S. Bracco, F. Delfino, F. Pampararo, M. Robba, M. Rossi
stefano.bracco@unige.it, federico.delfino@unige.it, fabio.pampararo@unige.it,
michela.robba@unige.it, mansueto.rossi@unige.it
DIME
Department of mechanical,
energy, management and
transportation engineering,
University of Genova
DIBRIS
Department of Computer Science,
Bioengineering, Robotics and
Systems Engineering
DITEN
Department of Naval,
Electrical , Electronic and
Communication Engineering,
University of Genova
Content
 Introduction about renewable energies, smart grid,





test-bed facilities for research development
Aim of this work
The Savona Campus Smart Polygeneration
Microgrid (SPM)
The SPM technologies and different subsystems
A first dynamic decision model for the SPM optimal
control
Conclusions and research challenges
Introduction
 New energy sources with low greenhouse emissions are needed in order
to reconcile the huge energy demand with an acceptable climatic
impact (Rubbia, 2006)
 Renewable resources (wind, solar, biomass, hydro, etc.) can be used to
provide energy: they are intermittent and distributed over the territory
 Many national and international research programs are aiming at
developing innovative technologies and new energy management
strategies in order to reach the targets set out by EU in the 20-20-20
Directive
 Microgrids integrate different distributed energy sources and energy
storage devices, and need intelligent management methods
Microgrids research: Experimental tests and
demonstration projects
 Importance of deriving new methods and tools for the
optimal control of smart grids
 Lidula and Rajapakse (2011) present a review of existing
microgrid test networks around the world (North America,
Europe and Asia) and some innovative simulation models
present in literature. They review about 20 test research
networks around the world
 Actually, there are about 90 examples of microgrids around
the world. Their number is supposed to increase in the near
future
Aim of this work
 To present the SPM (Smart Polygeneration
Microgrid)
 To highlight the SPM technologies and sub-systems
(like in Phillips (2008))
 To highlight the main research lines to be
developed within the SPM
 To present a first decision model as an example of
the possible approaches that can be used for the
SPM optimal control
The University of Genova, Savona Campus, SPM:
 Born from the “2020 Energy” Project (Italian Ministry of Education,
University and Research Funding) at the Savona University Campus
 During the year 2011 the preliminary design, the final design and the
working plan of the infrastructure have been developed
 In the current year 2012 works will start. The expected date for the testbed completion is June 2013.
The SPM can be used for two
main purposes:
• demonstration and teaching
activities
• to test models, methods and
tools related to the research
challenges of smart grids.
4
Energy Hub
Combustion Lab
Building
«Sustainable Energy»
6
Low/Medium Voltage
Electrical devices
1
2
Control Room
7
4
5
6
The SPM technologies
 a micro-cogeneration gas turbine fed by natural gas (C65 Capstone model, electrical
power output = 65 kWel, thermal power output = 112 kWth, electrical efficiency = 29%,
exhaust gas mass flow rate = 0.49 kg/s, exhaust gas exit temperature = 309°C);
 a photovoltaic field (nominal power output = 49.9 kWel, 13 parallel arrays, each
containing 16 modules, module efficiency = 14.5%, tilt angle = 30°, azimuth angle = -30°);
 two cogenerative concentrated solar-powered (CSP) systems, equipped with
Stirling engines (each characterized by 1 kWel and 3 kWth power output, electrical
efficiency = 13%, thermal efficiency = 40%, solar concentrator diameter = 3.75 m);
 two micro wind mills (one HAWT and one VAWT, each characterized by 3 kWel power
output);
 two absorption chillers (cooling capacity = 32.5 kWth, coefficient of performance =
0.75) equipped with a 3000 l storage tank;
 the electrical storage (high voltage Sodium-Nickel batteries having an energy storage
capacity of about 100 kWh);
 two electric vehicles charging stations;
 other electrical devices (inverters and smart metering systems).
The SPM’s
peculiarity/innovation
is due to:
• the set of generation units and
storage systems for both
electrical and thermal energy
production that make it a
complete test-case;
• the possibility of defining and
updating a software for the SPM
control;
• a fast telecommunication
network;
• and the integration with the
research activities of the
Engineering Faculty.
The SPM and the Campus
sub-systems
SPM
electrical
demand
External
Net
The SPM electrical
sub-system
Electrical power production,
storage, net - Campus - SPM
exchanges
Co-generation
plants
(microturbine)
CSP
The SPM control
sub-system
The CAMPUS
electrical sub-system
Campus
demand
Thermal production,
thermal storage
SPM
thermal
demand
The SPM thermal
sub-system
The CAMPUS
thermal sub-system
The SPM electrical sub-system
It includes: a cogeneration microturbine, two parabolic solar concentrators, two wind turbines, a photovoltaic,
electrical vehicle charging stations, smart meters, inverters, storage batteries, a dedicated grid connected to
