Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management
The rapid expansion of Electric Vehicle (EV) charging capacities, driven by the surge in transportation electrification, has a direct impact on the electric power system, particularly the Low Voltage (LV) grid. Consequently, there is a pressing need to redesign energy management systems, placing a g...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10463027/ |
| _version_ | 1797243363309649920 |
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| author | Lazher Mejdi Faten Kardous Khaled Grayaa |
| author_facet | Lazher Mejdi Faten Kardous Khaled Grayaa |
| author_sort | Lazher Mejdi |
| collection | DOAJ |
| description | The rapid expansion of Electric Vehicle (EV) charging capacities, driven by the surge in transportation electrification, has a direct impact on the electric power system, particularly the Low Voltage (LV) grid. Consequently, there is a pressing need to redesign energy management systems, placing a greater emphasis on mitigating the effects of EV charging. In this article, we crafted an online grid-level Model Predictive Control (MPC) incorporating Machine Learning (ML) prediction enhanced with Singular Spectrum Analysis (SSA). ML models predicts the future EV charging demands, which are the key factors for the MPC for managing efficiently a large-scale charging infrastructure, aiming to mitigate EV charging impacts, specifically phase unbalance and overloading. The MPC results, especially on phase unbalance metrics, mirrored in a predictive manner those of offline/no forecast optimization from our previous work. A week-long MPC simulation comparison with uncontrolled case showcased a 63% reduction in peak demand duration, a 68% decrease in Phasing Unbalance Index (PUI), and a 45% reduction in Voltage Unbalance Factor (VUF). The minimum voltage unbalance improved from 0.897 pu to 0.913 pu, reducing out-of-standard drops duration by 21%. Lastly, we observed a 20% cut in lines’ losses, equating to a 50 kgCO<sub>2</sub> reduction within a week. |
| first_indexed | 2024-04-24T18:53:55Z |
| format | Article |
| id | doaj.art-56486613968746b2bf3e16400c2e852e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:53:55Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-56486613968746b2bf3e16400c2e852e2024-03-26T17:45:18ZengIEEEIEEE Access2169-35362024-01-0112368963690710.1109/ACCESS.2024.337489710463027Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure ManagementLazher Mejdi0https://orcid.org/0000-0003-0554-3015Faten Kardous1https://orcid.org/0000-0002-6739-3561Khaled Grayaa2https://orcid.org/0000-0003-0334-0687LR16ES08 Research Laboratory of Smart Grids and Nanotechnology (LaRINa), National School of Advanced Sciences and Technologies of Borj Cedria (ENSTAB), University of Carthage, Carthage, TunisiaLR16ES08 Research Laboratory of Smart Grids and Nanotechnology (LaRINa), National School of Advanced Sciences and Technologies of Borj Cedria (ENSTAB), University of Carthage, Carthage, TunisiaLR16ES08 Research Laboratory of Smart Grids and Nanotechnology (LaRINa), National School of Advanced Sciences and Technologies of Borj Cedria (ENSTAB), University of Carthage, Carthage, TunisiaThe rapid expansion of Electric Vehicle (EV) charging capacities, driven by the surge in transportation electrification, has a direct impact on the electric power system, particularly the Low Voltage (LV) grid. Consequently, there is a pressing need to redesign energy management systems, placing a greater emphasis on mitigating the effects of EV charging. In this article, we crafted an online grid-level Model Predictive Control (MPC) incorporating Machine Learning (ML) prediction enhanced with Singular Spectrum Analysis (SSA). ML models predicts the future EV charging demands, which are the key factors for the MPC for managing efficiently a large-scale charging infrastructure, aiming to mitigate EV charging impacts, specifically phase unbalance and overloading. The MPC results, especially on phase unbalance metrics, mirrored in a predictive manner those of offline/no forecast optimization from our previous work. A week-long MPC simulation comparison with uncontrolled case showcased a 63% reduction in peak demand duration, a 68% decrease in Phasing Unbalance Index (PUI), and a 45% reduction in Voltage Unbalance Factor (VUF). The minimum voltage unbalance improved from 0.897 pu to 0.913 pu, reducing out-of-standard drops duration by 21%. Lastly, we observed a 20% cut in lines’ losses, equating to a 50 kgCO<sub>2</sub> reduction within a week.https://ieeexplore.ieee.org/document/10463027/Energy managementEVG3PCXload forecastingLV gridML |
| spellingShingle | Lazher Mejdi Faten Kardous Khaled Grayaa Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management IEEE Access Energy management EV G3PCX load forecasting LV grid ML |
| title | Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management |
| title_full | Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management |
| title_fullStr | Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management |
| title_full_unstemmed | Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management |
| title_short | Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management |
| title_sort | machine learning based online mpc for large scale charging infrastructure management |
| topic | Energy management EV G3PCX load forecasting LV grid ML |
| url | https://ieeexplore.ieee.org/document/10463027/ |
| work_keys_str_mv | AT lazhermejdi machinelearningbasedonlinempcforlargescalecharginginfrastructuremanagement AT fatenkardous machinelearningbasedonlinempcforlargescalecharginginfrastructuremanagement AT khaledgrayaa machinelearningbasedonlinempcforlargescalecharginginfrastructuremanagement |