Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060
The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical...
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MDPI AG
2025-07-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/7/408 |
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| author | Joshua Veli Tampubolon Rinaldy Dalimi Budi Sudiarto |
| author_facet | Joshua Veli Tampubolon Rinaldy Dalimi Budi Sudiarto |
| author_sort | Joshua Veli Tampubolon |
| collection | DOAJ |
| description | The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. Relative to a business-as-usual baseline, our approach improves balance scores by 64% and projects up to a 59% reduction in charging load by 2060. These results demonstrate the promise of data-driven demand-management strategies for maintaining grid reliability during large-scale EV integration. |
| format | Article |
| id | doaj-art-3fcc96eca1d04cf186987d74c06b45b8 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-3fcc96eca1d04cf186987d74c06b45b82025-08-20T03:56:47ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-07-0116740810.3390/wevj16070408Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060Joshua Veli Tampubolon0Rinaldy Dalimi1Budi Sudiarto2Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Jawa Barat, IndonesiaDepartment of Electrical Engineering, Universitas Indonesia, Depok 16424, Jawa Barat, IndonesiaDepartment of Electrical Engineering, Universitas Indonesia, Depok 16424, Jawa Barat, IndonesiaThe rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. Relative to a business-as-usual baseline, our approach improves balance scores by 64% and projects up to a 59% reduction in charging load by 2060. These results demonstrate the promise of data-driven demand-management strategies for maintaining grid reliability during large-scale EV integration.https://www.mdpi.com/2032-6653/16/7/408machine learningEV charging demandsupply demand balancegrid simulationJamali power gridinitial charging time |
| spellingShingle | Joshua Veli Tampubolon Rinaldy Dalimi Budi Sudiarto Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 World Electric Vehicle Journal machine learning EV charging demand supply demand balance grid simulation Jamali power grid initial charging time |
| title | Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 |
| title_full | Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 |
| title_fullStr | Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 |
| title_full_unstemmed | Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 |
| title_short | Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 |
| title_sort | dynamic machine learning based simulation for preemptive supply demand balancing amid ev charging growth in the jamali grid 2025 2060 |
| topic | machine learning EV charging demand supply demand balance grid simulation Jamali power grid initial charging time |
| url | https://www.mdpi.com/2032-6653/16/7/408 |
| work_keys_str_mv | AT joshuavelitampubolon dynamicmachinelearningbasedsimulationforpreemptivesupplydemandbalancingamidevcharginggrowthinthejamaligrid20252060 AT rinaldydalimi dynamicmachinelearningbasedsimulationforpreemptivesupplydemandbalancingamidevcharginggrowthinthejamaligrid20252060 AT budisudiarto dynamicmachinelearningbasedsimulationforpreemptivesupplydemandbalancingamidevcharginggrowthinthejamaligrid20252060 |