prolong the time between battery charges.
INPUTS
Reference power (charging/discharging); Enable / Disable local control function;
Switch On/Off control
OUTPUTS
The storage unit should provide the following measurements: Charging
/discharging power; State-of-charge; Control mode; Alarms and errors, battery
measurements (actual power, capacity,...); charge/discharge capability
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH
Electrochemical Storage [EDP]
OTHER COMPONENTS (INPUTS)
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DEPENDENCIES
OTHER
WITH
DTC [EDP], 3phase inverter [GPTech], HEMS [EDP]
COMPONENTS
(OUTPUTS)
2.1.2
Client/Retailer Devices
2.1.2.1 Low voltage storage residential support
COMPONENT INFORMATION
TITLE
Residential Storage for Évora Demonstrator
USE CASE
Residential storage for Évora demonstrator consists of electrochemical storage
units connected behind the meter that can either be owned by the customer or
supplied by a retailer and operated by that retailer according to a contract
agreed between both parts.
CONTACT PERSON
Migueljorge.marques@edp.pt; clara.s.gouveia@inescporto.pt
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The storage is connected behind the meter and can be controlled with three
distinct objectives:
Local energy management
DSM services
Provide grid supporting services
The residential storage maximum capacity is expected to be lower than 5 kW/
5 kWh.
The residential storage inverter will be single-phase similarly to the consumer
connection to the distribution network.
The unit is expected to interact with the DSO LV network management and
control system through a Home Energy Management System (HEMS), providing flexibility services not only to the LV network both for interconnected and
islanded operation, but also to enable flexibility and market participation.
The Residential Storage system is capable of communicating either using PLC
or using wireless technologies (ZigBee, Wi-FiX) or other like RS485 or Modbus, with an HEMS system.
INPUTS
Reference power (charging/discharging); Enable / Disable local control function
OUTPUTS
Current Set-point (Active power)
Current state-off charge
Availability to participate in grid services/network optimization.
Alarms or errors
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Real Time Platform [Indra], LV Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV network islanded operation [INESC]; HEMS [EDP];
Energy Market Service Platform[Empower]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
LV Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV
network islanded operation [INESC]. HEMS [EDP]; Energy Market Service
Platform [Empower].
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2.1.2.2 Residential Photovoltaic System
COMPONENT INFORMATION
TITLE
Residential Photovoltaic (PV) System
USE CASE
The PV Residential System for the Évora demonstrator consists of a Photovoltaic system connected behind the meter that can either be owned by the customer or supplied by a retailer and operated by that player according to a contract agreed between both parts. The PV System is connected via the HEMS,
that manages how the generated power is used and what are the appropriate
set points for each situation.
CONTACT PERSON
migueljorge.marques@edp.pt
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
A Photovoltaic (PV) System is a system designed to absorb and convert sunlight into electricity. It consists of two main components:
Solar Panel
DC/AC Inverter
A Solar Panel consists of an array of solar modules that, in turn, are arrays of
solar cells. These solar cells are typically made up of crystalline silicon, in
which the photovoltaic effect (by which the energy radiated from the sun converts into DC electrical power) takes place.
The DC/AC inverter, which is single-phased (similarly to the consumer connection to the distribution network), is intended to convert the DC power generated
by the solar panel into grid-adequate AC power.
Most converters are also equipped with a maximum power point tracker
(known as a MPPT), a system that maximizes the total generated power by
adjusting the output voltage and, hence, the equivalent output current (according to A current-voltage curve).
The rated power of the solar panels is expected to be between 1 kWp and 4,5
kWp.
The PV system is capable of communicating either using PLC or using wireless technologies (ZigBee, Wi-FiX) or other like RS485 or Modbus, with an
HEMS system.
INPUTS
Setpoint from the HEMS
OUTPUTS
Client generation to the HEMS
Client availability to the HEMS
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Real Time Platform [Indra], LV Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV network islanded operation [INESC]; HEMS [EDP];
Energy Market Service Platform[Empower]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
LV Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV
network islanded operation [INESC]. HEMS [EDP]; Energy Market Service
Platform [Empower].
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2.2
Low-level Control Systems
2.2.1
DTC
COMPONENT INFORMATION
TITLE
LV Network Controller and Data concentrator device
USE CASE
The DTC (Distribution controller Device) is an intelligent device that controls
the low voltage network it is associated with. It is an equipment to be installed
in secondary substations aimed to locally control and monitor the electricity
network which includes measurement, automation, processing, interface and
communication abilities.
In addition to the local control and supervising functionalities of the secondary
substation transformer, the DTC also concentrates the data collected by all the
Energy Boxes (Portuguese smart meters) installed downstream the electrical
grid of the respective power transformer and send it to the upstream central
systems. The DTC also intermediates the interaction between the utility central
management systems and the Energy Boxes.
CONTACT PERSON
migueljorge.marques@edp.pt
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The two main functions of a DTC are the following:
Management of the Energy Box (smart meters) infrastructure: it includes
the configuration, collecting of data and send of remote controls to the Energy Box installed on the respective low voltage network. It also includes
the management of the communication network (using different technologies) with the smart meters and with the central information systems.
Management and monitoring of the low voltage network: analysis of the
data collected on the power transformer and on the smart meters (events,
alarms, load profiles) to the management of the low voltage network. Monitoring and management of signals collected by external sensors and devices installed on the secondary substation.
For this purpose the DTC presents the following main functionalities:
Monitoring of the network: it includes voltage, energy and power measurements, load profiles for a minimum of 6 different energy quantities and with
configurable time interval (from 15 to 60 minutes), detection of voltage and
current phase unbalances on the power transformer and QoS monitoring
(TP overload, phase failures, under voltages and over voltages, current under load or current overload).
Communications module: DTC has the capability of communication through
an Ethernet (TCP/IP) and, optionally, GPRS/UMTS interface with EDP central systems to exchange all type of commercial and technical information,
using web services and FTP protocol. For the LAN communications, DTC
uses the PLC narrowband (CENELEC A band) technology to communicate
with the smart meters (Energy Boxes). Finally, regarding TAN communication, the DTC has interfaces RS485 with ModBus or with DLMS/COSEM in
order to communicate with monitoring, sensing equipment, Energy Boxes
installed on the secondary substation and some other future applications.
Man-Machine interface (MMI): In order to guarantee the possibility of standalone configuration of the equipment, the DTC has a web configuration tool,
locally or remotely accessible and protected with a password.
Distribution Substation control, including the control of a circuit breaker that
enables the islanding mode.
Dimensionally the DTC should have a maximum of 220 x 340 x 133 mm
(height, width, length).
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In addition to compliance with the EDP specifications, DTC also have to be
compliant with the following main standards:
IEC 60068 (environmental requests)
IEC 61000 (electromagnetic compatibility - EMC)
IEC 60255 (electrical relays)
IEC 61439 (low voltage switchgear and control gear)
EN 61709 (Reliability)
EN 50102 (IK code)
The DTC holds the following interfaces:
WAN interface – Ethernet port and, optionally, GPRS modem, antenna.
LAN interface – direct connection to the low voltage power network.
TAN interface – RS485 ports.
Voltage and current inputs
INPUTS
Storage data (SoC, On/off control, availability, voltage and set pointsX); Secondary Substation data (Voltage, power, Islanding circuit breaker data, Interlock to resync with the MV GridX); Client data (Voltage, powerX)
OUTPUTS
Storage data (SoC, On/off control, availability, voltage and set pointsX); Secondary Substation data (Voltage, power, Islanding circuit breaker data, Interlock to resync with the MV GridX); Client data (Voltage, powerX)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Energy Box [EDP]; Real Time Platform [Indra], LV Storage Optimization Tool
[INESC], MG Emergency Balance [INESC], LV network islanded operation
[INESC]; HEMS [EDP]; DMS Data Base [EDP]; GTStorage [EDP]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Energy Box [EDP]; Real Time Platform [Indra], LV Storage Optimization Tool
[INESC], MG Emergency Balance [INESC], LV network islanded operation
[INESC]; HEMS [EDP]; DMS Data Base [EDP]; GTStorage [EDP]
2.2.2
HEMS
COMPONENT INFORMATION
TITLE
Home Energy Management System (HEMS) device
USE CASE
A Home Energy Management System (HEMS) is a system designed to monitor, control and manage the energy devices of a household. In the scope of the
Évora Demonstrator, the HEMS will act as a flexibility hub that manages residential loads (controllable or non-controllable), storage devices or renewable
energy sources installed in the customer’s house. It is connected behind the
meter and can either be owned by the customer or supplied by a retailer and
operated by that retailer according to a contract agreed between both parts.
CONTACT PERSON
migueljorge.marques@edp.pt
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
A HEMS includes (but is not limited to) the following features:
Real time monitoring of the energy devices;
Interface with the data from the smart meter, with information of the real
energy consumptions;
Monitoring of the energy produced from residential alternative energy
sources (PV, windX);
Monitoring of data from residential energy storage devices (e.g. Batteries);
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Monitoring (and controlling) domestic devices (TV sets, fridge, Air Conditioning, PC, etc.) – Program the on/off cycles and power consumption
measurements;
Control and measurement of electric circuits (e.g. swimming pool circuits,
Air Conditioning, lighting, etc.)
The following devices are included in a HEMS:
Smart plugs: “Plug-n-play” devices installed on the customer premises between the wall plug and the electric equipment to be monitored / controlled.
It can have energy metering and remote control capabilities;
Circuit meters: devices installed on the customer’s electric control panel,
with the ability to monitor and control that circuit;
Gateway: central device with intelligence to control the actions defined by
the customers (either locally or on a web platform). This device is the communications hub with all the remaining devices installed on the customer’s
house and it also has the ability to send the collected data to the web server upstream;
The information collected from the home energy devices is managed and aggregated to interact with the higher-level systems upstream. The output will be
flexibility for each customer, i.e. a value that represents how much each of
them can contribute to the whole system and act, for example, as a virtual
power plant for the grid. This flexibility, which can be fine-tuned by each customer, translates to a number of set points to the controllable energy devices of
the customer’s house (micro production, storage or controllable loads).
The HEMS can be seen as a platform for future developments in order to accommodate new devices and services, such as:
Lighting control –not only turn on and off;
Security services;
Heat/cold control;
The HEMS is capable of communicating either using PLC or using wireless
technologies (ZigBee, Wi-FiX) or other like RS485 or Modbus, with both the
energy devices downstream and the smart meter upstream. It also communicates to higher-level systems and platforms.
The smart plug is connected between a wall socket and a home appliance to
monitor and control that appliance. It can also measure relevant electric quantities (energy, power, current voltage) and send it to the HEMS gateway.
The circuit meter is an equipment with the same functions as the smart plug
but it is installed on the customers control panel to monitor and control electric
circuits (direct or indirect connection), such as heating system, a lighting circuit
or a swimming pool pump, for example.
The gateway is a central device that collects all the data from the smart plugs
and the circuit meters as well as from the smart meter.
Upstream from the HEMS gateways, all the data from each individual HEMS
will be collected (including the availability of each client) and adapted to interact with the upstream (Real Time) Platform.
INPUTS
D1.2_SENSIBLE_Deliverable_final
Information collected by smart plugs;
Information collected by circuit meters;
Client information from the smart meter;
Service configurations received from the customer web portal;
Storage data from the storage devices;
Power generation data from the residential PV system;
Client controllable devices’ set point, from the upstream platform;
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OUTPUTS
Relay state changing of smart plugs according to configurations defined by the
customer;
Relay state changing of circuit meters according to configurations defined by
the customer;
Presentation of the information collected on the customer web portal;
Load management scheduling.
Client data to the upstream platform;
Orders and set points to the PV system and the client storage;
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Real Time Platform [Indra], LV Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV network islanded operation [INESC], HEMS [EDP],
Residential Storage [EDP], Residential PV [EDP], Energy Box [EDP] ; Energy
Market Service Platform[Empower].
DEPENDENCIES
WITH OTHER COMPONENTS
(OUTPUTS)
LV Storage Optimization Tool [INESC], MG Emergency Balance [INESC], LV
network islanded operation [INESC], Real Time Platform [Indra], Residential
Storage [EDP], Residential PV [EDP], Energy Market Service Platform[Empower].
2.3
Real Time Integration platform
COMPONENT INFORMATION
TITLE
Real Time Integration Platform
USE CASE
iSpeed is an integrated platform for real-time data acquisition and processing,
with the ability to handle large volumes of information at low latency. Its main
objective is to increase productivity and effectiveness in the exchange and
management of information generated by various monitoring and control applications, while reducing the chances of error in data manipulation. It is characterized by providing a fast and reliable exchange of information between the
actors connected to it, ensuring at all times the availability of information at low
and flexible coupling between them. In addition, this platform provides the necessary tools to perform real-time distributed data processing and also the
means to persist the information that will subsequently treated in batch analytic
processes.
CONTACT PERSON
Catherine Murphy-O’Connor cmurphy@indra.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
iSpeed is a platform developed under the XTPP concept (eXtreme Transaction
and Processing Platform) which is capable of providing the following services
anywhere in the data acquisition and processing network:
Unified Data Model: based on specific domain models (IEC 61970-61968,
IEC 61850X) and provides storage and retrieval capabilities in different
time horizons and data access technologies.
Real-Time Messaging Service: capable of supporting real-time communications between any of the systems involved in the management, monitoring
and network operation.
Complex Event Processing Service: to handle events both locally of an
asset (filtering and data analysis), and capable of processing large aggregates and global events.
iSPEED is a SOA architecture (Service Oriented Architecture) based on the
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publish/subscribe data distribution paradigm. It has been built over the DDS
(Data Distribution Service) middleware standard for data distribution, where
aspects such as transparency and failover, distribution and deployment of
software without service interruptions, and data delivery are specified. Moreover, the middleware that provides the messaging service in the real-time platform is capable of being loaded on computers with low processing capacity, so
that data can be collected directly from low-level nodes.
One of the key pieces of a real-time platform is the communication middleware.
It is a logical area in which the information, in data structures form, is shared
between two types of actors: publishers and subscribers.
Publishers are the elements that have information of interest to other systems and provide this information on the bus.
Subscribers are those actors who are awaiting information that will later
process.
The Real Time integration platform DDS data structures for real time communication in the Évora demonstrator will be defined and developed according to
the data exchange magnitudes included in D3.1 [2], in order to fulfil the requirements of the use cases defined in D1.3 [1].
INPUTS
As iSPEED is the middleware communication infrastructure, there will be not
explicit inputs. The different systems connected to the platform will exchange
the information using the SENSIBLE data structures that are going to be defined in the project taking into account the demonstrator use cases.
Interfases with the platform will be carried out through DDS technology, using
the Real-Time Publish-Subscribe (RTPS) protocol designed to be used over
UDP, in case of Real Time data or traditional REST-Json software architecture
style, using HTTP protocol, in case of historical data.
OUTPUTS
As iSPEED is the middleware communication infrastructure, there will be not
explicit outputs. The different systems connected to the platform will exchange
the information using the SENSIBLE data structures that are going to be defined in the project taking into account the demonstrator use cases.
Interfases with the platform will be carried out through DDS technology, using
the Real-Time Publish-Subscribe (RTPS) protocol designed to be used over
UDP, in case of Real Time data or traditional REST-Json or SOAP software
architecture style, using HTTP protocol, in case of historical data.
PRODUCT DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
DMS DATABASE [EDP], DMS [IndraEnergy Services Market Platform [EMPOWER], Real Time DSO Analytics [Indra], Operation analytics [INESC],
Planning analytics [INESC], OTS simulator [Indra], Forecasting [Armines], Energy Market Service Platform [EMPOWER], HEMS [EDP]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
DMS DATABASE [EDP], DMS [IndraEnergy Services Market Platform [EMPOWER], Real Time DSO Analytics [Indra], Operation analytics [INESC],
Planning analytics [INESC], OTS simulator [Indra], Forecasting [Armines], Energy Market Service Platform [EMPOWER], HEMS [EDP]
2.4
Analytics
2.4.1
DSO Operation Analytics
2.4.1.1 MG Emergency Balance
COMPONENT INFORMATION
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TITLE
Micro grid Emergency Balance Tool
USE CASE
MG Emergency Balance tool is a software application which is responsible for
managing flexible loads and grid support storage in order to ensure a secure
islanding operation. The tool monitor s the MG operating state prior and after
islanding and identifies a priori the most appropriate actions to take, if a certain
disturbance affecting MG load and generation balance occurs. Its main objective is to increase resilience of the LV network when operating under emergency conditions, taking advantage of flexible resources such as storage and
loads.