the existing Campus grid and to the public distribution network
 The net is essentially characterized by a number of bus-bars
(nodes) connected by a ring distribution network.
 At each node, a power balance for incoming and outcoming power
flows could be considered.
 Different levels of detail could be used to model this subsystem
 If a detailed representation of the units’ dynamic behaviour were
necessary, a quite complex electromechanical model, taking into
account gas turbines and inverters dynamic models, together with
their governors, controllers, etc., should be adopted.
 In the proposed application, given the relatively short
connections between bus-bars and, most important, the
simulation step of 15 minutes, far larger than the time
required by the electrical sub-system to reach the steady
state, this subsystem can be modeled as a single bus-bar,
considering the active power balance only.
The SPM thermal sub-system
Heat losses in the district heating network are neglected as well as the
dynamics due to the heat storage system. The following generation units have
been considered: the microturbines and the Campus boiler.
The SPM control sub-system
It includes local controllers, a communication network, and a
central controller.
The control network uses
both communication
protocols Modbus and
RS485, and the protocol IEC
61850 that, in future years,
should become a reference
standard for communications
and control architectures in
the low-voltage smart grid
sector.
ICT
The software for the overall system management guarantees the SPM operations,
monitoring and alarms management. Moreover, it allows adding new components,
models and tools useful for the optimization and control.
Local controllers subsystem: they include the
interfaces with the field,
composed by those
devices that directly
interact with the
electrical network
(RTUs-Remote Terminal
Units) with actions on
measurement of relevant
parameters (i.e., current,
voltage, temperature,
etc.) and on the different
actuators (e.g., switches)
State of the art and innovation
 The increase of renewable energies (intermittent, distributed) and of the concept of
distributed generation has opened new challenges for the definition of decision models,
decision support systems, and controllers that may help in the planning and management of
the overall electrical grid.
 Key issues from a system engineering point of view: lack of a unified mathematical
framework with robust tools for modeling, simulation, control and optimization of
time critical operations in complex multicomponent and multiscaled networks
(Amin, 2011).
 Other issues related to smart grids are: stochastic models for demands, prices, resources
availability estimation; state estimation and robustness, fault diagnosis, algorithms to
deal with complex systems and models
 Recent reviews about the research needs in control of microgrids with storage (Zamora and
Srivastava, 2010): centralized and decentralized architectures.
 Moreover, in literature, despite many contributions related to planning decision problems,
there are few articles in the field of real time optimal control of a mix of renewable power
plants integrated in an electrical network
 In this work, as an example, a first dynamic optimization
problem is presented, with specific reference to a portion
of the SPM.
Decision Support Systems (DSSs) may help for planning and
control purposes, taking into account the different objectives
and/or sub-systems
Modules of a DSS that should be used for
different spatial scales: local to
national/international level
Building
Sub-District
Modules of a DSS that should be used for
different decision problems at different time
scales: strategic planning, tactical planning,
and operational management
District
The Zonal Subsystem
The National Subsystem
Monitoring Sub-System
Electrical Market
The Local Subsystem
Aim of the presented decision model
 Determining the optimal values over time of the microturbines and boiler
electrical and thermal power output, of the storage injection/withdrawal,
and of the electrical power exchange with the external grid, according to
the time-varying thermal and electrical loads, fuel and electricity prices,
available energy forecasts
 Different performance indexes can be formalized (Delfino et al., 2010), for
example: costs related to purchasing of electricity and natural gas; benefits
due to electricity sale and incentives for local consume of produced energy;
the carbon footprint of the overall system .
 In this work, operating costs and benefits are considered as objectives,
while the carbon footprint is evaluated for the optimal solution.
The formalized decision model includes only a sub-set
(decribed by the previous equations) of the SPM system
State and control variables
State variables are represented by the storage state of charge SOCt
Decision variables
Primary: PPE,k,t, PPE,B,t, PNET,t and PS,t
Secondary (Pel,k,t, Pth,k,t, Pth,B,t)
Binary control variable, δk,t: it is set to 1 if the k-th microturbine works in
time interval (t, t+1), and 0 otherwise.
The electrical sub-system
Microturbine k:
Power output [kW] depends
on environmental conditions
Pel _ full , k ,t
*
pel
, k ,t
P

