project falcon

Project FALCONSanna Atherton Jenny WoodruffBen Godfrey

11kV Network Challenges

Inform long term investment decisions

Alleviate network constraints

T1 – Dynamic Asset RatingT2 – Auto Load TransferT3 – Meshed NetworkT4 – Energy StorageT5 – Distributed GenerationT6 – Demand Side Management

Engineering

Commercial

Select the best technique

Carbon

Implementation Speed

Cost

Network Performance

Network losses

Telecoms blueprint for the future

• Are the current profiles sufficient?• Do we need more sophisticated customer

profiles ?• To find out:

– Model different levels of uptake of low carbon technology

– Build customer profiles from types of use– Create a larger set of customer types

• SIM visualises expected constraints

Develop future load scenarios

Share what we learnwww.westernpowerinnovation.co.uk

Phased Delivery

Mobilise Design Build Implement Trials

Consolidate & Share

2011 2012 2013 2014 2015

Partner Contracts Agreed

SIM Blueprint Consultation SIM Built

New Load Scenarios Created

Final Report Produced

Trials Data Analysed

Scenario Investment Model(SIM)

What does it do?

• Network analysis for a Scenario encompassing many years.

• Applies possible techniques to constraints

• Assess solutions against multiple criteria (cost, practicality, CIs CMLs etc.)

• Analysis & Visualisation of results

Use of SIM

• Guidelines on alternatives to reinforcement• Best options for this type of problem?• In which conditions is this solution suitable?

Falcon

After Falcon

• To support long term network planning e.g. for capital program / price control.

• 11kV Network planning tool

• Evaluate other solutions than used in Falcon

How will it work?

Assessment time horizon

Now Time

Optimisation

Assessment time horizon

Now Time

SIM componentsSimulation Harness

Manage simulation branching

Network Modelling Tool

Identify constraints

Model techniques

Network edits

Calculate CML/CI , losses

Network visualisation

Load data

Network data

Economic module

Optimisation / prioritisation

Results store

Data mining tool

Visualisation

Load estimation

Load DataFeature Past Future“Worst” scenario

Winter Could be winter, summer max, summer min or any time.

Planning aim Design to avoid constraints

Understand duration and nature of constraints , may manage with dynamic techniques.

Planning data requirements

Winter maximum for average cold spell

Evaluate half hourly over many yearsTypical days (season, day type)

Monitoring requirements

Monitoring at primary substation.

View of power flows throughout the circuit to support dynamic techniques.

Plus predictions

Half Hourly Load Estimates present day Estimation MethodSettlement dataEnergy model

Network Measurements

Quality Metrics & Analysis

How well can we estimate loads today?Can we substitute estimates for monitoring equipment?

Fully monitored

Cost

Uncertainty

FullyEstimated

Optimum

Load Estimation – Industry Data• Based on the process used for settlement • Half Hourly estimates for non half hourly metered customers• Uses Estimated Annual Consumption + Profile coefficients for 8

different customer types.

Add in Half hourly metered load, unmetered supplies, losses.

Does this give us a good estimate? If so then use past data for similar day for real time estimation.

But not so good for predicting load in 20 years time.

Energy Model• Wider range of customer types

(Dwelling type & age, heating system, occupancy, demographics )

• Customer PropensityDifferential uptake of new technologies.

• Models different types of electricity usage (Heating, lighting, appliances, etc.)

• Calculate impact of new technologies / changed efficiencies on load profile

Future Energy Profiles & Scenarios

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Always on ( Fridge, freezer, security system, mains wired fire alarms etc.)

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Computers

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Cooking appliances

Lighting

Heating system

Always on ( Fridge, freezer, security system, mains wired fire alarms etc.)

Customer type A (present day) Customer type A (2020)

Changes reflecting Scenario

Engineering Intervention Techniques

Dynamic Asset Rating

33kV Undergroun

d Cables

33/11kV Transformer

s

11kV Undergroun

d Cables

11kV Overhead

Conductors

11k/415V Transformer

s

Real Time Ampacity Calculation to Control TemperatureModels

Cyclic Overload Ratings

Technique 1 Outcomes

Impacts•Capacity of assets increased•Change in Planning Standards•Increased capital costs•Potential for greater losses•Enhanced visibility of asset operation

Learning Objectives

•Comparing implementations•Development of thermal models•Thermal inertia of asset types•Modular installation across an existing network

Operational•Integration with existing Control•Understanding of reliability of predictions•Active intervention prior to thermal excursions•Pre and post fault running arrangements

Automatic Load Transfer

33kV 11kV

33kV 11kV

Technique 2 Outcomes

Impacts•Increase in utilisation factor•Effects on switchgear duty•Increased capital costs•Reduction of ampere-miles travelled and reduced losses•Risk of Mal-operations

Learning Objectives

•Understand variability of feeder loads•Dealing with automated control routines•Using customer load profile to determine connection strategy•Best placement of automated equipment

Operational•Optimisation of network for different running arrangements•Pro-actively anticipating load demands•Better management of large loads near multiple small customers

Meshed Networks

33kV 11kV

33kV 11kV

Technique 3 Outcomes

Impacts•Enhance power quality•Increase in customer security•Increased capital costs•Further complexity of circuits•Fast, reliable and error- free communications needed

Learning Objectives

•How to retrofit meshing on an existing network•Using new protection techniques across a communications network•Required grading times for IP based protection on the 11kV

Operational

•Integration with existing protection•Fault level management requirements•Post-fault isolation and re-energisation routines•Changes to standard switchgear specifications

Energy Storage

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Network ManagementSystem

33kV

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Technique 4 Outcomes

Impacts•Carbon offsetting through storage systems•Physical sizing of storage assets on the network•Reduction in I2R losses•Increase in storage losses•Lifespan of battery chemistries

Learning Objectives

•Optimum charge/discharge windows•Using distribution assets for ancillary grid services•Multiple set collaboration across an HV feeder•Best placement of storage on the system

Operational•Using power electronic devices to address power quality issues•Lifespan of battery versus running operation•Protection requirements•Integration with control environment

Commercial Intervention Techniques

What Services could we use?

Event relatedAn unplanned event has occurred which results in a network issue immediately, or in the next few hours.

SeasonalShort lived network issues occur when the network is in its normal state. Issues are regular and predictable.

DemandReduce demand

Generation

Increase or reduce generation

• reducing activity• time-shift load• switch to own generation

Post Event Demand Side

Response

Primary substationHV

Fee

der

Challenges

LocationLocationLocation

Customers willing and able to respond?

Commercial frameworks?

Practicalities of implementation?

Reliability?

• How much load is flexible• Can customers see benefits• How much financial reward• How should reward be structured

• Use of Aggregators• Common template

• Communicating requirements• Measuring response

• Realistic models for use in SIM

Learning

Project FALCONAny questions?