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LV Networks modelling via Natural Selection of Residential Energy Profiles.

Dr Stephen Haben PDRA Mathematical Institute University of Oxford Jan 2016. LV Networks modelling via Natural Selection of Residential Energy Profiles. Dr Georgios Giasemidis CountingLab Ltd. University of Reading. Overview. Thames Valley Vision project. Problem description.

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LV Networks modelling via Natural Selection of Residential Energy Profiles.

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  1. Dr Stephen Haben PDRA Mathematical Institute University of Oxford Jan 2016 LV Networks modelling via Natural Selection of Residential Energy Profiles. Dr Georgios Giasemidis CountingLab Ltd. University of Reading

  2. Overview • Thames Valley Vision project. • Problem description. • Method and justification: Genetic Algorithm. • Results and Conclusions.

  3. Thames Valley Vision Project • £30M Low Carbon Network Fund (LCNF) project led by Scottish and Southern Energy Power Distribution. • Prepare distribution network operators (DNOs) for a future low carbon future: larger more volatile demands and high penetrations of distributed generation.

  4. Thames Valley Vision: Project Partners • Automated demand side response. • Modelling low carbon scenarios. • Providing network modelling environment. • Customer engagement. • Hardware, e.g. batteries.

  5. Academic Partners • Short term forecasting for storage device control • Agent based modelling of forecast models • Customer segmentation • Probabilistic forecasts (GEFCOM 2014). • LV Network modelling (this talk!)

  6. Background: Low Voltage NETWOrKs

  7. LV Distribution Substations • Consist three to six feeders. • Each feeder has three phases. • Each feeder connects 10 to 100 customers (domestic/commercial mix). • New technology poses new threats to voltage/load thresholds etc.

  8. Challenge: How to manage/model LV networks? • Smart meter data may not be available, or could be expensive. • Substation monitoring possible (DNO owned) but expensive. • Standard profiles too “smooth” or unrepresentative. • Not clear links between household demand and socio-demographics (e.g. MOSAIC classes).

  9. lv nETWOrK Modelling via natural selection approach

  10. Aim of modelling To create a realistic, (and accurate) “high-resolution” model of a low voltage network using limited monitoring and publically available (or easily accessed) information.

  11. Available data • Quarterly meter readings for all consumers (residential and commercial). • Electrical demand profiles for about 300 monitored households at half-hourly resolution. • Electrical demand profiles of the substation phases at the half-hourly resolution • Connection information of customers on substation feeders. • Publically available data of households, e.g. MOSAIC group.

  12. Genetic Algorithm method • “Buddy” several sets (genomes) of monitored households to unmonitored households. • Evaluate fitness of such sets according to a fitness function. • Crossover and mutate these sets to produce next generation of buddies. • Repeat.

  13. Fitness Function

  14. Remarks • Have to optimize with respect to weighting, number of weeks. • Weighting can be viewed in two ways • Trust in the quality of the data. • Bias in individual household level to phase level • Selection of genomes restricted to households from similar groups (e.g. profile class and MOSAIC) to reduce computation. • Currently evaluate the buddying according to a yearly test set at the phase level.

  15. Results/Examples

  16. The Data • Considering 544 Phases (418 domestic). • Quarterly meter readings not as reliable as the substation monitoring. • Sparse monitoring of households on the monitored feeders. Hence full evaluation not possible. • Connectivity at the phase level poorly understood. • Commercial customers use standard profiles, GA chooses scaling. • Landlord lighting and BT boxes modelled with fixed profiles.

  17. Errors for buddy expanded over a years worth of data. Phase Level profile Relative Errors Customer Level daily demand Relative Errors Optimal w=0 Optimal w=1

  18. Examples weight = 0.1

  19. Commercial Example, Feeder level. (w=0).

  20. Effect of magnitude of SS phase daily demand at phase level (w=0, weeks=8).

  21. Effect of number of connected customers at phase level (w=0, weeks=8)

  22. Effect of number of connected customers at feeder level.

  23. Concluding Remarks • Still lots more research to do: Commercial customers, seasonal effects, simulated feeders, … • Presented a versatile method for modelling LV network at individual level with realistic profiles using limited monitoring. • Accuracy linked to number of customers/size of demand on feeder. • Could lead to inform management decisions on grid: • Which substations need monitoring? • What is the effect of various LCT uptake scenarios? • Other solutions? Demand response? Storage devices?

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