 Pel _ max, k if t  min, k

P
f

if






t
min, k
 k t

Efficiency depends on microturbine power
level and ambient temperature
el _ full ,k ,t


el _ max, k if t  min, k


g

if






k
t
t
min,
k

θt : ambient
temperature
Pel,k ,t
el* , k ,t  hk ( pel* , k ,t )
Pel _ full,k ,t
Power that corresponds to
primary energy
PPE ,k ,t 
Pel ,k ,t
el ,k ,t
el* ,k ,t

el ,k ,t
el _ full,k ,t
ηel,k,t and Pel,k,t being respectively the actual values in time interval (t,t+1) of
electrical efficiency and power output
The primary energy flow is PPE,k,t (natural gas flow rate multiplied by its lower
heating value)
The storage (Brekken, 2011):
SOCt 1  SOCt t
PS ,t
CAP
t
As regards the photovoltaic system, its power output PPV,t is an input of the
model. Finally, there is the variable PNET,t which indicates the power
exchanged with the external grid (withdrawn if positive, injected if negative).
The formalized decision model includes only a sub-set
(decribed by the previous equations) of the SPM system
The thermal sub-system
Microturbine k:
Pth,k ,t   k Pel,k ,t
k 
Pth _ nom ,k
Pel _ nom ,k
Boiler:
Pth,B,t  B  PPE,B,t
Pth_nom,k and Pel_nom,k being respectively the
gas turbine nominal thermal and electrical
power output (evaluated at ISO conditions).
The objective function
min CTOT  minCB  CK  CNET  BNET 
CTOT=operating costs over the time optimization horizon
CB=boiler costs
CK =microturbine costs
CNET, BNET=costs and benefits related to the electricity exchange with the net
T 1
P
CB 
PE , B , t
 TESpp  t
t 0
 T 1 Pel ,k ,t  t

CK   Ck   
 CNGt 
k 1
k 1  t 0 el ,k ,t  LHV

K
K
T 1
T 1
BNET 
C
t 0
CNGt  0.25el,k ,t  LHV  0.12 NGppwf  NGpp   NGpp
NET
 min(PNET ,t ,0)  t
CNET 
C
NET , t
 max( PNET ,t ,0)  t
t 0
NGpp and NGppwf being the natural gas purchasing price with and without fee (0.7
and 0.427 €/m3).
TESpp : the thermal energy service purchasing price of the boiler (0.0853 €/kWhPE)
LHV :the natural gas lower heating value (9.7 kWhPE/m3)
CNET,t is the electricity purchasing price expressed in €/kWhe
CNET is a medium price of the electricity sold to the external grid;
CNG is the gas unitary cost (€/m3) for cogeneration gas turbines
The constraints
Del,t and Dth,t :
respectively, the
electrical and the
thermal power
demand
K
Del ,t  PNET ,t   Pel ,k ,t  PPV ,t  PS ,t
k 1
All equations previously
formalized
Each microturbine is characterized by
a “technical minimum power”
(Pmin,el,k), below which the machine is
shut down in order to avoid high CO
emissions;
K
Dth,t 
P
th,k ,t
 Pth,B,t
k 1
0  SOCt  1
Battery state of charge
Pel,k ,t  Pmin,el,k k ,t
Pel,k ,t  M k ,t  0
The carbon footprint assessment
ECO  ECO2 ,B  ECO2 ,K  ECO2 ,NET
2
T 1
ECO2 , B 
P
PE, B,t
f e fo 1 t
t 0
T 1





