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Article

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

1
Faculty of Science and Engineering, Swansea University, Swansea SA1 8EN, UK
2
Materials and Manufacturing Academy, Swansea University, Swansea SA1 8EN, UK
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Author to whom correspondence should be addressed.
Academic Editors: William Holderbaum and Stephen Haben
Energies 2021, 14(12), 3453; https://doi.org/10.3390/en14123453
Received: 12 May 2021 / Revised: 2 June 2021 / Accepted: 5 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Forecasting and Management Systems for Smart Grid Applications)
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. View Full-Text
Keywords: short-term electrical load forecasting; distribution systems; photovoltaic power generation; constrained optimisation under uncertainty; battery energy storage system; machine learning short-term electrical load forecasting; distribution systems; photovoltaic power generation; constrained optimisation under uncertainty; battery energy storage system; machine learning
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MDPI and ACS Style

Borghini, E.; Giannetti, C.; Flynn, J.; Todeschini, G. Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation. Energies 2021, 14, 3453. https://doi.org/10.3390/en14123453

AMA Style

Borghini E, Giannetti C, Flynn J, Todeschini G. Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation. Energies. 2021; 14(12):3453. https://doi.org/10.3390/en14123453

Chicago/Turabian Style

Borghini, Eugenio; Giannetti, Cinzia; Flynn, James; Todeschini, Grazia. 2021. "Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation" Energies 14, no. 12: 3453. https://doi.org/10.3390/en14123453

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