CONTACT PERSON
Clara Gouveia (cstg@inescporto.pt) INESC TEC
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
MG Emergency Balance module will use the information sent by the smart
meters and storage controllers in order to characterize the MG operating conditions and update the MG emergency operation strategies according to the actual operating state. The main objectives of this module are:
Minimize energy not supplied and time of service interruption.
Ensure that the MG has sufficient capacity to ensure frequency regulation
following a given disturbance.
Maintain frequency excursions within admissible limits.
The application is designed to ensure a secure islanding of LV systems and
ensure energy balance during autonomous operation, considering the variability of loads and renewable based micro generation.
It requires online monitoring of the islanded system in order to assess the security of operation and characterize the flexibility of controllable resources such
as grid supporting storage.
Based on the current state of the MG the algorithm determines the best strategy (load control, storage dispatch) to maintain the security of the islanded system.
INPUTS
The module requires real-time information from the LV network controllers,
smart metering infrastructure namely: Power exchanged with the MV system,
total micro generation power, total power consumption, reserve capacity provided by storage and flexible loads. Specifically from the storage controllers the
module requires its state of charge, specific alarms from BMS and power exchanged with the LV network.
OUTPUTS
As outputs the module will return a set of control signals to controllable loads
and grid supporting storage.
Controllable loads – On/Off signals during emergency operation
Grid supporting storage – Reference charging/discharging power.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
LV grid support storage, Low voltage storage for residential support, Home
Energy Management System (HEMS), Energy Box (EB – smart meters), Distribution Transformer Controller (DTC), DMS Database, Real Time Platform,
Net-load and renewable energy forecast.
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
LV grid support storage (flywheel, 3phase Inverter (GPTECH)), Low voltage
storage for residential support, Home Energy Management System (HEMS),
Energy Box (EB – smart meters), Distribution Transformer Controller (DTC),
DMS Database, Real Time Platform.
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2.4.1.2
Real Time MV Analytics platform
COMPONENT INFORMATION
TITLE
DSO Analytic module
USE CASE
Real time analysis of the network current and near-future forecasted status
identifying local storage regulation services and operational set-point in order
to ensure the network quality of service, reliability and optimal performance.
CONTACT PERSON
Juan Prieto Vivanco, Indra
jprietov@indra.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
Independent Analytic server connected to the real time grid and storage control
devices information flow, allowing the automatic schedule execution of complex network analysis algorithm and automatic submittal of the analysis results
to the DSO operator and infield devices
The analytic server is a flexible grid analytic framework supporting the execution of power analysis algorithms in real time.
The functions provided by the platform to support the algorithms execution are:
Real Time in memory updated view of the network topology and relevant
measurements. Measurements and changes in topology are continuously
read from the Real Time platform iSpeed and updated in memory for analysis use
Automatic publishing of analysis results through iSpeed.
Schedule and customizable execution of analysis algorithms.
The main analytic functionalities identified for Sensible project to include in the
analytic server are:
State estimation.
Node demand/generation forecast.
Power flows.
Volt/Var control.
Optimal power flows: Optimal storage set-point calculation, optimal services
assignment, optimal service restoration.
INPUTS
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Grid measurements flow from the SCADA.
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Switching orders from the SCADA
Network topology.
Network electric characteristics.
Storage controllers RT available flexibility and operation conditions.
Market services matching to DSO.
OUTPUTS
Consistent network status.
Network status analysis results.
Forecasted operation limitations.
Optimal storage set-points to maintain supply quality and network efficiency.
DSO Storage services requests.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Real Time Platform, inGRID DMS. DSO Distribution Management System.
OTS, DMS DATABASE/MVStorage System
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Real Time Platform, inGRID DMS. DSO Distribution Management System.
OTS, DMS DATABASE/MVStorage System
2.4.1.3 MV Storage Optimization tool
COMPONENT INFORMATION
TITLE
MV Storage Optimization Tool
USE CASE
MV Storage Optimization Tool is responsible for managing local storage devices connected to the MV/LV substation (but on the MV side) in order to solve
technical problems at the MV network level (e.g. degradation of voltage profiles, losses). The storage devices are assumed to have two objectives: (a)
backup of an installation (or building) and (b) the residual capacity is used for
grid support.
The regulatory framework assumes that the DSO will be able to contract these
services either in a new ancillary services market (where the different agents
including storage may bid their flexibility), in bilateral contracts or as a regulated service.
CONTACT PERSON
Ricardo Bessa (ricardo.j.bessa@inescporto.pt) INESC Porto
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The first step of the MV Storage Optimization Tool consists in estimating the
available storage capacity that can be used for grid support, by considering the
forecasted load of the installation. Based on the forecasted load and corresponding uncertainty, the risk of not having sufficient backup capacity is calculated and the required storage capacity is defined based on the preferred risk
(defined by the user).
The MV Storage Optimization Tool will optimize the storage operating strategy
for a pre-defined time horizon (e.g., next hours, day) in order to minimize the
power losses and operating costs (including degradation costs if available),
while respecting the technical constraints of the network and considering the
future states of the MV grid.
INPUTS
Dynamic inputs (i.e., change with hours of the day and operating conditions)-Uncertainty load forecast (temporal trajectories) of the installation that uses
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storage as backup;
net-load forecasts for each node of the MV grid;
current state of the charge (SoC) of the storage device;
network topology;
Current status of the MV grid.
Static inputs:
electrical characteristics of the grid;
Storage model and characteristics (e.g., degradation curve).
Operating strategy of the storage devices for the next hours/day. This output
generates a set of control step-points for the storage steady-state operating.
OUTPUTS
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
MV storage system, Distribution Transformer Controller (DTC), DMS Database, SCADA, Real Time Platform, Net-load energy forecast.
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
MV storage system, Distribution Transformer Controller (DTC), DMS Database,
SCADA, Real Time Platform.
2.4.1.4 LV Storage Optimization tool
COMPONENT INFORMATION
TITLE
LV Storage Optimization Tool
USE CASE
LV Storage Optimization Tool is responsible for managing local distributed
storage devices connected directly to LV grid or central storage systems connected at the level of the secondary substation (MV/LV) but that are property of
the Distribution System Operator in order to solve technical problems in the LV
grid (namely related to voltage issues). The storage devices are to be coordinated with renewable generation at the LV level as well as with demand side
management schemes (controllable loads).
CONTACT PERSON
André Madureira (andre.g.madureira@inescporto.pt) – INESC Porto
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The LV Storage Optimization Tool will be based on a multi-temporal Optimal
Power Flow (OPF) algorithm for optimizing micro-grid operation while minimizing the deviation between actual and expected net load profile by making use
of storage connected to the secondary substation, LV distribution feeders and
buildings, as well as demand-side management.
The main objective of the LV Storage Optimization Tool will then be to optimize
the storage and controllable loads operating strategy for a pre-defined time
horizon (e.g., next hours, day) in order to minimize the power losses, improve
the quality of service and ensure continuity of service, while respecting the
technical constraints of the network (namely in terms of voltage profiles) and
considering the future states of the LV grid.
INPUTS
Dynamic inputs (i.e., change with hours of the day and operating conditions
net-load forecasts for each node of the LV grid;
renewable generation forecasts of micro-generation;
current state of the charge (SoC) of the storage device;
Current status of the LV grid.
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Static inputs:
network topology
electrical characteristics of the grid and all its components;
storage model and characteristics (e.g., degradation curve);
Controllable loads model and characteristics (e.g. periods of availability).
Operating strategy of the storage devices and controllable loads for the next
hours/day. This output generates a set of control step-points for the storage
and loads steady-state operation.
OUTPUTS
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
LV grid support storage, Low voltage storage for residential support, Home
Energy Management System (HEMS), Energy Box (EB – smart meters), Distribution Transformer Controller (DTC), DMS Database, Real Time Platform, Netload and renewable energy forecast.
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
LV grid support storage, Low voltage storage for residential support, Home
Energy Management System (HEMS), Energy Box (EB – smart meters), Distribution Transformer Controller (DTC), DMS Database, Real Time Platform.
2.4.2
DSO Planning Analytics
2.4.2.1 Monte Carlo Simulation for Life-Cycle Analysis
COMPONENT INFORMATION
TITLE
Monte Carlo Simulation for Life-Cycle Analysis
USE CASE
Monte Carlo LCA Analysis Tool is responsible for performing life-cycle analysis
of storage units to support micro-generation (primarily photovoltaic) installed in
the LV network. The Monte Carlo method can sequentially reproduce the operation of the system by including all chronological aspects such as renewable
energy variability, unplanned outages, etc. This methodology simulates the
storage performance under different operations strategies and obtains important statistics for the storage and grid systems (e.g. average number of
charges/discharges, amount of renewable energy not spilled).
CONTACT PERSON
Leonel Carvalho (lcarvalho@inescporto.pt) – INESC Porto
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
The most accurate way to assess the number of discharges and the respective
depth of discharge is to simulate the operation of the storage system under the
selected operating strategy and taking into account the chronological behaviour
of the renewable energy sources (RES). The Sequential Monte Carlo Simulation (SMCS) method is able to sample system states by “setting in motion” a
virtual or fictitious clock creating the “life story” of the power system. Since this
approach can sequentially reproduce the operation of the system, it is easy to
include all chronological aspects such as time and spatially correlated load
models, RES variability (normally via chronological series), customer damage
functions per area or per bus, unplanned outages of generating units and feeders, etc.
This tool uses the SMCS method to perform life-cycle analysis of storage
equipment (which can be stationary or rely on other type of technology) to support micro-generation (primarily solar) installed in the LV network. From the
storage system point of view, the methodology proposed will allow simulating
the performance of storage systems when integrated into a LV network under
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different operation strategies and obtain important statistics, such as average
number of charges/discharges, the average energy charged/discharged, and
consequently, the expected life time of the storage system in a realistic application. From the grid point of view, the methodology will allow quantifying the
achievable reduction on the amount of renewable energy spilled and the benefits not only in terms of the adequacy of supply but also in overall operation
costs.
INPUTS
LV network topology
Electrical characteristics of the grid and its components
Storage model and characteristics (e.g., degradation curve)
RES and load time series
OUTPUTS
Statistics and reliability indices related to storage and network operation; expected life time of the storage system.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Electrochemical storage model [Siemens]; LV Storage Optimization Tool [INESC]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
The outputs are not used by any component to be developed (or tested) in the
project.
2.4.2.2 MV Network Planning with Storage
COMPONENT INFORMATION
TITLE
MV Network Planning with Storage
USE CASE
MV Network Planning Tool is responsible for assessing the impact of local
storage flexibility in MV distribution network planning (e.g., which decisions the
DSO should take in terms of network reinforcement). Different operating scenarios (or regulatory frameworks) of storage are considered: (a) storage devices are directly controlled by the DSO (e.g., losses minimization); (b) storage
units are not owned or directly operated by the DSO (e.g., profit maximization).
CONTACT PERSON
Ricardo Ferreira (rjcf@inescporto.pt) – INESC Porto
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The MV Network Planning Tool consists in a simulation tool that integrates
local storage flexibility in distribution network planning, assuming different scenarios and objectives for storage operation. The tool conducts multi-criteria
decision making, exploring trade-offs such as minimize the total cost (investment and operation) and maximize the integration of energy from renewable
sources, subject to a set of technical constraints (e.g., voltage levels, branch
limits). A cost benefit analysis of storage vs. traditional planning decisions is
also provided by the tool.
The tool includes a module that generates typical profiles of storage operation
assuming that a third-party owns these devices. The possibility of using storage
flexibility as an ancillary service for the DSO in order to solve voltage and congestion problems is taken into account.
INPUTS
MV network topology
Electrical characteristics of the grid and its components
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Storage models and characteristics (e.g., degradation curve)
Hourly market price curves
OUTPUTS
Set of decisions concerning network planning, e.g. cost/benefit analysis of storage, grid reinforcement, investment deferral estimation
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
This component does not use any input from a component to be developed (or
tested) in the project.
DEPENDENCIES
WITH OTHER COMPONENTS
(OUTPUTS)
The outputs are not used by any component to be developed (or tested) in the
project.
2.4.2.3 Microgrid Dynamic Simulation Tools
COMPONENT INFORMATION
TITLE
Microgrid Dynamic Simulation Tool
USE CASE
The Microgrid dynamic simulation tool is a computational tool developed in
MATLAB®/Simulink® , designed to validate the operation of Microgrid system
operating under emergency conditions, namely Microgrid islanding and black
start procedures.
CONTACT PERSON
Clara Gouveia (cstg@inescporto.pt)- INESC TEC
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The platform enables the dynamic simulation of different MG test system during
emergency operation mode, namely MG islanded operation and local restoration procedure.
The MG full model is organized using a modular and hierarchical approach,
being divided in virtual subsystems representing the different components of
the Microgrid (micro generation, storage, EV and loads) connected by threephase four-wire LV feeders.
The MG system can be tested under balanced/ unbalanced operating conditions and includes the innovative ancillary services to be provided by energy
storage units. The main features of this tool are:
Representation of the LV voltage network as a three-phase four-wire system.
Includes the models for energy storage and MS single-phase power electronic interfaces.
Enables testing grid supporting storage provision of ancillary services for
improving Microgrid resilience when operating in emergency conditions (islanding and black start).
Analyses of Microgrid voltage unbalance problems.
INPUTS
The tool can include information of the LV network topology and feeder characteristics, active and reactive power consumption and storage and micro generation inverter characteristics (if provided).
OUTPUTS
Expected dynamic behaviour of the LV system operating under Microgrid concept, regarding: system frequency, voltage, expected voltage unbalance.
COMPONENT DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
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DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
This component does not use any input from a component to be developed (or
tested) in the project.
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
The outputs are not used by any component to be developed (or tested) in the
project.
2.4.2.4 Planning tool with storage
COMPONENT INFORMATION
TITLE
LV Network Planning with Storage
USE CASE
MV Network Planning Tool is responsible for assessing the impact of local
storage flexibility in MV distribution network planning (e.g., which decisions the
DSO should take in terms of network reinforcement). The case of an islanding
mode is chosen for dimensioning
CONTACT PERSON
Robin GIRARD, robin.girard@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DE-
SCRIPTION
The LV Network Planning Tool consists in a simulation tool that integrates local
storage flexibility in distribution network planning, assuming different scenarios
and objectives for storage operation. The tool conducts multi-criteria decision
making, exploring trade-offs such as minimize the total cost (investment and
operation) and maximize the integration of energy from renewable sources,
subject to a set of technical constraints (e.g., voltage levels, branch limits). The
tool will rely on the use of an OPF to simulate the optimal operation cost of a
given situation.
The dimensioning and positioning of the storage will be applied to the case of
Evora to insure the possibility of islanding mode.
INPUTS
LV network topology
Electrical characteristics of the grid and its components
Storage models and characteristics (e.g., degradation curve)
Hourly market price curves (not mandatory)
Load curves measured by smart meters
OUTPUTS
Set of decisions concerning network planning, e.g. cost/benefit analysis of
storage, grid reinforcement, investment deferral estimation
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
This component does not use any input from a component to be developed (or
WITH OTHER COM-
tested) in the project.
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PONENTS (INPUTS)
DEPENDENCIES
The outputs are not used by any component to be developed (or tested) in the
WITH OTHER COM-
project.
PONENTS
(OUT-
PUTS)
OTHER COMMENTS
2.4.3
This tool performs offline studies.
Service Provider Analytics
2.4.3.1 Demand Forecast
COMPONENT INFORMATION
TITLE
Electric demand forecast
USE CASE
Flexibility and DSM in wholesale market, Optimizing the operation of storage
devices in the LV network, Microgrid Emergency Balance Tool, Distributed
storage aggregator
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
The tool provides forecasts for the electric and heating demand for individual
consumers. It uses as inputs:
1. Weather forecasts
2. Historical measurements
And produces probabilistic forecasts of the two parameters. An example of this
chain is represented in the figure below.
The tool makes use of machine learning and is able to capture the behaviour of
the system (in this case, the electric and heating demand of the building) considering only the measurements. The availability of further parameters can help
to improve the precision of the forecast and speed up the learning process.
The time horizon can range between 15 minutes to 48 hours and the update
rate is of 1 hour. They are both customisable.
