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A data-driven approach for optimal distribution network operation

The UK’s current power network cannot meet the energy demand of an influx of EVs. The coupling will be aided by the development of vehicle-to-grid charging and Internet of Things platforms, which will enable the smart management of assets to facilitate a low carbon network. However, as these shifts in demand and supply happen, it will become increasingly difficult for already congested areas of the grid to balance the added pressures of widespread EV charging. A solution is to build large-scale energy storage infrastructures to provide energy for the rapid charging demand. The battery cost has plummeted in the past decade due to technological advancement. By providing a buffer to ease the transmission of electricity along congested lines, large-scale battery storage can enable active management of distribution networks to avoid grid overloads and allow smoother transitions to decarbonise both the energy and transport sectors.

This project develops an innovative toolkit to enable active management of distribution networks to maximise the distribution network’s voltage stability and minimise EV charging cost with large-scale battery storage. The purpose is to address the growing demand of fast charging stations for EVs. To account for the uncertainties introduced in the distribution networks with intermittent renewables and charging behaviour, a multi-agent reinforcement learning approach is adopted to identify the optimal operation for the distribution networks.By 2040, it is estimated that there will be 500 million EVs globally and there will be 36 million EVs in the UK. These EVs are radicalising the way citizens transfer, use and interact with energy and will have a great impact on local and national energy networks. There is a pressing need to identify and utilise the relevant energy storage technologies to mitigate wide-scale blackout due to power supply and demand imbalance.

Project duration: 1st February 2021- 30th July 2021

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Related Research Group(s)

Brunel Interdisciplinary Power Systems

Brunel Interdisciplinary Power Systems - Power systems analysis for transmission and distribution networks, smart grids; congestion monitoring in transmission networks; simulation and analysis of new energy markets; optimisation of the design and operation of electrical networks; condition monitoring of power station and power system plant; energy-efficient designs for underground electric power cables.

Project last modified 07/12/2020