Data-driven model predictive control of community batteries for voltage regulation in power grids subject to EV charging
In this paper, a Model Predictive Control (MPC) for community Battery Energy Storage Systems (BESS) is proposed to mitigate the Electric Vehicle (EV) charging demand while maintaining voltage regulation in residential power grids. With the increased penetration of renewable-based Distributed Energy...
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| Published in | Energy reports Vol. 9; pp. 236 - 244 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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01.05.2023
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| Abstract | In this paper, a Model Predictive Control (MPC) for community Battery Energy Storage Systems (BESS) is proposed to mitigate the Electric Vehicle (EV) charging demand while maintaining voltage regulation in residential power grids. With the increased penetration of renewable-based Distributed Energy Resources (DERs) and the EV uptake, distribution grids are highly subject to voltage fluctuations due to back power feed from DERs or significant EV charging demand. Conventionally, Distribution Network Operators (DNOs) have addressed this issue by adjusting the taps of transformers. However, managing the naturally unpredictable renewable generation and the power consumption behaviour of technology owners requires advanced real-time algorithms. In this paper, we apply a real-time control algorithm, based on Model Predictive Control (MPC), to a community BESS in order to reduce voltage fluctuations. The MPC algorithm performs in real-time based on readings from smart meters of households. It predicts the future values of voltages at households based on the current and past readings of smart meters, and calculates the BESS charging or discharging rate such that future voltage deviations are minimised. Simulations on the real-world power consumption data from Victorian residential consumers, in Australia, show that the proposed controller performs significantly better than the fixed charging/discharging BESS schedule in both voltage regulation and BESS degradation. |
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| AbstractList | In this paper, a Model Predictive Control (MPC) for community Battery Energy Storage Systems (BESS) is proposed to mitigate the Electric Vehicle (EV) charging demand while maintaining voltage regulation in residential power grids. With the increased penetration of renewable-based Distributed Energy Resources (DERs) and the EV uptake, distribution grids are highly subject to voltage fluctuations due to back power feed from DERs or significant EV charging demand. Conventionally, Distribution Network Operators (DNOs) have addressed this issue by adjusting the taps of transformers. However, managing the naturally unpredictable renewable generation and the power consumption behaviour of technology owners requires advanced real-time algorithms. In this paper, we apply a real-time control algorithm, based on Model Predictive Control (MPC), to a community BESS in order to reduce voltage fluctuations. The MPC algorithm performs in real-time based on readings from smart meters of households. It predicts the future values of voltages at households based on the current and past readings of smart meters, and calculates the BESS charging or discharging rate such that future voltage deviations are minimised. Simulations on the real-world power consumption data from Victorian residential consumers, in Australia, show that the proposed controller performs significantly better than the fixed charging/discharging BESS schedule in both voltage regulation and BESS degradation. |
| Author | Sajjadi, Samaneh Sadat Jalili, Mahdi Yu, Xinghuo Moradi Amani, Ali Somaweera, W. Arachchige |
| Author_xml | – sequence: 1 givenname: Ali orcidid: 0000-0001-5158-4307 surname: Moradi Amani fullname: Moradi Amani, Ali email: ali.moradiamani@rmit.edu.au – sequence: 2 givenname: Samaneh Sadat surname: Sajjadi fullname: Sajjadi, Samaneh Sadat – sequence: 3 givenname: W. Arachchige surname: Somaweera fullname: Somaweera, W. Arachchige – sequence: 4 givenname: Mahdi surname: Jalili fullname: Jalili, Mahdi – sequence: 5 givenname: Xinghuo surname: Yu fullname: Yu, Xinghuo |
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| Cites_doi | 10.1109/EEM.2018.8469789 10.1016/j.enpol.2019.03.041 10.1109/TEC.2020.3032534 10.3390/en13226069 10.3390/app12189083 10.1016/j.apenergy.2015.06.061 10.1109/TII.2017.2752464 10.1016/j.ijepes.2019.06.007 10.1016/j.apenergy.2014.08.060 10.1016/j.automatica.2011.11.005 10.1016/j.apenergy.2017.05.016 10.1016/j.apenergy.2018.03.085 10.3390/en13051183 10.1109/TSTE.2016.2608279 10.1109/TPEL.2019.2930182 10.1016/j.apenergy.2017.12.118 10.1016/j.jclepro.2017.02.150 10.1016/j.enconman.2019.112330 10.1109/TAC.2016.2525808 10.1049/ip-gtd:19990476 10.1016/j.renene.2018.06.016 10.1016/j.renene.2021.10.005 10.1109/TIE.2018.2840498 10.1109/ISGT-Europe47291.2020.9248889 10.1109/TII.2014.2299336 10.1109/TSG.2013.2282504 |
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| Keywords | Model predictive control Community battery storage system Electric vehicle charging Residential power grid Voltage regulation |
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| Title | Data-driven model predictive control of community batteries for voltage regulation in power grids subject to EV charging |
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