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Electric demand forecast and measured demand
100%
90%
PV Production [% of Pn]
80%
70%
+- 40%
60%
+- 30%
50%
+- 20%
40%
+- 10%
Measured
30%
20%
10%
0%
00
Weather forecasts
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Forecast
tool
Heating demand forecast and measured heating
100%
90%
PV Production [% of Pn]
80%
70%
+- 40%
60%
+- 30%
50%
+- 20%
40%
+- 10%
Measured
30%
20%
10%
0%
Historic measures
00
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Probabilistic forecasts
INPUTS
Necessary
Static inputs: geo-localisation of the consumer, rated power
Dynamic inputs: weather forecast, measured historical electric demand
Wished
Static inputs: Energy survey (building physical model with dimensions, isolation
class and thermal inertia class (EN ISO 13786), building destination (residential, commerce, office...), list of appliances, socio demographic information of
the users (number, wealth...)).
Dynamic inputs: internal temperature, measured heating if not electric (egg:
gas)
OUTPUTS
Forecasts are presented as time series of variable length (in general from 24 to
72 hours) and variable time resolution (in general 30 minutes) of the following
parameters:
Point forecast: forecasts for the most probable value of the demand, forecasts for the highest possible value of the demand
Probabilistic forecasts: time series representing the quantiles of the production (with a resolution in general of 5% to 10%)
The forecasts are calculated for the electric and heating demand
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
EDP Smart meter
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Storage aggregation optimisation
DMS DATABASE
2.4.3.2 PV Production Forecast
COMPONENT INFORMATION
TITLE
D1.2_SENSIBLE_Deliverable_final
PV Production forecast
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USE CASE
Flexibility and DSM in wholesale market, Optimizing the operation of storage
devices in the LV network, Microgrid Emergency Balance Tool, Distributed
storage aggregator
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The tool provides forecasts for pv production within a defined area. It uses as
inputs:
1. Weather forecasts
2. Historical measurements
And produces probabilistic forecasts of the parameter. An example of this chain
is represented in the figure below.
The tool makes use of machine learning and is able to capture the behaviour of
the system (in this case, the PV panel) considering only the measurements.
The availability of further parameters can help to improve the precision of the
forecast and speed up the learning process.
The time horizon can range between 15 minutes to 48 hours and the update
rate is of 1 hour. They are both customisable.
PV Forecast and measured production
100%
90%
PV Production [% of Pn]
80%
Weather forecasts
70%
60%
+- 40%
50%
+- 30%
40%
+- 20%
30%
+- 10%
Measured
20%
Forecast
tool
10%
0%
00
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Probabilistic forecasts
Historic measures
INPUTS
Necessary
Static inputs: geolocalisation of the producer, rated power of the plant, Dynamic
inputs: historical and real time production, meteorological forecasts
Wished
Static input: survey of the plant: orientation and inclination, shadow, albedo of
the neighbouring area
Dynamic inputs: measured solar radiation
OUTPUTS
Forecasts are presented as time series of variable length (in general from 24 to
72 hours) and variable time resolution (in general 30 minutes) of the following
parameters:
Point forecast: forecasts for the most probable value of the production, forecast for the higher possible value of the production
Probabilistic forecasts: time series representing the quantiles of the production
(with a resolution in general of 5% to 10%)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COM-
D1.2_SENSIBLE_Deliverable_final
DMS DATABASE
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PONENTS (INPUTS)
Residential photovoltaic system
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
DMS DATABASE
Storage aggregation optimisation tool
2.4.3.3 Storage aggregator
COMPONENT INFORMATION
TITLE
Storage aggregator
USE CASE
Distributed storage aggregator
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
This tool calculates the optimal plan for distributed energy storages taking into
account global and local constraints and objectives from multiple actors, such
as storage owners and grid operators. The tool verifies then that the plans at
the aggregate and individual level are followed, and calculates the necessary
actions for the BMS (battery management systems) in order to correct eventual
derives. The tool can express the potential of the storage controlled under the
form of bids for a market or as forecast of the flexibility.
INPUTS
Necessary
Static inputs: storage location (eg: connection point or grid zone), storage size
in energy (kWh) and power (kW).
Dynamic inputs: variable constraints, requests (of charge, discharge, target
state of charge), measured state of charge (kWh), measured charge discharge
(kW)
Wished
Static inputs: storage characteristics (size, charge and discharge efficiency,
technology, initial capital cost, expected lifetime)
Dynamic inputs: electricity prices
OUTPUTS
The output of the tool is a group of time series representing the optimal plans to
be followed by the storages. The horizon of the plan is usually between 24 and
72 hours and the time resolution is usually of 30 minutes.
If a market is available, the aggregator produces bids for this market in the
form of couple of power and price bids for individual time slots.
The aggregator produces also forecasts of the available flexibility corresponding to a determined cost
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
MV Storage Optimization tool
LV Storage Optimization tool
Energy Market Service Platform
Energy Markets
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
DMS DATABASE
Flywheel grid storage
Low voltage storage grid support
Low voltage storage residential support
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2.5
High-level Applications
2.5.1
DSO Applications
2.5.1.1 DMS Data Base
COMPONENT INFORMATION
TITLE
DMS Data Base
USE CASE
The DMS Data Base is a Grid Distribution Management System, owned and
managed by the Distribution System Operator (DSO), EDP Distribuição.
Within its main functions are:
AMI (Advanced Metering Infrastructure) application which works as a middleware between the Smart Grid and all the corporative systems;
Grid Control and Operation abilities, managing every asset of the DSO,
from both the LV and the MV grids, through SCADA systems;
Collection and provision of data from the grid upwards to the RTP.
No system can communicate directly with any component of the smart grid,
except those designated to do so. All the information needed from a specific
meter and all the commercial orders (ex: change of tariff plans) have to be requested to the Smart Grid DMS Data Base.
In order to facilitate the communication of Smart Grid DMS Data Base with all
corporate systems there is also a web services library which can be used by
authorized systems.
Smart Grid DMS Data Base bridges the gap between the LV and MV grid, operated and managed by the DSO EDP Distribuição, and the SENSIBLE Real
Time Integration Platform. All aspects regarding Grid Storage Systems, MV
Storage and Smart Meter Data go through the DMS Data Base.
CONTACT PERSON
migueljorge.marques@edp.pt
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The DMS Data Base includes a number of functionalities affected directly to the
DSO that can be divided as follows:
AMI system;
MV/HV SCADA system;
LV SCADA system;
Data Collection and Provision systems.
The Advanced Metering Infrastructure system collects data, events and alarms
from the smart meters and also executes orders (e.g. tariff plan change and
meter shutdown). This is done through a web services library implemented in
the DTC (Distribution Controller Transformer). The two systems requesting
information or sending orders to the AMI system are either commercial systems
or asset management systems, which means that these requests are either
commercial (e.g. load profile) or technical (e.g. firmware update).
The MV/HV SCADA System is a system designed to control the HV and MV
grid. It is an open, modular and distributed system, consisting of servers and
workstations, in which the dispatch operators have a GUI layout that integrates
navigation mechanisms between applications and diagrams. All MV/HV assets
(including the MV Storage) communicate with SCADA system using protocols
like IEC60870-5-104. Some of the additional features of the SCADA system
include real-time power flows, automatic grid diagrams and MV fault locations.
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The SCADA system is interconnected with multiple systems from different internal and external entities, demanding a stringent cyber security policy allowing operations and system maintenance at any time and from anywhere in the
world.
In the scope of the SENSIBLE project the SCADA system will be used to operate any MV component, such as the MV Storage system or the Secondary
Substation automation.
The LV SCADA System controls, operates and supervises the LV Grid, collecting all the available low voltage data.
The Data Collection and Provision Systems are designed to provide the upstream RTP System with data from the secondary substations, the grid storage,
the clients and the MV Storage. This data is made available in an FTP Server.
Conversely, other services can be requested to the DMS Data Base from upstream systems, to enable the following information to be sent to the DTC and
downwards to the clients:
Client controls: to be applied to some clients;
Grid Storage controls: to be send to grid storage systems
Moreover, some other services can be requested to the DMS Data Base, in
order to collect information from downstream systems:
Grid Storage information
Client information
The DMS Data Base is able to communicate to upstream systems using FTP
server (files repository) and IEC 60870-5-104. It uses Ethernet/GPRS (through
proprietary APN) interface to communicate to downstream systems.
INPUTS
Storage data (SoC, On/off control, availability, voltage and set pointsX); Secondary Substation data (Voltage, power, Islanding circuit breaker data, Interlock to resync with the MV GridX); Client data (Voltage, powerX)
OUTPUTS
Storage data (SoC, On/off control, availability, voltage and set pointsX); Secondary Substation data (Voltage, power, Islanding circuit breaker data, Interlock to resync with the MV GridX); Client data (Voltage, powerX)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
DTC [EDP]; EB [EDP]; Real Time Platform [INDRA]; MV Storage System [Siemens]; MG Emergency Balance [INESC]; LV network islanded operation [INESC]; HEMS [EDP]; DTC [EDP]; GTStorage [EDP]
DEPENDENCIES
DTC [EDP]; EB [EDP]; Real Time Platform [INDRA]; MV Storage System [Sie-
D1.2_SENSIBLE_Deliverable_final
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WITH OTHER COMPONENTS (OUTPUTS)
mens]; MG Emergency Balance [INESC]; LV network islanded operation [INESC]; HEMS [EDP]; DTC [EDP]; GTStorage [EDP]
2.5.1.2 DMS
COMPONENT INFORMATION
TITLE
Advanced Distribution Management System
USE CASE
Real time analysis of the network current and near-future forecasted status
identifying local storage regulation services and operational set-point in order
to ensure the network quality of service, reliability and optimal performance.
CONTACT PERSON
Juan Prieto Vivanco, Indra
jprietov@indra.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
InGRID DMS allows the coordinated operation of distribution grids through a
modular solution based on five basic principles:
Geo-referenced Information: All modules are supported on GIS advanced
features, allowing a clear view of the network status, and well as all the relationships among the grid elements.
Modularity and Interoperability: Standards-based interoperability, enabling
integration with call centres, business systems, management systems,
measurement, and other agents systems, providing operators and network
managers a complete and holistic network view.
Real Time: Integrated with SCADA systems and real-time platforms enabling the real-time monitoring and decision making.
Intelligence: InGrid incorporates advanced analytical and simulation capabilities that allow fast network diagnostics and effective identification and
implementation of optimal strategies for resolution.
Operation Smart Grid: Ready for the coordinated management of demand
(demand response), distributed generation, Storage and active network’s
elements.
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The DMS advanced analytic functionalities are supported by the DSO Analytic
modules described as a separate template and shown in the following component scheme.
INDRA is piloting in its inGRID DMS solution, advanced Distributed generation
and Demand response management modules, allowing the optimal integration
in every day operation of :
Distributed generation units.
Distributed Storage
Customer Demand response capabilities.
Control devices
o FACTS (Flexible AC transmission system) equipments
o STATCOM (STATic COMpensator ).
This new modules allow:
Real-time follow-up and future evolution of distributed generation, storage
and demand response.
Analysis of network impact current and next hours.
Optimal dispatch of signals to generation units, storage and demand response and control devices in order to cope with current or next future network contingencies or performance losses.
INPUTS
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Grid measurements flow from the SCADA.
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Switching orders from the operator coming through the SCADA/ DMS.
Fault notification from call centre/network sensors.
Weather information.
Consistent network status from the analytic component
Updated Network topology.
Network electric characteristics.
Storage controllers RT available flexibility and operation conditions.
Forecasted operation limitations and performance deviations.
Network current and next future status analysis results.
Optimal storage set-points to maintain supply quality and network efficiency.
Market services availability information.
OUTPUTS
Storage market flexibility services requests.
DSO Storage set-points according to agreed market services...
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
iSPEED Real time Integration platform: Real time information from network,
storage controllers & flexibility market.
inGRID DSO Analytic component: network contingency and performance analysis results, Calculated optimal storage set-points, and network manoeuvres.
Flexibility Market: availability of flexibility services, Final bids matching.
OTS Simulator
DMS DATABASE/MV Storage System
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Flexibility Market: Bids for flexibility services, Services settlement information.
inGRID DSO Analytic component.
Storage controllers: operation set-points.
Distributed Generation: Operation Set-points.
Demand: Demand Response signals.
OTS Simulator
DMS DATABASE/MV Storage System
2.5.1.3 Real Time Network Simulator (OTS)
COMPONENT INFORMATION
TITLE
Operator Training Simulator module
USE CASE
Real time dynamic simulation of the Distribution network behaviour (in steady
state), including physical response of every component, measurement instruments, protection devices and simulation of a wide range of malfunction of the
previous ones.
CONTACT PERSON
Juan Prieto Vivanco, Indra
jprietov@indra.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
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Independent Network Simulator server connected to RTP (iSPEED), allowing
the simulation of the network behaviour in real time, and injecting on RTP only
the measured magnitudes that would be available if the RTP would be connected to the real network instrumentation.
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Operator
authorized
Commands
OTS (Instructor
station client)
Distribution
Management System
(DMS)
Commands and
monitoring
Apache
Thrift
Static Data (JSON)
Operator Training Simulator (OTS, server)
Simulation of Physical
behaviour of
components and
devices
DDS
DDS - RPC
Simulation oflocal
control and
automation
systems
Measurements,
Analysis results,
Computed
commands
Simulation of
malfunctions
DDS
Measurements
iSPEED
Head end
SCADA
Head end
Gen. & FACTS
Head end
MV/LV subs
Simulated domain
Real Domain
Field (IEC-104, 101, 102, ftp, etc)
OTS is a flexible real time grid simulation tool that provides a very realistic environment for testing the response and behaviour of other DSO tools. The main
features that OTS includes are:
Simulation of loads and generators based on complex historical load
curves.
Ability for using real-time measurements of loads and generators power
instead of direct simulation.
Simulation of generators response to PQ set points.
Simulation of the response of a wide variety of network devices (transformers taps, voltage regulators, condensers, FACTS, storage devices, etc.)
Complete load flow analysis of the network with previous boundary conditions.
Simulation of protection devices triggering (including short circuit triggering).
Simulation of measurements (digital and analogical), including noise and
error.
Simulation of a wide variety of malfunctions (e.g. bad measurements, spurious triggering, etc.)
INPUTS
Static data of the network, the different devices (technical specification) and the
instrumentation.
Historic power data in loads and generators / load characterization.
Initial network topology for simulation.
Available real time power measurements in loads and generators (optional).
OUTPUTS
Measured values (P, Q, U, I, where applies).
Communicated state of every device (where applies)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
inGRID DMS. DSO Distribution Management System (only for static information
and OTS process control commands).
DEPENDENCIES
iSPEED Real Time integration Platform.
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WITH OTHER COMPONENTS (OUTPUTS)
2.5.2
inGRID DMS. DSO Distribution Management System.
DSO_Analytics.
Market Applications
2.5.2.1 Energy Market Service Platform
COMPONENT INFORMATION
TITLE
Energy Market Service Platform
USE CASE
The Energy Market Service Platform combines grid enabled storage and resource control with market signals like price and individual contract constraints.
The energy market service platform thus creates new business opportunities
for end customers, energy suppliers and DSOs. It permits all kinds of flexible
resources present in a heterogeneous market with multiple network operators
and suppliers to come together on a level playing field based on market rules.
This lowers the barrier of entry for new suppliers and service providers to enter
the market and amplifies the aggregation force provided by the different technologies that provide initial technical aggregation of metering points. Services
like active balance management by using storage can be used to lessen price
risks for customers, thus lowering the total price of energy. Additionally, portfolios of storage and distributed energy connected through different technologies
will be shown to stabilize the energy system by connecting them to the regulating power markets.
CONTACT PERSON
Tuukka Rautiainen, Empower IM Oy, tuukka.rautiainen@empower.fi
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The Energy Market Service Platform consists of different modules covering the
topics of Customer Information Management (CIS), Energy Data Management
(EDM) and Energy Services Management (EMS). The module enable efficient
use of information gathered from the storage concepts combine it with energy
market information/processes and as a result clarifies the management of distributed energy resources.
INPUTS
The Energy Market Service Platform utilizes measurement, control, forecast
and contract information provided by the storage enabled buildings and communities. The platform will also have inputs from the energy markets utilizing
price and other market data.
Specific data structures, contents and interfaces will be specified during the
project based on the services and concepts selected for the demonstrations.
OUTPUTS
As an output the Energy Market Service Platform will provide the buildings and
communities a connection to energy market processes. This can include for
example the transfer of market price data, forecasts, individual contract data
and billing data. The data can be utilized to determine how the storage solutions should be managed to achieve a balance between the requirements of
DSO’s, suppliers and the storage enabled communities and buildings.
