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Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation / Eugenio Borghini, Cinzia Giannetti, James Flynn, Grazia Todeschini

Energies, Volume: 14, Issue: 12, Start page: 3453

Swansea University Authors: Eugenio Borghini, Cinzia Giannetti, James Flynn, Grazia Todeschini

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DOI (Published version): 10.3390/en14123453

Abstract

The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative t...

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Published in: Energies
ISSN: 1996-1073
Published: MDPI AG 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57063
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Abstract: The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed.
Keywords: short-term electrical load forecasting; distribution systems; photovoltaic power generation; constrained optimisation under uncertainty; battery energy storage system; machine learning
College: College of Engineering
Funders: EPSRC
Issue: 12
Start Page: 3453