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Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation

Eugenio Borghini, Cinzia Giannetti Orcid Logo, James Flynn, Grazia Todeschini

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

Swansea University Authors: Eugenio Borghini, Cinzia Giannetti Orcid Logo, 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
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URI: https://cronfa.swan.ac.uk/Record/cronfa57063
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spelling 2022-01-04T17:30:33.5696721 v2 57063 2021-06-08 Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation f4f3adbe64cb98a2d80004d570ad786c Eugenio Borghini Eugenio Borghini true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 90788c8b9c1334834ba9cc37403ea471 James Flynn James Flynn true false c4ff9050b31bdec0e560b19bfb3b56d3 Grazia Todeschini Grazia Todeschini true false 2021-06-08 MECH 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. Journal Article Energies 14 12 3453 MDPI AG 1996-1073 short-term electrical load forecasting; distribution systems; photovoltaic power generation; constrained optimisation under uncertainty; battery energy storage system; machine learning 10 6 2021 2021-06-10 10.3390/en14123453 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) UK Engineering and Physical Sciences Research Council (EPSRC) EP/S001387/1; EP/T013206/1; EP/L015099/1 2022-01-04T17:30:33.5696721 2021-06-08T15:31:30.6594269 College of Engineering Engineering Eugenio Borghini 1 Cinzia Giannetti 0000-0003-0339-5872 2 James Flynn 3 Grazia Todeschini 4 57063__20322__e4b1e326eccd452b84bd38fece177ccf.pdf 57063.pdf 2021-07-02T10:01:42.3904188 Output 4341167 application/pdf Version of Record true ©2021 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng http://creativecommons.org/licenses/by/4.0/
title Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
spellingShingle Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
Eugenio Borghini
Cinzia Giannetti
James Flynn
Grazia Todeschini
title_short Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
title_full Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
title_fullStr Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
title_full_unstemmed Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
title_sort Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
author_id_str_mv f4f3adbe64cb98a2d80004d570ad786c
a8d947a38cb58a8d2dfe6f50cb7eb1c6
90788c8b9c1334834ba9cc37403ea471
c4ff9050b31bdec0e560b19bfb3b56d3
author_id_fullname_str_mv f4f3adbe64cb98a2d80004d570ad786c_***_Eugenio Borghini
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti
90788c8b9c1334834ba9cc37403ea471_***_James Flynn
c4ff9050b31bdec0e560b19bfb3b56d3_***_Grazia Todeschini
author Eugenio Borghini
Cinzia Giannetti
James Flynn
Grazia Todeschini
author2 Eugenio Borghini
Cinzia Giannetti
James Flynn
Grazia Todeschini
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container_title Energies
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container_issue 12
container_start_page 3453
publishDate 2021
institution Swansea University
issn 1996-1073
doi_str_mv 10.3390/en14123453
publisher MDPI AG
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hierarchy_top_title College of Engineering
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hierarchy_parent_title College of Engineering
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description 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.
published_date 2021-06-10T04:13:00Z
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