Specific requirements for the outputs will be determined during the project.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
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Real Time Integration Platform [INDRA], Energy Markets (Empower IM)
Other inputs will be connected through INDRA’s platform (Storage Controllers
[EDP, INESC], DMS DATABASE [EDP], DMS [INDRA, EDP, INESC], Forecasting tools [Armines], eBroker [GPTech])
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DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Real Time Integration Platform [INDRA], Energy Markets (Empower IM)
Other outputs will be connected through INDRA’s platform (Storage Controllers
[EDP, INESC], DMS DATABASE [EDP], DMS [INDRA, EDP, INESC], Forecasting tools [Armines], eBroker [GPTech])
OTHER COMMENTS
Final integration between different systems should be determined based on the
concepts to be demonstrated.
2.5.2.2 Energy Markets
COMPONENT INFORMATION
TITLE
Energy Markets
USE CASE
The developed use cases utilize energy markets in various ways. The markets
can be divided into five levels covering derivative, day-ahead, intraday, balancing power and reserve markets. Also energy retail markets are closely attached
to the execution of the use cases. The stakeholders utilizing energy markets
are most often energy suppliers/retailers. The suppliers can utilize flexible resources like storage to optimize the energy production and procurement on
different market levels and therefore maximize earnings and minimize expenditures. Also grid operators, energy communities and individual prosumers may
utilize energy markets either directly or through aggregators or energy service
providers.
CONTACT PERSON
Tuukka Rautiainen, Empower IM Oy, tuukka.rautiainen@empower.fi
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The energy markets consist of retail and wholesale markets. In this project
mainly the wholesale markets are being utilized at different time levels. Different countries and areas have their own market structures and power exchanges. The majority of this project’s countries are covered by EPEX, APX, MIBEL
and Nord Pool.
INPUTS
The energy markets get inputs from the Energy Market Service Platform implemented by Empower IM. Through the EMSP bids can be executed/simulated to the energy markets to provide balancing power, manage the
energy balance of a supplier, get maximal input for distributed generation etc.
OUTPUTS
The energy markets provide outputs in the form of price signals. Prices determined by open competition and solely by demand and supply, ensure that resources are being utilized in the most effective way.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Energy Market Service Platform [Empower IM]
Other inputs will be connected through Empower’s platform (HEMS, DMS,
DMS DATABASE, distributed energy resources etc.)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Energy Market Service Platform [Empower IM]
Other outputs will be connected through Empower’s platform (HEMS, DMS,
DMS DATABASE, distributed energy resources etc.)
OTHER COMMENTS
Final integration between different components will be determined based on the
concepts to be demonstrated.
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3
Nottingham Architecture
The Nottingham demonstrator is focused on storage-enabled energy management and energy
market participation of buildings (homes) and communities. At residential level, a number of customers will have controllable loads or batteries. In order to take full advantage of the potentiality
of the flexibility offered, they subscribe to the services of an aggregator. The aggregator pools
together the flexibilities of all customers and takes part in different markets in order to show the
potential to maximise the revenue from the different flexibilities. On the other hand, an independent energy community will be created with the confines of the project and regulatory frameworks
in force and to enable the gathering of data and practical demonstration of the benefits that can
be delivered to the community. This demonstrator will make use of a customer’s available flexibility and community based storage and power flow control capabilities in order to make the
community a much more attractive energy user and by optimizing energy costs to clients where
the community cluster can be used in a more controlled way to procure the electricity at the best
possible price. In addition, several situations will be tested where the system has to deal with the
limitation of PV penetration or with stability issues in the internal grid due to the over production of
PV or with the lack of PV generation.
The figure below shows Nottingham’s SENSIBLE components that will result in a flexible platform
that facilitates the control of the residential community buildings, including a wide range of devices, protocols and technologies.
Fig. 2: Nottingham’s architecture schematic
Intelligent Electronic Devices
In this figure, the different IEDs for last mile communications can be seen at the bottom of the
graph. They have been grouped taking into account the building where they will be installed: NEP
building and a school which need mostly 3 phase devices and residential buildings which will
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have one phase devices. Regarding the storage, 3phase inverters will be located to control the
flywheel and electrochemical storage on the NEP building and the school. At residential level, a
storage inverter will be used to connect the single phase supply in a residential home within the
demonstrator site to the electro-chemical battery storage units. Moreover, an ImmerSUN controller will enable a variable amount of power to be delivered to a passive load, typically a thermal
heating element submersed in a domestic hot water storage tank. With respect to PV generation,
SMA inverters (or equivalent) will be installed to integrate them into the grid.
When it comes to smart meters, two different types will be installed in the demonstrator. One
connected to one-phased circuits and the other one to monitor 3phased circuits. This smart meter
will be installed to monitor the PV generation and loads of the building as well as the storage devices in the case of residential buildings or LV grid power in the case of the NEP and school
buildings.
Low Level Control Systems
The eBroker component is an embedded control algorithm which is seamlessly connected to every controllable energy generator and storage system which compounds a micro or smart grid. It is
a distributed control-based system which aims to control the electrical grid stability and dynamically improves the quality. The eBroker will be connected to the Integration Gateway and will be
able to receive all data flowing through this gateway.
The integration gateway will be designed to integrate data coming from the devices installed in
the building (smart-meters, converters or auxiliary data such as temperature or CO2) into the
Real Time Platform providing the required communication, computing and storage capabilities for
data integration at the same time that will make the measuring process design independent from
the platform assumed as being the transparent component or bridge between them.
As we go up in the ecosystem, we find the Real Time Integration platform component for providing real-time connectivity among the upper software components through publishes-subscribe or
request-response mechanisms.
Community tools
At the right top part of the architecture diagram the Real Time Platform integrates the community
applications. The Meadows Data Manager will manage all the data from the different pieces of
equipment and control commands of the demonstrator. It will also be used to implement analytical algorithms or micro-grid management functions as well as to act as the high level Energy
management system required to coordinate the eBroker implementations within the distributed
equipment. The Visualization tool will be available and oriented to final users. It shall provide realtime access to energy consumption data from users´ dwellings and houses. In addition, the
Meadows Weather Station will be also integrated into the Real Time Platform to provide weather
sensor data such as dry bulb temperature or wind speed to the rest of the interested components.
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Independent actors
The Analytics module connected to the Real Time Platform integrates the participation of independent actors such as DER aggregators or services providers. The storage aggregator will calculate the optimal plan for distributed energy storages taking into account global and local constraints and objectives from multiple actors, such as storage owners and grid operators. Moreover, PV production and demand forecast will be provided by this module.
New market tools
Last, the Energy Market Service Platform combines the enabled storage and resource control
with market signals like price and individual contract constraints, creating new business opportunities for end customers, energy suppliers and DSOs. The Energy Market Service Platform will be
connected to the Energy Markets component that will simulate retail and wholesale markets in
the demonstrator.
In the following sections, the descriptions of the Nottingham architecture components are explained including the inputs and outputs of each component as well as the dependencies with the
rest of the components in the architecture.
3.1
Residential/Community devices
3.1.1
Thermal storage
PRODUCT INFORMATION
TITLE
Functionality definition for the electrical-to-thermal store hardware data bridge
USE CASE
All use cases which are to be tested at the Meadows will use this, specifically,
Enabling and Independent Energy Community
CONTACT PERSON
Lee Empringham - lee.empringham@nottingham.ac.uk
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The ImmerSUN controller is primarily designed to enable a variable amount of
power to be delivered (single phase) to a passive load, typically a thermal heating element submersed in a domestic hot water storage tank.
The primary function of the data bridge is to create a wireless data link between
the thermal controller and a router.
Although not implemented at present, ImmerSUN are willing to add functionality
such that the SENSIBLE consortium would be able to send remote demands to
the unit irrespective of control method. As such, this would make the unit a
programmable power controller for DMS functionality.
INPUTS
Immersun controller data, control data for the storage controller
OUTPUTS
Usage data, monitoring feedback data
PRODUCT DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
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DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
3.1.2
communications hardware/ integration gateway [UoN, Adevice/USE]
Real time integration platform [Indra]
Electrical to thermal controller ImmserSUN
COMPONENT INFORMATION
TITLE
Functionality definition for the electrical-to-thermal store hardware
USE CASE
All use cases which are to be tested at the Meadows will use this, specifically,
Enabling and Independent Energy Community
CONTACT PERSON
Lee Empringham - lee.empringham@nottingham.ac.uk
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The ImmerSUN controller is primarily designed to enable a variable amount of
power to be delivered (single phase) to a passive load, typically a thermal heating element submersed in a domestic hot water storage tank.
The primary function would be to increase self-usage of generated PV energy
by diverting / matching the exported energy into the thermal store.
The unit can also be programmed to take advantage of dual tariff billing systems.
Although not implemented at present, ImmerSUN are willing to add functionality
such that the SENSIBLE consortium would be able to send remote demands to
the unit irrespective of control method. As such, this would make the unit a
programmable power controller for DMS functionality.
Each unity is rated at 3kW.
INPUTS
Network control data(Via Integration Gateway), household export data
Power demands from analytical entities.
OUTPUTS
Electrical energy to passive load
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Software controllers [UoN], communications hardware/ integration gateway
[UoN, Adevice/USE]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Software controllers [UoN], communications hardware/ integration gateway
[UoN, Adevice/USE] Real Time Integration Platform [Indra]
OTHER COMMENTS
The unit can be installed together with 3 other units and use a proprietary wireless communications interface to link them together with the grid export current
sensor. A wireless Ethernet based communications bridge can be used to
communicate with the system.
3.1.3
Storage inverter
COMPONENT INFORMATION
TITLE
Functionality definition for the Meadows residential storage inverter
USE CASE
All use cases which are to be tested at the Meadows will at some point use the
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hardware
CONTACT PERSON
Lee Empringham - lee.empringham@nottingham.ac.uk
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The residential storage inverter will be used to connect the single phase supply
in a residential home within the Meadows demonstrator site to the electrochemical battery storage units. These are distinctly different from the three
phase units designed by GP Tech for three phase ‘community’ level storage.
The most likely candidate would be one of the SMA Sunny Island Inverters as
they seem to offer the flexibility needed for the purposes of the SENSIBLE project.
INPUTS
Local measurement data, battery state of charge data, household usage, configuration data, mode change data - all through distributed integration gateways
Power demands from analytical entities.
OUTPUTS
real time data for partners, equipment configuration data / control
PRODUCT DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Real time integration platform [Indra], integration gateway (USE), eBroker
(GPTech)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Real time integration platform [Indra], integration gateway (USE), eBroker
(GPTech)
OTHER COMMENTS
See following pages
Meadows Demonstrator Residential Storage Inverter
Abstract: This document describes a potential candidate for the residential storage inverter to be
used in the houses within the Meadows demonstrator site.
Introduction: The SMA Sunny Island is the single phase AC/DC battery inverter. The inverter
can work with and without PV integration and it is envisaged that the single phase on-grid system
will be used within the Meadows as shown in Figure1. Since the inverter will be installed within
the homes of the participants of the SENSIBLE project, it is imperative that a CE marked; ‘off the
shelf’ product is used. The main functionality of the inverter would be to ‘prevent’ any regeneration of PV energy within a PV installed house, and to store cheap rate electricity at night
time and invert this back to the household during the day time, matching the household usage
where possible where dual tariff agreements exist. The inverter should also be flexible enough to
be controlled by the Meadows Manager hardware via the USE integration gateways in order to be
able to validate various use cases throughout the duration of the project. Inverter data such as
state of battery charge, household usage etc. should also be available via the integration gateway
to higher level monitoring. Figure 3 shows the connection area of the inverter.
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Figure 3: Sunny Island System Configuration.
Battery Management: The sunny island has its own battery management system. Battery management of the battery inverter, Sunny Island, is based on precise determination of the state of
charge. By combining the three most common methods of state of charge determination, the
Sunny Island achieves a measurement accuracy of greater than 95%. This reliably prevents
overcharging and deep discharging of the batteries.
The sunny island measures the battery temperature, current and voltage for battery protection.
Battery management issues a warning message if one of the following events occurs:
The battery temperature is within 5°C of the maximum permissible battery temperature.
The battery temperature is less than − 10°C.
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If the maximum permissible battery temperature is exceeded, Sunny Island switches itself off. As
soon as the battery has cooled down to a predefined temperature, Sunny Island starts again.
The battery's state of charge, SOC is estimated by the measured battery voltage. If the battery's
state of charge falls below defined limiting values, battery protection mode is activated. In battery
protection mode, Sunny Island switches to standby or switches itself off. Battery protection mode
has three levels. For each level, you can set one state of charge limiting value. Levels 1 and 2 of
battery protection mode have specific start and end times and are therefore dependent on the
time of day.
Level 1 if the state of charge drops below the limiting value for level 1 at any time between the
start time and end time, the Sunny Island switches to standby. This way, you can specify times
for which you prefer the stand-alone grid to be switched off if there is an energy deficit.
Level 2 if the state of charge drops below the limiting value for level 2, the Sunny Island switches
to standby. During the day, when PV inverters can supply energy, the Sunny Island attempts to
charge the battery. Using the start time and end time, you define the time period during which the
Sunny Island starts every two hours in order to charge the battery. If no energy is available to
charge the battery, the Sunny Island remains on standby.
Level 3 if the state of charge drops below the limiting value for level 3, the Sunny Island switches
itself off. This protects the battery against deep discharge and severe damage. To charge the
battery again, the Sunny Island must be switched on and started manually. At all three levels, the
Sunny Island only switches to standby or switches itself off if no charging current flows within six
minutes.
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Figure 4: Connection area of the Sunny Island inverter
Communication:
The Sunny Island is equipped with two interface slots seen in Figure 4 (E) for the connection of
SMA communication interfaces.
1.
Interface slot SICOMSMA
The interface slot SICOMSMA is for connecting the Speedwire data module SWDMSI-xx or
the RS485 communication interface SI-COMSMA.BGx. The Speedwire data module
SWDMSI-xx allows the Sunny Island inverter to be integrated into a Speedwire network.
Speedwire is a cable-based type of communication based on the Ethernet standard and the
communication protocol SMA Data2+. This enables inverter-optimized 10/100 Mbit data
transmission between Speedwire devices, e.g. between Sunny Island and Sunny Home
Manager. The SI-COMSMA.BGx communication interface allows the Sunny Island inverter to
be integrated into an RS485 communication bus. You can inherently connect the Sunny Island to the following products using RS485:
SMA communication products (e.g. Sunny WebBox)
PV inverters
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Wind power inverters
Extension cluster masters
If the Sunny Island inverters are ordered with the RS485 communication interface SICOMSMA.BGx or with the Speedwire data module SWDMSI-xx, the Sunny Island inverters
are delivered with premounted communication interfaces.
It is envisaged that the RS485 interface could be used together with the Integration Gateway
to control and configure the inverter.
2.
Interface slot SISYSCAN
On Sunny Island device types SI6.0H-11 or SI8.0H-11, the interface slot SISYSCAN is for
connecting the multicluster data module SI-SYSCAN.BGx. In a multicluster system, the masters of the clusters must communicate with each other via a separate CAN bus. An SISYSCAN.BGx communication interface must be installed in each master for multicluster
communication. If the Sunny Island inverters are ordered with the communication interface
SI-SYSCAN.BGx, the masters are delivered with premounted communication interfaces.
It is envisaged that the CAN interface could also be used together with the Integration Gateway to control and configure the inverter. The best option will need to be discussed with
USE.
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Figure 5: Circuitry of the single phase SMA Flexible Storage System
System Requirements:
If no automatic load control and no limitation of the active power feed-in are required, you can
equip a PV system solely with a Sunny Island and do without the SMA Flexible Storage System
(Sunny home manger). With this option, however, you implement only the intermediate storage of
PV energy. The Sunny Island receives no data regarding PV generation. This means that the
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Sunny Island cannot display some of its parameters, e.g. the increased self-consumption values.
For a purely Sunny Island storage system, the following SMA products are required:
Sunny Island 6.0H / 8.0H
SMA Speedwire data module for Sunny Island
SMA Energy Meter, D0 interface type (D0 interface is optical communication adapter)
Sunny Remote Control
BatFuse B.01 / B.03 (DC fuse)
In a Sunny Island storage system, the SMA Energy Meter must be connected directly to the Sunny Island via a network cable. Energy meters with S0 interfaces are not compatible with the Sunny Island storage system.
This is the configuration which will be used for the SENSIBLE project with all non-standard control being configured via the Integration Gateway.
Summary of Sunny Island System Components:
1.
2.
Power Cable
1.1
DC power cable: 2 terminal lugs M8, 20mm to 25mm wide, cable diameter 14mm to
25mm (conductor cross-section: 50mm2 to 95mm2).
