NINES Project
Learning to date
Stewart Reid – SSEPD
Graham Ault – University of Strathclyde
John Reyner – Airwave solutions
NINES Overview
2
• No Mainland connection
Single DC link £500M
• Demand
Max. 50MW-Min. 14MW
• Renewables
4% by capacity
7% by Unit production
l.f. ~50%
• Population
~22,000
-2-
NINES System Overview
New Large Wind
Existing Generation
New Small Wind
Lerwick Power
Station
LIC
SVT Power
Station
Burradale
Windfarm
LIC
Active Network Management System
LIC
LIC
LIC
Thermal Store
DDSM
1MW Battery
NINES Update
New Large Wind
Existing Generation
New Small Wind
Lerwick Power
Station
LIC
SVT Power
Station
Burradale
Windfarm
LIC
Active Network Management System
LIC
LIC
LIC
Thermal Store
DDSM
1 MW Battery
University of Strathclyde
Modelling the Shetland
Power System
Shetland System Modelling: Overview
Strategic Models
Economic
and
commercial
model
Allocation of
costs and
benefits.
Scheduling
services
enduring
commercial
arrangements
Customer
demand
forecast
model
System
development
optimisation
model
Evaluated
system
development
options
Operating
schedule and
cost for given
system
configuration.
Estimate of energy
demands for
operational period
Operational Models
Unit
scheduling
model
Transient stability
envelope for
system operation
Strategic
and
operational
risk model
Operational
risks
Dynamic
system
model
Shetland System Modelling: Outcomes
• Operational Models
– Customer Demand: Quantification of flexible heat demand and thermal
energy storage for domestic customers
– Power System Dynamics: Envelope of stable/secure system operation
– Unit Scheduling: Estimate of renewable energy access and role of
flexible demand and energy storage
• Strategic Models
– Economic and Commercial: Private costs and benefits of Shetland
repowering options and commercial arrangements concepts
– Strategic Risk: Extensive mapping of Shetland low carbon smart grid
risks and repowering investment decision tree
– System Development: identification of future system development
options and optimisation model specification
Control Philosophy for the Active Network
Management (ANM) Scheme
Scheduling
Engine
Works ahead of real
time based on
forecasts and
current system state
Real Time
Application of
Schedule
Applies schedules to
flexible demand and
battery storage
Automatic RealTime Monitoring
and Control
Manages generation setpoint within constraints.
Monitors energy delivery
to flexible demand and
monitors forecast error.
Control Centre
Manual
Intervention
Power system
operators able to
intervene in response
to system conditions.
Model Inputs to Operational System
Homes with
Heaters/Tank
Monitored
parameters
Control
Instructions
Local Interface
Controllers
Load/storage
state
Controls and
Schedules
Domestic DSM
‘Element Manager’
Aggregate
zone/group
energy demand
data
Demand sampling
requirements
Controls and
Schedules
ANM System
Resource
status and
forecasts
Control Room /
EMS / DMS
Consumer
classification
Aggregation
and scaling
methods
Customer Demand
Model
Energy
forecast
Unit Scheduling
Model
Required
frequency
response
Schedule
block
sizes
System stability
constraints/rules
Scheduling
constraints/rules
System Dynamic
Model
System stability
constraints/rules
Shetland System Dynamic Simulation:
Transient frequency limits
2% underfrequency
limit
• Dynamic models of all system components in NINES:
– Frequency responsive demand, thermal and renewable generation,
energy storage
• Identification of allowable/stable/secure system states through
simulation
System constraints on wind generation access
• Identification of allowed ‘envelope’ for wind generation operation (forms
input to scheduling model and operations)
• Modification of ‘envelope’ dependent on de-risking NINES innovative
solutions
Unit Scheduling Model: Overview
Demand and
wind
forecasts
Stability
Rules
Network
Rules
Conventional
Generation
Smoothing
Optimised
energy
schedule
• Model configuration and setup:
– Demand Model input: customer constraints
– Dynamic Model input: stability/security constraints
– System model, objectives and flexible demand and energy storage
parameters
• Uses Optimal Power Flow with linkage between time periods across
scheduling horizon (e.g. 24 hours):
– Applies constraints in priority order to generate schedule of energy flows
to/from connected devices
– Maximisation of low carbon generation
Unit Scheduling Model: Energy Storage
Domestic Space Heating: input from demand model
300.0
Demand for Heat (MW)
250.0
200.0
Current SOC
150.0
Target SOC
100.0
50.0
0.0
00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00
Time
Domestic Hot Water: input from demand model
160.0
Demand for Heat (MW)
140.0
120.0
Current SOC
100.0
80.0
60.0
40.0
Target SOC
20.0
0.0
00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00
Time
Battery Storage: flexible within scheduling process
1.5
1
?
0.5
Current SOC
0
00:00
-0.5
-1
-1.5
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
00:00
Target SOC
Scheduling Example: Stability Rules
Fixed Demand
25
Power (MW)
20
15
10
5
0
00:00
02:00
04:00
06:00
08:00
10:00
Wind
14:00
16:00
18:00
20:00
22:00
00:00
16:00
18:00
20:00
22:00
00:00
Scheduled DDSM
Power (MW)
10
12:00
5
0
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
• Starting with fixed component of demand and wind power
forecast: schedule flexible demand (DDSM) within
stability/security constraints
• Domestic flexible heat demand scheduled into period of low
fixed demand and high wind power output
Scheduling Example: Network Rules
Minimum Conventional Generation
Current Scheduled Demand
25
Power (MW)
20
15
10
5
0
00:00
02:00
04:00
06:00
08:00
10:00
12:00
Scheduled
16:00
18:00
20:00
22:00
00:00
Curtailed
Power (MW)
10
14:00
5
0
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
• With interim stage schedule: apply network constraint
rules to achieve ‘network constrained schedule’
• Domestic heat demand rescheduled into periods when
wind power would otherwise be constrained
16:00
18:00
20:00
22:00
00:00
Scheduling Example: Final Schedule and
Actual Outcome
Final Demand
DDSM Demand
Fixed Demand
25
Power (MW)
20
15
10
5
0
00:00
02:00
04:00
06:00
Actual Wind
08:00
10:00
12:00
Actual Conventional
14:00
Forecast Wind
16:00
18:00
20:00
22:00
00:00
22:00
00:00
Forecast Conventional
25
20
15
10
5
0
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
• Final schedule is subject to forecast error in delivery so
‘optimal’ schedule must be adjusted in real time
• Acceptable deviations to conventional generation schedule
16:00
18:00
20:00
NINES
Making the Connection
Ross Macindoe
Head of Future Networks
Airwave
Making the connection
Power
Sources
Homes
Advanced
Energy
Storage
ANM
• Inter-system Gateway
• Devices group management
• Aggregated data processing
and feedback
• Fast group-based comms
• Integrated LIC and
Communications
Wider Long Term Benefits
DDSM
Outage
Management
Social Alarming
Distributed
Generation
Airwave
SmartWorld
Telemonitoring
Fault Monitoring
Security and
Alarming
REAL PROGRESS = REAL LEARNING •THE KIT
•THE PEOPLE
•THE BUSINESS CASE
ANM
system
Live
Battery
installed
6 home
trial
complete
Customers
validated
benefits of
Quantum
Heaters
Design for the
customer not just
for our “smart”
aspirations
NINES
informing
solutions
elsewhere
Detailed modelling
and 6 homes
confirming initial
expected benefits
Comms
contract
DSM/Storage
portfolio
management
is essential
THANK YOU