Stewart Reid – SSEPDGraham Ault – University of StrathclydeJohn Reyner – Airwave solutions
NINES ProjectLearning to date
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NINES Overview• No Mainland connection
Single DC link £500M
• Demand Max. 50MW-Min. 14MW
• Renewables4% by capacity
7% by Unit production
l.f. ~50%
• Population~22,000
NINES System Overview
LIC
New Small Wind
LIC
Lerwick Power Station
SVT Power Station
Burradale Windfarm
LIC
Existing GenerationNew Large Wind
DDSM
1MW BatteryThermal Store
LICLIC
Active Network Management System
NINES Update
LIC
New Small Wind
LIC
Lerwick Power Station
SVT Power Station
Burradale Windfarm
LIC
Existing GenerationNew Large Wind
DDSM
1 MW BatteryThermal Store
LICLIC
Active Network Management System
Modelling the Shetland Power System
University of Strathclyde
Customer demand forecast model
Unit scheduling
model
Economic and
commercial model
Strategic and
operational risk model
System development optimisation
model
Estimate of energy demands for
operational period
Transient stability envelope for
system operation
Operational Models
Evaluated system
development options
Strategic Models
Allocation of costs and benefits.
Operating schedule and cost for given
system configuration.
Operational risks
Dynamic system model
Scheduling services enduring
commercial arrangements
Shetland System Modelling: Overview
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
ScheduleApplies schedules to flexible demand and
battery storage
Automatic Real-Time Monitoring
and ControlManages generation set-point within constraints. Monitors energy delivery to flexible demand and monitors forecast error.
Control Centre Manual
InterventionPower system
operators able to intervene in response to system conditions.
Resource status and forecasts
Local Interface Controllers
Homes with Heaters/Tank
Domestic DSM ‘Element Manager’
ANM System
Customer Demand Model
System Dynamic Model
Unit Scheduling Model
Aggregate zone/group energy demand data
Controls and Schedules
Controls and Schedules
Demand sampling requirements
Energy forecast
Load/storage state
Schedule block sizes
Consumer classification
Aggregation and scaling methods
System stability constraints/rules
Required frequency response
Scheduling constraints/rules
System stability constraints/rules
Control Room / EMS / DMS
Control Instructions
Monitored parameters
Model Inputs to Operational System
Shetland System Dynamic Simulation: Transient frequency limits
2% under-frequency 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
• 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
Demand and wind forecasts
Stability Rules Network RulesConventional Generation Smoothing
Optimised energy schedule
Current SOC
Current SOC Target SOC
Current SOC
0.0
20.0
40.0
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160.0
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Dem
and
for
Hea
t (M
W)
Time
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50.0
100.0
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Dem
and
for
Hea
t (M
W)
Time
-1.5
-1
-0.5
0
0.5
1
1.5
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Domestic Space Heating: input from demand model
Domestic Hot Water: input from demand model
Battery Storage: flexible within scheduling process
Unit Scheduling Model: Energy Storage
Target SOC
Target SOC
?
0
5
10
15
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25
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Pow
er (M
W)
Fixed Demand
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Pow
er (M
W)
Wind Scheduled DDSM
Scheduling Example: Stability Rules
• 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
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5
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Pow
er (M
W)
Current Scheduled Demand Minimum Conventional Generation
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Pow
er (M
W)
Scheduled Curtailed
Scheduling Example: Network Rules
• 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
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5
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25
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Pow
er (M
W)
Final Demand DDSM Demand Fixed Demand
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25
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Actual Wind Actual Conventional Forecast Wind Forecast Conventional
Scheduling Example: Final Schedule and Actual Outcome
• Final schedule is subject to forecast error in delivery so ‘optimal’ schedule must be adjusted in real time
• Acceptable deviations to conventional generation schedule
Ross MacindoeHead of Future Networks Airwave
NINESMaking the Connection
Airwave SmartWorld
Making the connection
Secure Resilient Communications
Network
Integrated Hub
Element Manager
PowerSources
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
AirwaveSmartWorld Fault Monitoring
DDSM
Distributed Generation
Telemonitoring
Social Alarming
Security and Alarming
OutageManagement
REAL PROGRESS = REAL LEARNING
ANM system
Live
Battery installed
6 home trial
complete
Comms contract
Customers validated
benefits of Quantum Heaters
• THE KIT• THE PEOPLE• THE BUSINESS CASE
Design for the customer not just
for our “smart” aspirations
DSM/Storage portfolio
management is essential
Detailed modelling and 6 homes
confirming initial expected benefits
NINES informing solutions
elsewhere
THANK YOU