1.2
AC power cable: copper wire, cable diameter: 9mm to 18mm, conductor crosssection: maximum 16 mm2
1.3
Grounding conductor: cable diameter 1mm to 14mm
Battery fuse
Fuse links are matched to the Sunny Island. •
SMA Sunny Island
Fuse current
SI3.0M-11
80A
SI4.4M-11
100A
SI6.0H-11
160A
SI8.0H-11
200A
Table 1: List of Battery Fuses.
3.
Fuse switch disconnector.
4.
Circuit breaker 32A, C rating, 1 pole.
5.
Residual current device, RCD, 40A/0.03A, 1-pole + N, type A.
6.
Sunny Island 6.0H (4.6kW) / 8.0H (6kW).
7.
SMA Speedwire data module for Sunny Island.
8.
SMA Energy Meter with D0 interfaces type.
9.
Battery
Manufacture
Type
LG Chem
48V, 5kWh battery pack
Sony
Battery system IJ1003E
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Akasol
neeoQube 48V
IBC
SolStore X.X Li
Saft
Intensium Home product family
Dispatch Energy Innovations
BD5000
Samsung
Table 2: List of Approved Lithium-ion Batteries.
Reference
[1]
Installation Manual Sunny Island 3.0M/4.4M/6.0H/8.0H
[2]
Planning Guidelines, SMA SMART HOME, and The system solution for more independence.
[3]
Battery Management, Gentle charging control based on current state of the battery, Sunny Island
[4]
Technical optical communication adapter EMH metering.
3.1.4
PV panels
COMPONENT INFORMATION
TITLE
Functionality definition for the Meadows PV Panels
USE CASE
All use cases which are to be tested at the Meadows will use the PV Panels
CONTACT PERSON
Lee Empringham - lee.empringham@nottingham.ac.uk
COMPONENT DESCRIPTION
PRODUCT
DESCRIPTION
All use cases and test scenarios which are related to the Meadows demonstrator will use the Meadows PV panel hardware.
Residences typically have up to 2kWp of PV panels installed on their roofs.
These are typically integrate into the grid using SMA inverters
INPUTS
power export demands
OUTPUTS
Monitoring data
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
eBroker (GPtech), Integration Gateway (USE)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
eBroker (GPtech), Integration Gateway (USE)
3.1.5
Three port converter
COMPONENT INFORMATION
TITLE
Requirements definition of a three port power converter
USE CASE
The use case for this hardware would be to show extended battery life by using
super-capacitors to provide peak power also showing the possibility of the use
of second life batteries.
This power converter will be used to connect both battery banks and super-
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capacitor banks to a common 3 phase grid interface
CONTACT PERSON
Lee Empringham - lee.empringham@nottingham.ac.uk
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The power converter would ideally be a three port power converter with 1 X
three phase grid interface and 2 X bidirectional DC input ports.
Power flow should be bidirectional and controllable on each port. The main idea
being that average power can be transferred to and from the battery bank and
peak / pulse power can be delivered using the supercap bank. Power can also
be transferred between the supercap and battery banks independent from the
operation of the grid interface.
Indicative ratings for each port.
3 phase port:16A rated
415V L-L
pf = 1
Battery DC
3KW nominal
Vmax < 400V
Vmin > 200V,
Irated @ Vmin = 15A
Irated @ Vmax = 7.5A
Supercap DC
11.5kW Peak
Vmax < 400V,
Vmin > 100V,
Ipeak @Vmin =115A
Ipeak @Vmax=28.8A
One way this converter could be achieved would be to utilise an active front
end (AFE) to generate an intermediate DC link (aprox 750V) which would then
be connected to two DC/DC converters. Either DC/DC converter could drive
power into or absorb power from the DC-link whose voltage would then be regulated by the AFE automatically.
INPUTS
Independent current demands from local controller for the DC ports
OUTPUTS
Voltage and current measurements of key parts of the internal circuit going to a
local controller.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Software controllers [UoN], communications hardware [UoN, Adevice]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Software controllers [UoN], communications hardware [UoN, Adevice]
Real Time Integration Platform [Indra]
3.1.6
3 Phase Inverter
COMPONENT INFORMATION
TITLE
D1.2_SENSIBLE_Deliverable_final
3phase Battery power converter
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USE CASE
Enabling an independent energy community
Microgrid Energy Market
CONTACT PERSON
Salvador Rodríguez – srodriguez@greenpower.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The 3phase Inverter is a battery power conversion system from 30 kW and 100
kW. As shown, the power module is coupled to the DC and AC side through a
disconnection module. The disconnection module consists AC and DC switches into a compact single enclosure separated from the power module
The power converter will be implemented with Sotf-switching, direct paralleling
and transformerless technology
The 3Phase Inverter is able to provide a wide range of grid support services by
means on its Advanced Power Control System so called Software Controller.
Services and functionalities:
Remote control of active and reactive power
Controlled ramp rate
Three modes of operation for Low Voltage Right Through:
1. Maximum reactive power injection.
2. Constant power factor.
3. No current injection.
Automatic power regulation according to frequency variations.
Setting charging and discharging times to suit the needs of the grid and
improve the life cycle of the storage cells
Direct management of the battery via the communication bus
Configurable for multiple targets:
o Frequency regulation.
o Spinning reserve
o Peak shifting
o VAR support
o Voltage regulation
o eBroker Functionality
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Electrochemical Storage [UoN]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Integration Gateway [USE]
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3.1.7
Smart Meter SM
COMPONENT INFORMATION
TITLE
Smart Metering, SM
USE CASE
Smart meters devices for one-phased circuits are energy diagnostic systems
aimed to monitor energy consumption and quality of service (QoS) of energy
supply in 3 different phased using one single SmartMeter, with an easy and
non-intrusive installation that work with wired or wireless communications.
Thanks to the measures of energy consumption, obtained measurements can
be used to estimate both consumption and capacity of storage systems.
By storing all measurements into an historical consumption log, different consumption patterns could be identified and classified by type of client, storage
system used, etc.
SmartMeters only can be used in AC ports
Type and frequency of the measurements (active, reactive, QoS, etc.) To perform will be defined during the project.
CONTACT PERSON
Manuel Alberto Moreno García, manuel.moreno@adevice.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
SmartMeters used for storage monitoring measure indirectly through Split Core
Current Transformers, this facilitate installation of current transformers in the
target phases to be monitored.
Current transformers shall have a ratio of: 5 Amperes.
SmartMeters only can be used in AC ports
Initially following parameters can be measured:
Active Power
Reactive Power
Apparent Power
Power Factor
About QoS:
Voltage variations
Current variations
Voltage and current harmonics
Regulations, standards and CE Marking:
EN 61326-1: 2006;
ETSI EN 301 489-1 v.1.9.2; -17 v2.1.1
EN 61010-1: 2010;
EN 50385:2002.
Dimensions: (6-DIN modules)
INPUTS
D1.2_SENSIBLE_Deliverable_final
Smart meters are directly connected to phase to be measured.
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Energy and QoS measurements.
Measurements usually will be sent to managements systems by WebServices
through a TCP/IP connection.
It is possible to connect external relays to smartmeters in order to perform also
actuation tasks.
OUTPUTS
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Just in case of bidirectional communication:
Integration Gateway (USE), RT Integration Platform (INDRA).
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Integration Gateway (USE), RT Integration Platform (INDRA).
OTHER COMMENTS
Smart meters are directly connected to phases to be measured.
SmartMeters only can be used in AC ports
Input from other partners needed, related to:
What parameters have to be measured?
Measurements frequency (milliseconds) and sending frequency to management systems (seconds?)
3.1.8
Smart Meter SM3
COMPONENT INFORMATION
TITLE
Smart Metering 3phased
USE CASE
Smart meters devices 3 phased are energy diagnostic systems aimed to monitor energy consumption and quality of service (QoS) of energy supply in
3phased circuits, with an easy and non-intrusive installation that work with
wired or wireless communications.
Thanks to the measures of energy consumption, obtained measurements can
be used to estimate both consumption and capacity of storage systems.
By storing all measurements into an historical consumption log, different consumption patterns could be identified and classified by type of client, storage
system used, etc.
SmartMeters only can be used in AC ports
Type and frequency of the measurements (active, reactive, QoS, etc.) To perform will be defined during the project.
CONTACT PERSON
Manuel Alberto Moreno García, manuel.moreno@adevice.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
SmartMeters 3phased (SM3) used for storage monitoring measure indirectly
through Split Core Current Transformers, this facilitate installation of current
transformers in the target phases to be monitored.
Current transformers shall have got a ratio of: 5 Amperes.
SmartMeters only can be used in AC ports
Initially following parameters can be measured:
Active Power
Reactive Power
Apparent Power
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Power Factor
About QoS:
Voltage variations
Current variations
Voltage and current harmonics
Unbalanced voltage in 3-phased systems
Unbalanced current in 3-phased systems
Regulations, standards and CE Marking:
EN 61326-1: 2006;
ETSI EN 301 489-1 v.1.9.2; -17 v2.1.1
EN 61010-1: 2010;
EN 50385:2002.
Dimensions: (6-DIN modules)
INPUTS
Smart meters are directly connected to phases to be measured.
OUTPUTS
Energy and QoS measurements.
Measurements usually will be sent to managements systems by WebServices
through a TCP/IP connection.
It is possible to connect external relays to smartmeters in order to also perform
actuation tasks.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Just in case of bidirectional communication:
Integration Gateway (USE), RT Integration Platform (INDRA).
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Integration Gateway (USE), RT Integration Platform (INDRA).
OTHER COMMENTS
Smart meters are directly connected to phases to be measured.
SmartMeters only can be used in AC ports
Input from other partners needed, related to:
What parameters have to be measured?
Measurements frequency (milliseconds) and sending frequency to management systems (seconds?)
3.2
Low-level Control Systems
3.2.1
Meadows Auxiliary Data Collector
COMPONENT INFORMATION
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TITLE
Functionality definition for the Meadows Community Data Collection
USE CASE
All use cases which are to be tested at the Meadows will at some point use the
hardware and protocols outlined here.
CONTACT PERSON
Mark Gillott - mark.gillott@nottingham.ac.uk
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The meadows community comprises an aggregated collection of buildings that
are consumers of energy and in some cases energy produces through onsite
renewable energy generation. Each building will be instrumented to obtain a
local set of data which when combined with the entire community creates the
aggregated community energy data set. Within each building data will be collected in real time at 5 minute intervals and stored remotely - each data set will
have identical 5 minute interval time stamps to facilitate data processing. The
system comprises a hub and node network of EnOcean energy harvesting
wireless sensor technologies. The hub communicates with each sensor wirelessly using EnOcean radio protocols which have been optimised to transmit
information using extremely little power for short transmission period (<1ms) of
each EnOcean telegrams. The hub then communicates to external servers via
the home communications hub (either wired or 3G). The route for this data
could be either direct through the internet or via the RTP and integration gateway.
INPUTS
The following sets of data will be monitored:
In-situ monitoring: Each building should have the following minimum monitoring specification:
Thermal comfort (heating energy indicator): Temperature (dry bulb) and
relative humidity sensors will be located in the main occupied spaces.
Occupancy: PIR sensors will be located in the main occupied spaces to determine occupancy profiles.
Heat Meters: Heat meters to be installed to measure space heating and hot
water usage (and production for any solar thermal systems). These will output
1 pulse per Wh of thermal energy this data will then be transmitted at 5 minute
intervals utilising an EnOcean pulse sender giving a record of Wh per minutes.
Electricity generation and use: Energy/watt pulse meters will be installed to
record electricity import, export and generation. Additionally the system will
provide individual circuit monitoring and key individual loads such as lighting,
kitchen appliances, under-floor heating, ventilation fans etc. These will output 1
pulse per Wh of electrical energy this data will then be transmitted at 5 minute
intervals utilising an EnOcean pulse sender giving a record of Wh per minutes.
OUTPUTS
real time data for partners, equipment configuration data / control
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Software controllers [UoN], communications hardware/ integration gateway
[UoN, Adevice/USE]
Real time integration platform [Indra]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Real time integration platform [Indra] Software controllers [UoN], communications hardware/ integration gateway [UoN, Adevice/USE]
OTHER COMMENTS
Exact quantity and specification will only be known once recruitment of buildings into the community is complete.
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3.2.2
EBroker
COMPONENT INFORMATION
TITLE
eBroker- Smart grid manager and optimization
USE CASE
“eBroker2.0” is a control algorithm embedded which is seamlessly connected to
every controllable energy generator and storage system which compounds a
micro or smart grid. It is a distributed control-based system which aims to control
the electrical grid stability and dynamically improves the quality. This control
algorithm method implements an innovative adaptive controller, which integrates
generation, storage and measurement systems. The necessary hardware for its
implementation is also developed.
“EBroker 2.0” is intended to cover and solve several situations which smart grid
can be found, the main features are highlighted below::Power flows optimization.
CONTACT PERSON
Grid faults detection and solution
Islanding scenario management
Electrical grid quality control: voltage dips, harmonic loadsX
equivalent line impedance changes, phase unbalance
Weak grids. Frequency and voltage control
Scenario with consumption storage operation.
Scenario of net operating balance without storage.
Joaquín Álvarez Agudo, jalvarez@gte.esi.us.es ; USE
Salvador Rodriguez, srodriguez@greenpower.es ; GPTech
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
”eBroker 2.0” is a novel distributed controller, which is able to integrate generation, storage and measurement systems, by flexible, seamless and secure
communications.
This smart device (figure 6) will be connected to each generation, storage and
measurement system which is included within a micro/smart grid. These intelligent devices are able to communicate with each other in order to control the grid
autonomously.
Input
Profiles
Generator
Exchanges
Manager
System
Supervisor and
Alert Manager
Optimal
Calculation of
Priority
Parameters
To Anothers Data Model
Associated
Device
Data Model
To a central Data Model
Figure 6: Scheme of smart distributed control device and function modules
Main features of “eBroker 2.0” are described next:
Adaptable method to new technologies with the ability to prioritize power
exchanges
Optimization of production and consumption agents in a microgrid taking into
account the user specifications, priority, and the price of power.
Dynamic Response to market price and network parameters. Operability taking
into account both technical reference (grid codes: ramp rate, frequency response
...) economic reference parameters (energy prices, grid faults ...).
. It is a distributed control-based system which aims to control the electrical grid
stability and dynamically improves the quality of a microgrid or a smart grid, in-
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creasing renewable energy penetration without the necessity of a central controller system or a central energy manager
It is a simple, robust and reliable design with the capability of interoperates with
every systems regardless communication protocol or standard. Plug &Play and
easy self-maintenance solution
The method uses a Common Telecommunications Infrastructure (CTI) associated to each one of the electrical devices that could be presented in a micro or
smart grid. Therefore, the proposed scheme is not based in the hierarchical
agent-method or any other hierarchy topology.
Main Grid
High performance
communication lines
Plug-in Electric
Vehicle
Wind Power System
Active Filter
Photovoltaic System
FACTS Device e.g.
Statcom
Diesel Generator
Storage System
e.g. Battery
Supercapacitor
Load
Load
Load
Load
Electric lines
Figure 7: Scheme of smart grid controller
INPUTS
Active and Reactive power from non-controlled
generators and consumers
Energy rate
Forecasting (Optional)
Trading priorities
Battery Management system information
OUTPUTS
Power Factor, Active and Reactive power to power converter
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Storage Controllers [GPTech, UoN], Smart meters [Adevice],Auxiliaty Data Controller [UoN], Real Time Platform [Indra], Energy Market Service Platform [EMPOWER],
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Storage Controllers [GPTech, UoN], Real Time Platform [Indra]
3.2.3
Integration Gateway
COMPONENT INFORMATION
TITLE
D1.2_SENSIBLE_Deliverable_final
Integration gateway
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USE CASE
The integration gateway will be designed to integrate data coming from certain
devices (smart-meters, converters, etc.) into the INDRA’s real-time integration
platform providing the required communication, computing and storage capabilities for data integration at the same time that will make independent the measuring process design from the platform being assumed as a transparent component or bridge between them.
CONTACT PERSON
Clara Lujan – cilujan@us.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The integration gateway consists of the required software and hardware to
integrate data from certain devices into the INDRA’s real-time integration platform. A specific integration gateway will be defined for each device or set of
devices in the same emplacement.
The gateway will provide the required communication, computing and storage
capabilities to integrate data and will allow making independent the measuring
process design from the platform development.
Specifically it will provide the following capabilities:
External communication: providing the necessary communication modules
for devices to interact with the platform via Internet.
