Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/12/3453 |
| _version_ | 1797530556286631936 |
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| author | Eugenio Borghini Cinzia Giannetti James Flynn Grazia Todeschini |
| author_facet | Eugenio Borghini Cinzia Giannetti James Flynn Grazia Todeschini |
| author_sort | Eugenio Borghini |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T10:30:42Z |
| format | Article |
| id | doaj.art-d399744a0c3f412191a7db118b682575 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T10:30:42Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d399744a0c3f412191a7db118b6825752023-11-21T23:40:13ZengMDPI AGEnergies1996-10732021-06-011412345310.3390/en14123453Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV GenerationEugenio Borghini0Cinzia Giannetti1James Flynn2Grazia Todeschini3Faculty of Science and Engineering, Swansea University, Swansea SA1 8EN, UKFaculty of Science and Engineering, Swansea University, Swansea SA1 8EN, UKMaterials and Manufacturing Academy, Swansea University, Swansea SA1 8EN, UKFaculty of Science and Engineering, Swansea University, Swansea SA1 8EN, UKThe 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.https://www.mdpi.com/1996-1073/14/12/3453short-term electrical load forecastingdistribution systemsphotovoltaic power generationconstrained optimisation under uncertaintybattery energy storage systemmachine learning |
| spellingShingle | Eugenio Borghini Cinzia Giannetti James Flynn Grazia Todeschini Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation Energies short-term electrical load forecasting distribution systems photovoltaic power generation constrained optimisation under uncertainty battery energy storage system machine learning |
| title | 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_short | 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 |
| topic | short-term electrical load forecasting distribution systems photovoltaic power generation constrained optimisation under uncertainty battery energy storage system machine learning |
| url | https://www.mdpi.com/1996-1073/14/12/3453 |
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