PPE,k ,t f e fo 1 t 


k 1 
 t 0

K
ECO2 , K

T 1
ECO2 , NET 

t 0


max 0, PNET , t f e, NET 2 t
fe ,fo : the emission (56 tCO2/TJPE) and the
oxidation factor (0.995) of the natural gas
μ1 ,μ2 : conversion factors
fe,NET : the emission factor of the national
electrical mix (0.465kgCO2/kWhe)
Results
Time horizon: 24 hours
Time discretization:15
Mixed non linear optimization problem
The optimal value of the daily operating
cost is equal to 475 €, and CO2
emissions are equal to 1.38 t.
Receding Horizon control scheme
Horizon: 3 hours
Local optima runtime: 10 seconds
Global optimum runtime (Global
Solver): 4 minutes
Number of variables: 108
Results
Scenario b, the control variable PS,t for the storage has been set equal
to zero. The daily operating cost is of 479 € and CO2 emissions are
equal to 1.43 t.
Conclusions and research
challenges
 A test-bed facility at the Savona University Campus has
been shown
 The SPM sub-systems have been highlighted
 A first dynamic decision model for the SPM optimal
control has been investigated
 A lot of research efforts can be applied to the SPM in the
future: modelling/simulation of the whole SPM,
centralized/decentralized control schemes, algorithm for
reduction of complexity, multi-objective decision
problems, etc.
Possible impact of the SPM on the Savona City
Challenges for the control
subsystem
 Dynamic decision models for real time optimal control that take
into account multiple objectives (economic, environmental,
technical)
 Dynamic decision models that take into account the switching
possibilities (i.e., on/off of the different operation modes of
generators, storages, electrical system, demands) of the overall system.
This challenge can be exported to the building context
 Complexity reduction
 Decision problems for microgrids based on hierarchical,
distributed, multilevel architectures, taking into account the
different information flows
 Models and methods for wider smart grids or for networked
microgrids
 Models for resources, prices, demands estimation/forecasting
 Risk Assessment, reliability, fault diagnosis
References
R. Zamora, A.K. Srivastava, “Controls for microgrids with storage: Review, challenges, and research needs.” Renewable and Sustainable Energy
Reviews, Vol. 14, pp. 2009-2018, 2010.
W.A. Lidula, A.D. Rajapakse, “Microgrids research: A review of experimental microgrids and test systems.” Renewable and Sustainable Energy
Reviews, Vol. 15 pp. 186-202, 2011.
L.R. Phillips, “The microgrid as a system of systems”, Systems of Systems Engineering – Principles and Applications, Taylor & Francis
Publishers, London, UK, 2008.
T.K. Brekken, , A. Yokochi, A. von Jouanne, Z.Yen, H.M. Hapke, D.A. Halamay, “Optimal Energy Storage Sizing and Control for Wind Power
Applications”, IEEE Transactions on Sustainable Energy, Vol. 2, pp. 69-77, 2011.
Delfino, F., Denegri, G.B., Invernizzi, M., Amann, G, Bessède, J.L., Luxa, A., Monizza, G. A Methodology to Quantify the Impact of a Renewed
T&D Infrastructure on EU 2020 Goals. IEEE Power and Energy Society General Meeting, 2010.