Platform connection: managing connections with the platform, process requests and responses and managing users and permissions.
Data sender: Building correct requests for each measurement and sending
it to the platform. The response from the platform will be processed and
measurements will be marked as sent, resent or stored for retrying later.
Temporal storage: enabling a mechanism that avoid losing data when the
input stream from devices is larger than the platform input capability, in
case of fail connection, wrong request, etc. The temporal storage system
will store data until they are uploaded to the platform.
Protocol handler: adapting the input stream from each device (protocol and
data model) to the format expected by the public interfaces provided by the
platform
Device communication: performing the communication with devices to get
the input data stream. Relevant information regarding the protocol, data
model, registers address, etc. will be used to gather data from devices and
to allow a proper interpretation of them.
Hardware connection: Physical interface to connect with the devices.
INPUTS
Data input stream from measuring devices
OUTPUTS
Real Time Integration Platform requests
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Storage Controllers [GPTech, UoN], Smart meters [Adevice], Auxiliaty Data
Controller [UoN], Real Time Platform [Indra]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Storage Controllers [GPTech, UoN], Smart meters [Adevice], Real Time Platform [Indra]
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3.3
Real Time Integration platform
COMPONENT INFORMATION
TITLE
Real Time Integration Platform
USE CASE
ISpeed is an integrated platform for real-time data acquisition and processing,
with the ability to handle large volumes of information at low latency. Its main
objective is to increase productivity and effectiveness in the exchange and
management of information generated by various monitoring and control applications, while reducing the chances of error in data manipulation. It is characterized by providing a fast and reliable exchange of information between the
actors connected to it, ensuring at all times the availability of information at low
and flexible coupling between them. In addition, this platform provides the necessary tools to perform real-time distributed data processing and also the
means to persist the information that will subsequently treated in batch analytic
processes.
CONTACT PERSON
Catherine Murphy-O’Connor cmurphy@indra.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
iSpeed is a platform developed under the XTPP concept (eXtreme Transaction
and Processing Platform) which is capable of providing the following services
anywhere in the data acquisition and processing network:
Unified Data Model: based on specific domain models (IEC 61970-61968,
IEC 61850 X) and provides storage and retrieval capabilities in different
time horizons and data access technologies.
Real-Time Messaging Service: capable of supporting real-time communications between any of the systems involved in the management, monitoring
and network operation.
Complex Event Processing Service: to handle events both locally of an asset (filtering and data analysis), and capable of processing large aggregates
and global events.
ISPEED is a SOA architecture (Service Oriented Architecture) based on the
publish/subscribe data distribution paradigm. It has been built over the DDS
(Data Distribution Service) middleware standard for data distribution, where
aspects such as transparency and failover, distribution and deployment of software without service interruptions, and data delivery are specified. Moreover,
the middleware that provides the messaging service in the real-time platform is
capable of being loaded on computers with low processing capacity, so that
data can be collected directly from low-level nodes.
One of the key pieces of a real-time platform is the communication middleware.
It is a logical area in which the information, in data structures form, is shared
between two types of actors: publishers and subscribers.
Publishers are the elements that have information of interest to other systems and provide this information on the bus.
Subscribers are those actors who are awaiting information that will later
process.
The Real Time integration platform DDS data structures for real time communication in the Nottingham demonstrator will be defined and developed according
to the data exchange magnitudes included in D3.1 [2], in order to fulfil the requirements of the use cases defined in D1.3 [1].
INPUTS
D1.2_SENSIBLE_Deliverable_final
As iSPEED is the middleware communication infrastructure, there will be not
explicit inputs. The different systems connected to the platform will exchange
the information using the SENSIBLE data structures that are going to be defined in the project taking into account the demonstrator use cases.
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Interfases with the platform will be carried out through DDS technology, using
the Real-Time Publish-Subscribe (RTPS) protocol designed to be used over
UDP, in case of Real Time data or traditional REST-Json software architecture
style, using HTTP protocol, in case of historical data.
OUTPUTS
As iSPEED is the middleware communication infrastructure, there will be not
explicit outputs. The different systems connected to the platform will exchange
the information using the SENSIBLE data structures that are going to be defined in the project taking into account the demonstrator use cases.
Interfases with the platform will be carried out through DDS technology, using
the Real-Time Publish-Subscribe (RTPS) protocol designed to be used over
UDP, in case of Real Time data or traditional REST-Json or SOAP software
architecture style, using HTTP protocol, in case of historical data.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Integration gateway [USE], Energy Market Service Platform [EMPOWER],
Forecasting and Storage Aggregator [Armines], MDM [UoN], Meadows Weather Station [UoN]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Integration gateway [USE], Energy Market Service Platform [EMPOWER],
Forecasting and Storage Aggregator [Armines], MDM [UoN], Visualization tool
[ADEVICE]
3.4
Service provider Analytics
3.4.1
Demand Forecast
COMPONENT INFORMATION
TITLE
Electric demand forecast
USE CASE
Optimized energy procurement, (Microgrid/Community, Microgrid PV Management, the component is not visible in the component lists of these two UCs, but
present in the description of the UC), Distributed storage aggregator
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
The tool provides forecasts for the electric and heating demand for individual
consumers. It uses as inputs:
1. Weather forecasts
2. Historical measurements
And produces probabilistic forecasts of the two parameters. An example of this
chain is represented in the figure below.
The tool makes use of machine learning and is able to capture the behaviour of
the system (in this case, the electric and heating demand of the building) considering only the measurements. The availability of further parameters can help
to improve the precision of the forecast and speed up the learning process.
The time horizon can range between 15 minutes to 48 hours and the update
rate is of 1 hour. They are both customisable.
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Electric demand forecast and measured demand
100%
90%
PV Production [% of Pn]
80%
70%
+- 40%
60%
+- 30%
50%
+- 20%
40%
+- 10%
Measured
30%
20%
10%
0%
00
Weather forecasts
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Forecast
tool
Heating demand forecast and measured heating
100%
90%
PV Production [% of Pn]
80%
70%
+- 40%
60%
+- 30%
50%
+- 20%
40%
+- 10%
Measured
30%
20%
10%
0%
Historic measures
00
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Probabilistic forecasts
INPUTS
Necessary
Static inputs: geo-localisation of the consumer, rated power
Dynamic inputs: weather forecast, measured historical electric demand
Wished
Static inputs: Energy survey (building physical model with dimensions, isolation
class and thermal inertia class (EN ISO 13786), building destination (residential, commerce, office...), list of appliances, socio demographic information of
the users (number, wealth...)).
Dynamic inputs: internal temperature, measured heating if not electric (e.g.:
gas)
OUTPUTS
Forecasts are presented as time series of variable length (in general from 24 to
72 hours) and variable time resolution (in general 30 minutes) of the following
parameters:
Point forecast: forecasts for the most probable value of the demand, forecasts for the highest possible value of the demand
Probabilistic forecasts: time series representing the quantiles of the production (with a resolution in general of 5% to 10%)
The forecasts are calculated for the electric and heating demand
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Smart Meter SM
Smart Meter SM3
Meadows weather station
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Meadows auxiliary data collector, Meadows Data Manager
3.4.2
PV Forecast
COMPONENT INFORMATION
TITLE
D1.2_SENSIBLE_Deliverable_final
PV Production forecast
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USE CASE
(Microgrid/Community, Microgrid PV Management, the component is not visible
in the component lists of these two UCs, but present in the description of the
UC), Distributed storage aggregator
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The tool provides forecasts for pv production within a defined area. It uses as
inputs:
1. Weather forecasts
2. Historical measurements
And produces probabilistic forecasts of the parameter. An example of this chain
is represented in the figure below.
The tool makes use of machine learning and is able to capture the behaviour of
the system (in this case, the PV panel) considering only the measurements.
The availability of further parameters can help to improve the precision of the
forecast and speed up the learning process.
The time horizon can range between 15 minutes to 48 hours and the update
rate is of 1 hour. They are both customisable.
PV Forecast and measured production
100%
90%
PV Production [% of Pn]
80%
Weather forecasts
70%
60%
+- 40%
50%
+- 30%
40%
+- 20%
30%
+- 10%
Measured
20%
Forecast
tool
10%
0%
00
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Probabilistic forecasts
Historic measures
INPUTS
Necessary
Static inputs: geolocalisation of the producer, rated power of the plant, Dynamic
inputs: historical and real time production, meteorological forecasts
Wished
Static input: survey of the plant: orientation and inclination, shadow, albedo of
the neighbouring area
Dynamic inputs: measured solar radiation
OUTPUTS
Forecasts are presented as time series of variable length (in general from 24 to
72 hours) and variable time resolution (in general 30 minutes) of the following
parameters:
Point forecast: forecasts for the most probable value of the production, forecast for the higher possible value of the production
Probabilistic forecasts: time series representing the quantiles of the production
(with a resolution in general of 5% to 10%)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
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Medows Weather Station
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DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
3.4.3
Storage aggregation optimisation tool
Medows data manager
Storage aggregator
COMPONENT INFORMATION
TITLE
Storage aggregator
USE CASE
Distributed storage aggregator, (proposed for Microgrid/Community)
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
This tool calculates the optimal plan for distributed energy storages taking into
account global and local constraints and objectives from multiple actors, such
as storage owners and grid operators. The tool verifies then that the plans at
the aggregate and individual level are followed, and calculates the necessary
actions for the BMS (battery management systems) in order to correct eventual
derives. The tool can express the potential of the storage controlled under the
form of bids for a market or as forecast of the flexibility.
COMPONENT
DESCRIPTION
INPUTS
Necessary
Static inputs: storage location (eg: connection point or grid zone), storage size
in energy (kWh) and power (kW).
Dynamic inputs: variable constraints, requests (of charge, discharge, target
state of charge), measured state of charge (kWh), measured charge discharge
(kW)
Wished
Static inputs: storage characteristics (size, charge and discharge efficiency,
technology, initial capital cost, expected lifetime)
Dynamic inputs: electricity prices
OUTPUTS
The output of the tool is a group of time series representing the optimal plans to
be followed by the storages. The horizon of the plan is usually between 24 and
72 hours and the time resolution is usually of 30 minutes.
If a market is available, the aggregator produces bids for this market in the
form of couple of power and price bids for individual time slots.
The aggregator produces also forecasts of the available flexibility corresponding to a determined cost
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Energy Market Service Platform
Energy Markets
Energy storage
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Energy Market Service Platform
Energy storage
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3.5
High-level Applications
3.5.1
Community applications
3.5.1.1 Meadows Weather Station
COMPONENT INFORMATION
TITLE
Functionality definition for the Meadows Community Data Collection
USE CASE
All use cases which use forecasting and real-time weather data which are to be
tested at the Meadows will at some point use the hardware.
CONTACT PERSON
Mark Gillott - mark.gillott@nottingham.ac.uk
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The meadows weather station will be mounted on a candidate building and the
externally mounted sensors will utilise the buildings monitoring network. Measurements of the following parameters will be recorded in real time at 5 minute
intervals: dry bulb temperature (deg.C), relative humidity (%), wind speed (m/s),
wind direction, global horizontal solar radiation (W/sq.m) and rainfall.
INPUTS
Weather sensor data,
OUTPUTS
real time data for partners, analytics and forecasting algorithms
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
None
DEPENDENCIES
WITH OTHER COMPONENTS
(OUTPUTS)
Software controllers [UoN], communications hardware/ integration gateway
[UoN, Adevice/USE]
Real time integration platform [Indra]
3.5.1.2 Meadows Data Manager
COMPONENT INFORMATION
TITLE
Functionality definition for the Meadows data manager hardware
USE CASE
All use cases which are to be tested at the Meadows will use the manager
hardware
CONTACT PERSON
Lee Empringham - lee.empringham@nottingham.ac.uk
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
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All use cases and test scenarios which are related to the Meadows demonstrator will use the Meadows data manager hardware.
This will consist of a high spec PC which will nominally use LabVIEW to visualise and control data coming from and to the demonstrator.
It is expected that the data from the different pieces of equipment and control
commands to the demonstrator resources will be come through the Indra Integration Platform in order to control the different pieces of equipment within the
community. This hardware will also be used to implement analytical algorithms
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or micro-grid management functions either from UoN or other partners within
the consortium within the LabVIEW environment.
This could also act as a node which will re-transmit data to and from the other
analytical functionalities of external partners if an external processor / control
algorithm is used and this functionality is lacking elsewhere.
This hardware will also be expected to act as the high level Energy management system required to coordinate the eBroker implementations within the
distributed equipment. Where the eBroker cannot be physically installed within
equipment, this hardware will instantiate individual eBroker nodes and use the
data transfer capabilities of the system to remotely control individual pieces of
equipment as if it were installed locally.
INPUTS
Network electrical measurement data, battery state of charge data, household
usage, auxiliary data collection - all through distributed integration gateways via
Indra integration Platform
Power / energy transfer demands from analytical entities/ market forecasters.
OUTPUTS
Power demands for grid interfaces, real time data for partners, equipment configuration data / control to implement different use case scenarios
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Software controllers [UoN], communications hardware/ integration gateway
[UoN, Adevice/USE]
Real time integration platform [Indra]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Real time integration platform [Indra]
OTHER COMMENTS
Specific requirements:
The manager should have access to all data nodes on the demonstrator
network via the Integration platform,
o Residential electrical measurements from three nodes in the household: PV generation, electrical / thermal storage charge / export data,
net electrical import / export
o Auxiliary data: Temperature, CO2 concentration, domestic hot water
usage data
o Community storage electrical measurements: generation, electrical
storage charge / export data, Community building energy usage
o All sampled data with 1second or greater sampling time should be
available in real-time
o All high speed sub second sampling can be buffered for later download
and processing
The manager should be able to send real-time control data to any piece of
equipment within in the demonstrator network in order to re-configure
equipment for the validation of different use case scenarios, for example:
o Set charge rates for residential and community energy storage inverters.
o To be able to disable default control modes of the residential inverters
in order to vary the operation based on the analytics packages being
validated
o To be able to group enable / disable charging / operation of storage
and PV inverters in order to determine the effect on the grid.
The manager will be able to host other partner’s grid analytics functionalities
/ packages if needed.
The manager will be able to send control data to the resources of the demonstrator in order to control and maintain the energy flow within the nodes
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of the demonstrator according to the needs of the use-case under investigation.
The manager will also be capable of managing the lab-based resources
within the FlexElec lab which will be needed to validate / prove equipment
and ICT architectures prior to deployment or test scenarios deemed to risky
for testing within the Meadows
The manager will need to have the final ON/OFF control of external analytics / control from external partners in order to fulfil the ethics requirements.
(this just means that the final control of the demonstrator must be within the
demonstrator in other partners are controlling the demonstrator hardware
for any particular scenario and will not change the functionality of the applied external control)
The manager will be used to calculate the amount of energy that has been
imported over and above that which the user would normally use if the use
case being tested forces the user to absorb energy that they did not need:
o This is only an issue since the residential energy storage devices will
be located behind the point of metering for individual billing purposes.
o This will be particularly important when demonstrating the use of residential storage for an aggregated community storage model
o Or if for example, one residence is demonstrating the ability to store
energy from several houses with PV generation, To the energy billing
company, that residence has used more energy than they have in reality since the energy will be exported later
The Manager will also be used to aggregate several network measurements
in order to create a ‘virtual’ micro-grid set of electrical values since the residences in the Meadows area are sparsely distributed and fed from different
substations. It would be more appropriate if this aggregation (summation of
the correct nodes on the network) could be done by the Indra Integration
Platform and then provide not only the individual nodal measurements but a
‘virtual’ substation measurement which would consider the demonstrator as
if it were a community fed by a single substation. This information will be
necessary for several use cases which need the ‘community’ consumption.
3.5.1.3 Visualization tool
COMPONENT INFORMATION
TITLE
Visualization Tool for users
USE CASE
The visualization tool should be a tool available and oriented to final users.
It shall provide real-time access to energy consumption data from users´ dwellings and houses.
Visualization tool main objective is to improve the user’s engagement with
SENSIBLE project, thanks to:
CONTACT PERSON
Provide remote access to real-time energy consumption.
Consumption monitoring in their smartphones or PCs
Manuel Alberto Moreno García, manuel.moreno@adevice.es
COMPONENT DESCRIPTION
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COMPONENT
DESCRIPTION
The visualization tool is designed to enable users to be aware of the advantages offered by the project implemented within the sensitive systems.
This tool shall serve exclusively as consultation service, ie, it allows to users to
know, remotely and in real-time, power consumption and supply quality.
