Research on EV ownership and load forecasting methods based on improved Gray Markov models
With the rapid large-scale adoption of electric vehicles (EVs) and the ongoing expansion of charging infrastructure, power system stability is increasingly. To address the limitations in current EV ownership forecasting accuracy and outdated load prediction methodologies, this study proposes an enha...
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| Vydáno v: | International journal of electrical power & energy systems Ročník 173; s. 111323 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.12.2025
Elsevier |
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| ISSN: | 0142-0615 |
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| Abstract | With the rapid large-scale adoption of electric vehicles (EVs) and the ongoing expansion of charging infrastructure, power system stability is increasingly. To address the limitations in current EV ownership forecasting accuracy and outdated load prediction methodologies, this study proposes an enhanced grey Markov model for EV ownership prediction, incorporating an improved Grey Wolf Optimization (GWO) algorithm, integrated with a Monte Carlo-based approach for EV load forecasting. First, to better capture the underlying patterns and long-term growth trends in ownership data, a grey model (GM(1,1)) is employed for initial forecasting, followed by residual correction using a Markov chain model to improve prediction accuracy. Second, to resolve the issue of suboptimal background value selection in the conventional grey model, an improved GWO algorithm is applied to optimize key model parameters. Subsequently, probabilistic and statistical models are utilized to simulate critical EV battery parameters and user driving behaviors, with distributional goodness-of-fit tests and sensitivity analyses conducted to ensure robustness and reliability of input data for load forecasting. Finally, the Monte Carlo simulation is implemented to generate probabilistic load profiles for the period 2024–2026. The proposed framework is validated using real-world EV ownership and charging load data from Wuhan City. Case study results demonstrate that the proposed model achieves high predictive accuracy and practical applicability, yielding a minimum Mean Absolute Percentage Error (MAPE) of 2.4036%. Furthermore, the daily load curve peaks consistently at 8:00 p.m., aligning with typical evening charging behavior.
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•Combine GWO, GM(1,1), and Markov models to enhance stability with limited ownership data.•The improved GWO better optimizes GM(1,1) background values for enhanced prediction.•Comparative study validates the proposed hybrid model’s effectiveness•EV results by type, considering travel habits & ownership forecasts. |
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| AbstractList | With the rapid large-scale adoption of electric vehicles (EVs) and the ongoing expansion of charging infrastructure, power system stability is increasingly. To address the limitations in current EV ownership forecasting accuracy and outdated load prediction methodologies, this study proposes an enhanced grey Markov model for EV ownership prediction, incorporating an improved Grey Wolf Optimization (GWO) algorithm, integrated with a Monte Carlo-based approach for EV load forecasting. First, to better capture the underlying patterns and long-term growth trends in ownership data, a grey model (GM(1,1)) is employed for initial forecasting, followed by residual correction using a Markov chain model to improve prediction accuracy. Second, to resolve the issue of suboptimal background value selection in the conventional grey model, an improved GWO algorithm is applied to optimize key model parameters. Subsequently, probabilistic and statistical models are utilized to simulate critical EV battery parameters and user driving behaviors, with distributional goodness-of-fit tests and sensitivity analyses conducted to ensure robustness and reliability of input data for load forecasting. Finally, the Monte Carlo simulation is implemented to generate probabilistic load profiles for the period 2024–2026. The proposed framework is validated using real-world EV ownership and charging load data from Wuhan City. Case study results demonstrate that the proposed model achieves high predictive accuracy and practical applicability, yielding a minimum Mean Absolute Percentage Error (MAPE) of 2.4036%. Furthermore, the daily load curve peaks consistently at 8:00 p.m., aligning with typical evening charging behavior.
[Display omitted]
•Combine GWO, GM(1,1), and Markov models to enhance stability with limited ownership data.•The improved GWO better optimizes GM(1,1) background values for enhanced prediction.•Comparative study validates the proposed hybrid model’s effectiveness•EV results by type, considering travel habits & ownership forecasts. With the rapid large-scale adoption of electric vehicles (EVs) and the ongoing expansion of charging infrastructure, power system stability is increasingly. To address the limitations in current EV ownership forecasting accuracy and outdated load prediction methodologies, this study proposes an enhanced grey Markov model for EV ownership prediction, incorporating an improved Grey Wolf Optimization (GWO) algorithm, integrated with a Monte Carlo-based approach for EV load forecasting. First, to better capture the underlying patterns and long-term growth trends in ownership data, a grey model (GM(1,1)) is employed for initial forecasting, followed by residual correction using a Markov chain model to improve prediction accuracy. Second, to resolve the issue of suboptimal background value selection in the conventional grey model, an improved GWO algorithm is applied to optimize key model parameters. Subsequently, probabilistic and statistical models are utilized to simulate critical EV battery parameters and user driving behaviors, with distributional goodness-of-fit tests and sensitivity analyses conducted to ensure robustness and reliability of input data for load forecasting. Finally, the Monte Carlo simulation is implemented to generate probabilistic load profiles for the period 2024–2026. The proposed framework is validated using real-world EV ownership and charging load data from Wuhan City. Case study results demonstrate that the proposed model achieves high predictive accuracy and practical applicability, yielding a minimum Mean Absolute Percentage Error (MAPE) of 2.4036%. Furthermore, the daily load curve peaks consistently at 8:00 p.m., aligning with typical evening charging behavior. |
| ArticleNumber | 111323 |
| Author | Sheng, Wenxin Wang, Mingyu Li, Hanting Tang, Minan Zhang, Kaiyue |
| Author_xml | – sequence: 1 givenname: Minan orcidid: 0000-0003-1424-2708 surname: Tang fullname: Tang, Minan email: tangminan@mail.lzjtu.cn organization: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China – sequence: 2 givenname: Wenxin orcidid: 0009-0003-6351-7074 surname: Sheng fullname: Sheng, Wenxin email: 12231647@stu.lzjtu.edu.cn organization: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China – sequence: 3 givenname: Kaiyue orcidid: 0000-0001-7235-6282 surname: Zhang fullname: Zhang, Kaiyue email: chzhan3.k@edu.spbstu.ru organization: Institute of Energy, Peter the Great St.Petersburg Polytechnic University, 859111, Russia – sequence: 4 givenname: Hanting orcidid: 0009-0006-6601-2639 surname: Li fullname: Li, Hanting email: 13230061@stu.lzjtu.edu.cn organization: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China – sequence: 5 givenname: Mingyu orcidid: 0009-0002-9177-9509 surname: Wang fullname: Wang, Mingyu email: 12232230@stu.lzjtu.edu.cn organization: School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China |
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| Keywords | Electric vehicle Grey Markov model Gray wolf optimization algorithm Load forecasting Retention projections |
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