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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Computers
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Cooking appliances
Lighting
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Always on ( Fridge, freezer, security system, mains wired fire alarms etc.)
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Computers
Dishwasher & Laundry
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
11kV
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?
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