But it will not allow sending commands to the system or modify the storage
systems performance.
Visualization tool is based on web services and shall be able to send queries to
Real Time platform in order to show the selected magnitudes.
INPUTS
Data stored in Real Time Platform from Indra.
OUTPUTS
Visualization tool show magnitudes selected by users.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
RT Integration Platform (INDRA)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Visualization in web browsers
OTHER COMMENTS
Consumption and QoS queries are sent periodically or on request
3.5.2
Market Applications
3.5.2.1 Energy Market Service Platform
COMPONENT INFORMATION
TITLE
Energy Market Service Platform
USE CASE
The Energy Market Service Platform combines grid enabled storage and resource control with market signals like price and individual contract constraints.
The energy market service platform thus creates new business opportunities
for end customers, energy suppliers and DSOs. It permits all kinds of flexible
resources present in a heterogeneous market with multiple network operators
and suppliers to come together on a level playing field based on market rules.
This lowers the barrier of entry for new suppliers and service providers to enter
the market and amplifies the aggregation force provided by the different technologies that provide initial technical aggregation of metering points. Services
like active balance management by using storage can be used to lessen price
risks for customers, thus lowering the total price of energy. Additionally, portfolios of storage and distributed energy connected through different technologies
will be shown to stabilize the energy system by connecting them to the regulating power markets.
CONTACT PERSON
Tuukka Rautiainen, Empower IM Oy, tuukka.rautiainen@empower.fi
COMPONENT DESCRIPTION
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COMPONENT
DESCRIPTION
The Energy Market Service Platform consists of different modules covering the
topics of Customer Information Management (CIS), Energy Data Management
(EDM) and Energy Services Management (EMS). The modules enable efficient
use of information gathered from the storage concepts, combine it with energy
market information/processes and as a result clarifies the management of distributed energy resources.
INPUTS
The Energy Market Service Platform utilizes measurement, control, forecast
and contract information provided by the storage enabled buildings and communities. The platform will also have inputs from the energy markets utilizing
price and other market data.
Specific data structures, contents and interfaces will be specified during the
project based on the services and concepts selected for the demonstrations.
OUTPUTS
As an output the Energy Market Service Platform will provide the buildings and
communities a connection to energy market processes. This can include for
example the transfer of market price data, forecasts, individual contract data
and billing data. The data can be utilized to determine how the storage solutions should be managed to achieve a balance between the requirements of
DSO’s, suppliers and the storage enabled communities and buildings.
Specific requirements for the outputs will be determined during the project.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Real Time Integration Platform [INDRA]
Other inputs will be connected through INDRA’s platform (Storage Controllers
[UoN, Mozes], Forecasting tools [Armines], eBroker [GPTech]), Smart Meters
[Adevice], Meadows Data Manager [UoN], Integration Gateway [UoN])
DEPENDENCIES
WITH OTHER COMPONENTS
(OUTPUTS)
Real Time Integration Platform [INDRA]
Other outputs will be connected through INDRA’s platform (Storage Controllers
[UoN, Mozes], Forecasting tools [Armines], eBroker [GPTech]), Smart Meters
[Adevice], Meadows Data Manager [UoN], Integration Gateway [UoN])
OTHER COMMENTS
Final integration between different systems should be determined based on the
concepts to be demonstrated.
3.5.2.2 Energy Markets
COMPONENT INFORMATION
TITLE
Energy Markets
USE CASE
The developed use cases utilize energy markets in various ways. The markets
can be divided into five levels covering derivative, day-ahead, intraday, balancing power and reserve markets. Also energy retail markets are closely attached
to the execution of the use cases. The stakeholders utilizing energy markets
are most often energy suppliers/retailers. The suppliers can utilize flexible resources like storage to optimize the energy production and procurement on
different market levels and therefore maximize earnings and minimize expenditures. Also grid operators, energy communities and individual prosumers may
utilize energy markets either directly or through aggregators or energy service
providers.
CONTACT PERSON
Tuukka Rautiainen, Empower IM Oy, tuukka.rautiainen@empower.fi
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
The energy markets consist of retail and wholesale markets. In this project
mainly the wholesale markets are being utilized at different time levels. Differ-
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ent countries and areas have their own market structures and power exchanges. The majority of this project’s countries are covered by EPEX, APX, MIBEL
and Nord Pool.
INPUTS
The energy markets get inputs from the Energy Market Service Platform implemented by Empower IM. Through the EMSP bids can be executed/simulated to the energy markets to provide balancing power, manage the
energy balance of a supplier, get maximal input for distributed generation etc.
OUTPUTS
The energy markets provide outputs in the form of price signals. Prices determined by open competition and solely by demand and supply, ensure that resources are being utilized in the most effective way.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Energy Market Service Platform [Empower IM]
Other inputs will be connected through Empower’s platform (Storage Controllers [UoN, Mozes], Meadows Data Manager [UoN], Integration Gateway [USE])
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Energy Market Service Platform [Empower IM]
Other outputs will be connected through Empower’s platform (Storage Controllers [UoN, Mozes], Meadows Data Manager [UoN], Integration Gateway [USE])
OTHER COMMENTS
Final integration between different components will be determined based on the
concepts to be demonstrated.
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4
Nuremberg Architecture
The Nuremberg demonstrator is focused on multi-modal energy storage in commercial buildings,
considering electro-thermal and electro-chemical storage, CHP, and different energy vectors
(electricity, gas). This section describes SENSIBLE’s technical approach to fulfil the three use
cases that will be tested in this demonstrator: increased percentage of self-consumption, optimized energy procurement from the power grid and managing energy flexibility.
The figure below shows Nuremberg SENSIBLE components that will result in a flexible platform
that facilitates the continuous monitoring of the building, including a wide range of devices, protocols and technologies. This platform will provide desirable mechanisms for the active power
management of smart buildings such as integration of renewable energy sources and energy
storage technologies or support grid stability in emergency situations.
Fig. 8: Nuremberg’s architecture schematic
In this figure, the different IEDs for last mile communications can be seen at the bottom of the
graph. The IEDs are located at the different points of the building to monitor and control the electro-thermal and electro-chemical storages and two energy generators: CHP-unit and Heat Pump.
An AHU is used to store energy in the building inertia and to use the whole thermal comfort zone
of a building. The BEMS will be responsible for collecting the data from the IEDs as well as to
send control signals to the field. In addition, a PV Emulation system and an Uncontrollable Building Base Load Emulation will be also integrated in the BEMS.
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As we go up in the ecosystem, we find the Real Time Integration platform component for providing real-time connectivity among the upper components through publish-subscribe or requestresponse mechanisms. These upper systems will give added-value to the BEMS. On the one
hand, the Analytics Module which includes the demand, PV generation and weather forecast algorithms will allow the BEMS to operate the infrastructure intelligently to best utilize the internal
flexibility in power consumption with respect to the future. On the other hand, the Energy Market
Service Platform combines the enabled storage and resource control with market signals like
price and individual contract constraints, allowing the reduction of building operation costs. The
Energy Market Service Platform will be connected to the Energy Markets component that will
simulate retail and wholesale markets in the demonstrator.
In the following sections, the descriptions of the Nuremberg architecture components are explained including the inputs and outputs of each component as well as the dependencies with the
rest of the components in the architecture.
4.1
Hardware devices
4.1.1
Energy storage
4.1.1.1 Power to Heat
COMPONENT INFORMATION
TITLE
Heat Pump Test Bench
USE CASE
The Heat Pump Test Bench is to emulate the behaviour of a building in a smart
grid. Thermal storages will include storage for heating and cooling. The Storage
Device Test Bench is controlled by the BEMS.
The self-consumption optimization potential will be investigated for different
scenarios.
Furthermore in a first step the possibilities of cold and hot storage in combination with the electrically driven heat will be studied. In a second step the potential of latent heat storage will be investigated.
The focus of the storage devices are applications in commercial buildings.
CONTACT PERSON
Arno Dentel, arno.dentel@th-nuernberg.de, THN
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
D1.2_SENSIBLE_Deliverable_final
The Heat Pump Test Bench will include storage for heating and cooling. The
storages will be sensible and, in a second step, can be latent. The Heat Pump
Test Bench is connected to a simulation area (simulated building and components).
1. Heat Pump Plant
The test bench consists of a heat pump (Dimplex SI 11TU) and a hot and a cold
water storage. The source for the heat pump is simulated and is represented by
a ground heat exchanger that is connected to both storages. Therefore the heat
pump is able to produce cold and hot water at the same time.
The heat pump is connected to the BEMS.
It follows a component description and a plan.
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INPUTS
As the Heat Pump Test Bench provides heating and cooling energy for the
building, it needs the heating and cooling demand of a (virtual) building.
OUTPUTS
The Heat Pump Test Bench gives out thermal power.
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DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
BEMS [Siemens]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
BEMS [Siemens]
4.1.1.2 Heat Power Coupling
COMPONENT INFORMATION
TITLE
CHP-Unit Test Bench
USE CASE
The CHP-Unit Test Bench is to emulate the behaviour of a building in a smart
grid. Thermal storage will include storage for heating and domestic hot water.
The CHP-Unit is controlled by the BEMS.
The self-consumption optimization potential will be investigated for different
scenarios. In particular, the potential of heat and electricity production in combination and in interaction with a smart grid will be studied.
The focus of the storage devices are applications in commercial buildings.
CONTACT PERSON
Arno Dentel, arno.dentel@th-nuernberg.de, THN
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The CHP-Unit Test Bench will include storage for heating and domestic hot
water. The storages will be sensible and, in a second step, can be latent. The
Test Bench is connected to the BEMS that provides the heating and cooling
energy of a virtual the building.
2.
CHP- Unit Plant
The test bench exists of a CHP-Unit and hot and domestic water storage. The
CHP-Unit uses natural gas as fuel.
The CHP-Unit is connected to the BEMS.
It follows a component description and a plan.
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INPUTS
As the CHP-Unit Test Bench provides heating energy for the building, it needs
the heating demand of a (virtual) building.
OUTPUTS
The CHP-Unit gives out thermal and electrical power.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
BEMS [Siemens]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
BEMS [Siemens]
4.1.1.3 Electrical power and building response
COMPONENT INFORMATION
TITLE
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AHU Test Bench
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USE CASE
The AHU Test Bench is to emulate the behaviour of the inertia of a building.
The AHU-Lab is controlled by the BEMS.
In particular, the potential of pre-cooling and pre-heating of the building volume
will be studied.
Furthermore the possibilities of electrical power in interaction with the response
of the building will be investigated.
The focus of the storage devices are applications in commercial buildings.
CONTACT PERSON
Arno Dentel, arno.dentel@th-nuernberg.de, THN
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The AHU Test Bench is for studying the electrical power consumption in addition to the response of the building. The AHU Test Bench is connected to the
BEMS that provides the heating and cooling energy of a virtual the building.
3. AHU-Lab
The AHU-Lab consists of two air conditioning systems for heating and cooling
and of a climate chamber, existing of a indoor and a outdoor zone.
The controller (Px/Tx controller: Siemens product) is connected virtually to the
BEMS.
It follows a component description.
INPUTS
As the Storage Device Test Bench provides heating and cooling energy for the
building, it needs the heating and cooling demand of a (virtual) building.
OUTPUTS
The storage device gives out thermal and electrical power, provided by the
energy generators
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
BEMS [Siemens]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
BEMS [Siemens]
4.1.2
Energy generation
The Generator-System-Lab provides a test environment for electrical driven heat and cold generators and decentralised power generators. There will be two energy generators: heat pump and
CHP-Unit. It is possible to test the dynamic behaviour of real generators and their interaction with
the grid. The facility provides an emulation test bench for heat pumps up to a capacity of 50 kW.
Also buffer storage tanks, a ground heat exchanger and the consideration of different buildings
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types can be implemented. The emulation test bench is controlled by LabView and TRNSYS. All
components, heat pump, CHP-Unit, storages, communicate by BEMS, provided by Siemens.
4.1.2.1 Heat Pump
The existing heat pump in the generation system lab provides heating and cooling energy. The
heat pump is a model Dimplex SI 11TU and has a heating power up to 10.9 kW. It is planned to
install a 1000 litres hot and 1000 litres cold water storage for the SENSIBLE Project. The communication interface is a Modbus.
Fig. 9: Heat Pump schematic
4.1.2.2 CHP-Unit
The planned CHP-Unit will provide power for heating up to 12.5 kW and for electricity up to 5 kW.
The fuel will be natural gas. It will be connected to 1000 litter hot water storage, 500 litres domestic hot water storage and to the electrical storage/grid. The communication interface is a Modbus
or RS 232.
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Fig. 10: CHP-Unit schematic
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4.2
Low-level Control Systems
4.2.1
Battery Storage System
COMPONENT INFORMATION
TITLE
Battery Storage System
USE CASE
Load-shifting is enabled by storing part of the excess energy generated during
maximum generation periods (e.g. by PV) in electro-chemical storage units
(e.g. Li-Ion Battery). Stored electrical energy can be used later on during peak
hours. In this way, additional flexibility is available to maximize selfconsumption and reduce energy procurement from local power grids.
CONTACT PERSON
Dr. Amjad Mohsen, amjad.mohsen@siemens.com, Siemens AG, CT RTC PET
POM-DE
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
Renewable energy resources (RES) such as PV, besides local generation units
such as CHP and HP units, are increasingly integrated in commercial buildings
in order to reduce energy procurement from local power grids. Energy storage
units in general and electro-chemical storage units in particular play a central
role to enable efficient usage of energy generated by RES by enabling loadshifting.
The BEMS optimizes an operation schedule to maximize self-consumption by
considering electrical and thermal loads, locally generated energy by CHP, HP
or RES as well as energy storage units (e.g. battery and thermal storage). The
BEMS considers the additional flexibility offered by a battery storage system to
optimize the operation schedule and to maximize the energy efficiency in the
building infrastructure.
The targeted battery storage system consist of a Li-Ion battery connected to a
bidirectional inverter/rectifier (3 phase) for charging/ discharging and a management system (controller). The maximum power load of the bidirectional
inverter is 50 kW (in both directions). The battery has a capacity of 25 (36)
kWh. Charging/discharging of the battery is directly planed/ controlled by BEMS
as an integrated part of the building infrastructure.
INPUTS
400 V (3-phase AC) and control signals. No other direct (external) inputs are
needed to this component.
OUTPUTS
400 V (3-phase AC). No direct (external) outputs are needed to this component.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
(Not directly dependent on other components but managed by the BEMS)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
(Not directly dependent on other components but managed by the BEMS)
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4.2.2
PV Emulation System
COMPONENT INFORMATION
TITLE
PV Emulation System
USE CASE
Self-consumption of locally generated power (e.g. from PV) is maximized to
reduce energy procurement from the power grid. For that purpose, the BEMS
optimizes the power flow from the PV to the storage units (electro-chemical and
thermal) and the energy consumers.
CONTACT PERSON
Dr. Amjad Mohsen, amjad.mohsen@siemens.com, Siemens AG, CT RTC PET
POM-DE
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The integration of renewable energy resources (RES) such as PV, besides
local generation units such as CHP and HP units, is necessary in smart buildings in order to reduce the energy procurement from the local power grids. The
BEMS optimizes an operation schedule to maximize self-consumption of the
locally generated energy. Locally generated energy can either be consumed
directly or stored for later use during peak demand. The electrical energy from
RES (e.g. PV) can either be directly consumed/stored as an “electrical energy”
or transformed to other energy forms (e.g. thermal energy). The BEMS optimizes the right mix between electrical and thermal energy to maximize the energy efficiency in the building infrastructure.
The BEMS optimizes an operation schedule for energy consumers and energy
generators in the building taking into consideration the expected energy generation by PV. For that purpose, the BEMS uses weather forecast (e.g. temperature) together with the “global horizontal irradiation” (GHI) and “diffuse horizontal irradiation” (DHI) and other configuration parameters to predict the electrical
energy gained over time from the locally installed PV. During online power
monitoring and management, BEMS monitors the actual energy generation by
all energy generation units including the PV and necessary actions are taken to
compensate for mismatches between generation forecast and actual generation as an integrated part of managing the entire infrastructure of the building.
The PV Emulator consists of a PV panel emulator which is connected to a PVinverter. The inverter can feed active and reactive power to building nano grid.
The panel emulator uses AC/DC inverters to emulate a PV-Panel. The assumed PV panel configuration together with the weather and solar radiation
forecasts are used to compute the PV panel U/I characteristic.
