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Project FALCON Sanna Atherton Jenny Woodruff Ben Godfrey.

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Presentation on theme: "Project FALCON Sanna Atherton Jenny Woodruff Ben Godfrey."— Presentation transcript:

1 Project FALCON Sanna Atherton Jenny Woodruff Ben Godfrey

2 11kV Network Challenges

3 Inform long term investment decisions

4 Alleviate network constraints T1 – Dynamic Asset Rating T2 – Auto Load Transfer T3 – Meshed Network T4 – Energy Storage T5 – Distributed Generation T6 – Demand Side Management Engineering Commercial

5 Select the best technique Carbon Implementation Speed Cost Network Performance Network losses Network losses

6 Telecoms blueprint for the future

7 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

8 Share what we learn

9 Phased Delivery Mobilise Design Build Implement Trials Consolidate & Share Partner Contracts Agreed SIM Blueprint Consultation SIM Built New Load Scenarios Created Final Report Produced Trials Data Analysed

10 Scenario Investment Model(SIM)

11 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

12 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

13 How will it work? Assessment time horizon Now Time

14 Optimisation Assessment time horizon Now Time

15 SIM components Simulation 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

16 Load estimation

17 Load Data FeaturePastFuture “Worst” scenario WinterCould be winter, summer max, summer min or any time. Planning aimDesign 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 years Typical days (season, day type) Monitoring requirements Monitoring at primary substation. View of power flows throughout the circuit to support dynamic techniques. Plus predictions

18 Half Hourly Load Estimates present day Estimation Method Settlement data Energy model Estimation Method Settlement data Energy model Network Measurements Quality Metrics & Analysis How well can we estimate loads today? Can we substitute estimates for monitoring equipment? Fully monitored CostUncertainty Fully Estimated Optimum

19 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.

20 Energy Model Wider range of customer types (Dwelling type & age, heating system, occupancy, demographics ) Customer Propensity Differential uptake of new technologies. Models different types of electricity usage (Heating, lighting, appliances, etc.) Calculate impact of new technologies / changed efficiencies on load profile

21 Future Energy Profiles & Scenarios Customer type A (present day) Customer type A (2020) Changes reflecting Scenario

22 Engineering Intervention Techniques

23 Dynamic Asset Rating 33kV Underground Cables 33/11kV Transformers 11kV Underground Cables 11kV Overhead Conductors 11k/415V Transformers Real Time Ampacity Calculation to Control Temperature Models Cyclic Overload Ratings

24 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

25 Automatic Load Transfer 33kV 11kV 33kV 11kV

26 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

27 Meshed Networks 33kV 11kV 33kV 11kV

28 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

29 Energy Storage + - Network Management System 33kV 11kV

30 Technique 4 Outcomes Impacts Carbon offsetting through storage systems Physical sizing of storage assets on the network Reduction in I 2 R 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

31 Commercial Intervention Techniques

32 What Services could we use? Event related An unplanned event has occurred which results in a network issue immediately, or in the next few hours. Seasonal Short lived network issues occur when the network is in its normal state. Issues are regular and predictable. Demand Reduce demand Generation Increase or reduce generation reducing activity time-shift load switch to own generation

33 Post Event Demand Side Response Primary substation HV Feeder

34 Challenges Location

35 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

36 Project FALCON Any questions?


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