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• Renewables

4% by capacity7% by Unit productionl.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 Rules

Conventional Generation Smoothing

Optimised energy schedule

Current SOC

Current SOC Target SOC

Current SOC

0.020.040.060.080.0

100.0120.0140.0160.0

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

Dem

and

for H

eat (

MW

)

Time

0.0

50.0

100.0

150.0

200.0

250.0

300.0

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

Dem

and

for H

eat (

MW

)

Time

-1.5

-1

-0.5

0

0.5

1

1.5

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

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

20

25

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

Pow

er (M

W)

Fixed Demand

0

5

10

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

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

0

5

10

15

20

25

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

Pow

er (M

W)

Current Scheduled Demand Minimum Conventional Generation

0

5

10

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

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

0

5

10

15

20

25

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

Pow

er (M

W)

Final Demand DDSM Demand Fixed Demand

0

5

10

15

20

25

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

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

LiveBattery 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