INPUTS
400 V (3-phase AC),
Control signals,
Weather forecast data,
(The PV Emulation is an integrated part of the building infrastructure directly
controlled by the BEMS: No direct (external) inputs are needed to this component.)
OUTPUTS
400 V (3-phase AC). No other direct (external) outputs are needed to this component. The PV Emulation is an integrated part of the building infrastructure
directly controlled by the BEMS.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Forecasting tools [Armines],
(obtained through the BEMS)
DEPENDENCIES
(Not directly dependent on other components but managed by the BEMS)
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WITH OTHER COMPONENTS (OUTPUTS)
4.2.3
Building Uncontrollable Load
COMPONENT INFORMATION
TITLE
Building Uncontrollable Load
USE CASE
Optimizing the operation schedule in the commercial building in order to minimize energy procurement from the power grid and to maximize the energy efficiency of the building infrastructure in the presence of uncontrollable loads (e.g.
Lighting, desktop computer and other electrical appliances).
CONTACT PERSON
Dr. Amjad Mohsen, amjad.mohsen@siemens.com, Siemens AG, CT RTC PET
POM-DE
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
Controllable loads offer a major degree of freedom for the BEMS to optimize an
operation a schedule in order to reduce energy procurement from the power
grid. However, the influence of uncontrollable loads (base load) on the total
energy consumption in the building infrastructure cannot totally be ignored
when optimizing the operation. Hence, uncontrollable loads are simply aggregated. To include their influence, uncontrollable loads are emulated by using an
AC/DC inverter able to stick to a previously defined load course and a DC/AC
controllable power supply (400 V, 3-phase) feeding back the electric energy to
the grid.
INPUTS
No direct (external) inputs are needed to this component except a 3-phase
power source.
OUTPUTS
No direct (external) outputs are needed to this component.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
(Not directly dependent on other components but managed by the BEMS)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
(Not directly dependent on other components but managed by the BEMS)
4.2.4
BEMS
COMPONENT INFORMATION
TITLE
Active Building Energy Management System
USE CASE
The BEMS is indented to enable active multi-modal power management in a
commercial building, especially by integrating relevant electro-chemical and/or
thermal storage(s). The BEMS uses appropriate forecast algorithms to predict
electrical and thermal power production and demand in the building and operates the underlying infrastructure intelligently to best utilize the internal flexibility
in power consumption with respect to future uncertainties. The BEMS also op-
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timizes the proportion and distribution of different energy resources (electrical
vs. Thermal power consumption) to enable most efficient usage of electrical
energy.
CONTACT PERSON
Dr. Amjad Mohsen, amjad.mohsen@siemens.com, Siemens AG, CT RTC PET
POM-DE
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
Many features needed for active power management such as integration of
renewable energy sources and energy storage technologies in smart buildings
are missing in current BEMS and BACS (building automation and control systems). The new active BEMS enables buildings to plan and operate the underlying infrastructure at maximum sustainability by extending current technologies
such as BACS and integrating multi-modal energy management technologies.
The active BEMS intelligently operates power generators, energy storage units
and controllable loads to utilize the internal flexibility in the underlying infrastructure. Power generation includes electrical (e.g. PV), thermal (e.g. solar
collectors and heat pumps) sources and combined heat and power systems
(CHP). The main energy storage technologies are electro-chemical energy
storage (e.g. Lithium-Ion battery) and thermal energy storage (TES). Controllable electrical loads and thermal loads (e.g. domestic hot water and radiant
heating and cooling system) are considered as well.
The multi-modal power-intelligent operation of the building includes two major
functional modules, namely offline power flow optimization and online power
monitoring & management. A power flow plan is optimized offline based on
historic load and forecast data usually obtained from external providers (e.g.
weather forecast service providers). The online module then monitors the state
of power generation and consumption to ensure that the optimized flow plan is
being followed in the best possible manner despite of disturbances and unforeseen events.
Main objectives of the active BEMS are enabling best utilization of the internal
flexibility in the building to shave off peak power consumption and to match
power demand to available power supply (e.g. through load shifting) and to
possibly generate revenue within the energy market.
INPUTS
Energy price data (e.g. energy tariff models) and real-time balancing power
provision [EMPOWER],
Forecast data [ARMINES]
OUTPUTS
Load forecast at PCC [EMPOWER],
Aggregated flexibility of the building infrastructure,
Real-time measurement (e.g. power consumption at PCC)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Energy market platform provider [EMPOWER],
Forecasting tools [Armines],
Real-time integration platform [INDRA]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Energy market platform provider [EMPOWER],
Real-time integration platform [INDRA]
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4.3
Real Time Integration platform
COMPONENT INFORMATION
TITLE
Real Time Integration Platform
USE CASE
iSpeed is an integrated platform for real-time data acquisition and processing,
with the ability to handle large volumes of information at low latency. Its main
objective is to increase productivity and effectiveness in the exchange and
management of information generated by various monitoring and control applications, while reducing the chances of error in data manipulation. It is characterized by providing a fast and reliable exchange of information between the
actors connected to it, ensuring at all times the availability of information at low
and flexible coupling between them. In addition, this platform provides the necessary tools to perform real-time distributed data processing and also the
means to persist the information that will subsequently treated in batch analytic
processes.
CONTACT PERSON
Catherine Murphy-O’Connor cmurphy@indra.es
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
iSpeed is a platform developed under the XTPP concept (eXtreme Transaction
and Processing Platform) which is capable of providing the following services
anywhere in the data acquisition and processing network:
Unified Data Model: based on specific domain models (IEC 61970-61968,
IEC 61850 X) and provides storage and retrieval capabilities in different
time horizons and data access technologies.
Real-Time Messaging Service: capable of supporting real-time communications between any of the systems involved in the management, monitoring
and network operation.
Complex Event Processing Service: to handle events both locally of an asset (filtering and data analysis), and capable of processing large aggregates
and global events.
iSpeed is a SOA architecture (Service Oriented Architecture) based on the
publish/subscribe data distribution paradigm. It has been built over the DDS
(Data Distribution Service) middleware standard for data distribution, where
aspects such as transparency and failover, distribution and deployment of software without service interruptions, and data delivery are specified. Moreover,
the middleware that provides the messaging service in the real-time platform is
capable of being loaded on computers with low processing capacity, so that
data can be collected directly from low-level nodes.
One of the key pieces of a real-time platform is the communication middleware.
It is a logical area in which the information, in data structures form, is shared
between two types of actors: publishers and subscribers.
Publishers are the elements that have information of interest to other systems and provide this information on the bus.
Subscribers are those actors who are awaiting information that will later
process.
Although the iSPEED Real Time Integration Bus is designed to exchange information in real time, we cannot forget the classic techniques for data exchange, such as ESB based on the Request/Response paradigm. The communication cannot always be performed asynchronously and thus, it is in this
situation where the use of web services enables synchronous communication
between the systems that are connected. In order to be as flexible and agile as
possible in the implementation, SOAP and Restful services are also proposed
in the iSPEED bus.
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In the case of the Nuremberg demonstrator, the integration of the different systems carried out by the RTP will be through the ESB. If critical real-time data is
needed in the demonstrator, such as Grid Order Signal, this information exchange could be conducted through the DDS standard.
INPUTS
As iSPEED is the middleware communication infrastructure, there will be not
explicit inputs. The different systems connected to the platform will exchange
the information using the SENSIBLE data structures or web services that are
going to be defined in the project taking into account the demonstrator use
cases.
Interfases with the platform will be carried out through DDS technology, using
the Real-Time Publish-Subscribe (RTPS) protocol designed to be used over
UDP, in case of Real Time data or traditional SOAP or REST-Json software
architecture style, using HTTP protocol, in case of historical data.
OUTPUTS
As iSPEED is the middleware communication infrastructure, there will be not
explicit inputs. The different systems connected to the platform will exchange
the information using the SENSIBLE data structures or web services that are
going to be defined in the project taking into account the demonstrator use
cases.
Interfases with the platform will be carried out through DDS technology, using
the Real-Time Publish-Subscribe (RTPS) protocol designed to be used over
UDP, in case of Real Time data or traditional SOAP or REST-Json software
architecture style, using HTTP protocol, in case of historical data.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
The RTP in the Nuremberg demonstrator will act as an ESB routing service.
The systems integrated in the ESB will only need to know the IP where the
ESB will be installed and they will not need to know any of the other system’s
IP where the web services really will be allocated. The systems integrated by
the ESB of the RTP are: BEMS [SIEMENS], Energy Markets [Empower], Energy Market Service Platform [Empower] and Forecasting and optimization algorithms [Armines]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
The RTP in the Nuremberg demonstrator will act as an ESB routing service.
The systems integrated in the ESB will only need to know the IP where the
ESB will be installed and they will not need to know any of the other system’s
IP where the web services really will be allocated. The systems integrated by
the ESB of the RTP are: BEMS [SIEMENS], Energy Markets [Empower], Energy Market Service Platform [Empower] and Forecasting and optimization algorithms [Armines]
4.4
Analytics
4.4.1
PV Production Forecast
COMPONENT INFORMATION
TITLE
PV Production forecast
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
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The tool provides forecasts for PV production within a defined area.
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INPUTS
Necessary
Static inputs: geolocalisation of the producer, rated power of the plant, Dynamic
inputs: historical and real time production, meteorological forecasts
Wished
Static input: survey of the plant: orientation and inclination, shadow, albedo of
the neighbouring area
Dynamic inputs: measured solar radiation
OUTPUTS
Forecasts are presented as time series of variable length (in general from 24 to
72 hours) and variable time resolution (in general 30 minutes) of the following
parameters:
Point forecast: forecasts for the most probable value of the production, forecast for the higher possible value of the production
Probabilistic forecasts: time series representing the quantiles of the production (with a resolution in general of 5% to 10%)
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
BEMS [SIEMENS]
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
BEMS [SIEMENS]
Energy Market Service Platform [EMPOWER]
4.4.2
Demand Forecast
COMPONENT INFORMATION
TITLE
Electric demand forecast
USE CASE
Increased percentage of self-consumption, Optimized energy procurement,
Managing Building Energy Flexibility
CONTACT PERSON
Alexis BOCQUET, alexis.bocquet@mines-paristech.fr, ARMINES
Andrea MICHIORRI, andrea.michiorri@mines-paristech.fr, ARMINES
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
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The tool provides forecasts for the electric and heating demand for individual
consumers. It uses as inputs:
1. Weather forecasts
2. Historical measurements
And produces probabilistic forecasts of the two parameters. An example of this
chain is represented in the figure below.
The tool makes use of machine learning and is able to capture the behaviour of
the system (in this case, the electric and heating demand of the building) considering only the measurements. The availability of further parameters can help
to improve the precision of the forecast and speed up the learning process.
The time horizon can range between 15 minutes to 48 hours and the update
rate is of 1 hour. They are both customisable.
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Electric demand forecast and measured demand
100%
90%
PV Production [% of Pn]
80%
70%
+- 40%
60%
+- 30%
50%
+- 20%
40%
+- 10%
Measured
30%
20%
10%
0%
00
Weather forecasts
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Forecast
tool
Heating demand forecast and measured heating
100%
90%
PV Production [% of Pn]
80%
70%
+- 40%
60%
+- 30%
50%
+- 20%
40%
+- 10%
Measured
30%
20%
10%
0%
Historic measures
00
01
03
04
06
07
09
10
12
13
15
16
18
19
21
22
Time [hh]
Probabilistic forecasts
INPUTS
Necessary
Static inputs: geo-localisation of the consumer, rated power
Dynamic inputs: weather forecast, measured historical electric demand
Wished
Static inputs: Energy survey (building physical model with dimensions, isolation
class and thermal inertia class (EN ISO 13786), building destination (residential, commerce, office...), list of appliances, socio demographic information of
the users (number, wealth...)).
Dynamic inputs: internal temperature, measured heating if not electric (e.g.:
gas)
OUTPUTS
Forecasts are presented as time series of variable length (in general from 24 to
72 hours) and variable time resolution (in general 30 minutes) of the following
parameters:
Point forecast: forecasts for the most probable value of the demand, forecasts for the highest possible value of the demand
Probabilistic forecasts: time series representing the quantiles of the production (with a resolution in general of 5% to 10%)
The forecasts are calculated for the electric and heating demand
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
BEMS
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
BEMS
4.5
Market Applications
4.5.1
Energy Market Service Platform
COMPONENT INFORMATION
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TITLE
Energy Market Service Platform
USE CASE
The Energy Market Service Platform combines grid enabled storage and resource control with market signals like price and individual contract constraints.
The energy market service platform thus creates new business opportunities
for end customers, energy suppliers and DSOs. It permits all kinds of flexible
resources present in a heterogeneous market with multiple network operators
and suppliers to come together on a level playing field based on market rules.
This lowers the barrier of entry for new suppliers and service providers to enter
the market and amplifies the aggregation force provided by the different technologies that provide initial technical aggregation of metering points. Services
like active balance management by using storage can be used to lessen price
risks for customers, thus lowering the total price of energy. Additionally, portfolios of storage and distributed energy connected through different technologies
will be shown to stabilize the energy system by connecting them to the regulating power markets.
CONTACT PERSON
Tuukka Rautiainen, Empower IM Oy, tuukka.rautiainen@empower.fi
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The Energy Market Service Platform consists of different modules covering the
topics of Customer Information Management (CIS), Energy Data Management
(EDM) and Energy Services Management (EMS). The module enable efficient
use of information gathered from the storage concepts combine it with energy
market information/processes and as a result clarifies the management of distributed energy resources.
INPUTS
The Energy Market Service Platform utilizes measurement, control, forecast
and contract information provided by the storage enabled buildings and communities. The platform will also have inputs from the energy markets utilizing
price and other market data.
Specific data structures, contents and interfaces will be specified during the
project based on the services and concepts selected for the demonstrations.
OUTPUTS
As an output the Energy Market Service Platform will provide the buildings and
communities a connection to energy market processes. This can include for
example the transfer of market price data, forecasts, individual contract data
and billing data. The data can be utilized to determine how the storage solutions should be managed to achieve a balance between the requirements of
DSO’s, suppliers and the storage enabled communities and buildings.
Specific requirements for the outputs will be determined during the project.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Real Time Integration Platform [INDRA]
Other inputs will be connected through INDRA’s platform (BEMS [Siemens],
Forecasting tools [Armines])
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Real Time Integration Platform [INDRA]
Other outputs will be connected through INDRA’s platform (BEMS [Siemens],
Forecasting tools [Armines])
OTHER COMMENTS
Final integration between different systems should be determined based on the
concepts to be demonstrated.
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4.5.2
Energy Markets
COMPONENT INFORMATION
TITLE
Energy Markets
USE CASE
The developed use cases utilize energy markets in various ways. The markets
can be divided into five levels covering derivative, day-ahead, intraday, balancing power and reserve markets. Also energy retail markets are closely attached
to the execution of the use cases. The stakeholders utilizing energy markets
are most often energy suppliers/retailers. The suppliers can utilize flexible resources like storage to optimize the energy production and procurement on
different market levels and therefore maximize earnings and minimize expenditures. Also grid operators, energy communities and individual prosumers may
utilize energy markets either directly or through aggregators or energy service
providers.
CONTACT PERSON
Tuukka Rautiainen, Empower IM Oy, tuukka.rautiainen@empower.fi
COMPONENT DESCRIPTION
COMPONENT
DESCRIPTION
The energy markets consist of retail and wholesale markets. In this project
mainly the wholesale markets are being utilized at different time levels. Different countries and areas have their own market structures and power exchanges. The majority of this project’s countries are covered by EPEX, APX, MIBEL
and Nord Pool.
INPUTS
The energy markets get inputs from the Energy Market Service Platform implemented by Empower IM. Through the EMSP bids can be executed/simulated to the energy markets to provide balancing power, manage the
energy balance of a supplier, get maximal input for distributed generation etc.
OUTPUTS
The energy markets provide outputs in the form of price signals. Prices determined by open competition and solely by demand and supply, ensure that resources are being utilized in the most effective way.
DEPENDENCIES WITH OTHER SENSIBLE COMPONENTS
DEPENDENCIES
WITH OTHER COMPONENTS (INPUTS)
Energy Market Service Platform [Empower IM]
Other inputs will be connected through Empower’s platform (BEMS, distributed
energy resources etc.)
DEPENDENCIES
WITH OTHER COMPONENTS (OUTPUTS)
Energy Market Service Platform [Empower IM]
Other outputs will be connected through Empower’s platform (BEMS, distributed energy resources etc.)
OTHER COMMENTS
Final integration between different components will be determined based on the
concepts to be demonstrated.
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