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Joint Planning of Renewable Energy and Electric Vehicle Charging Stations Based on a Carbon Pricing Optimization Mechanism

Author

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  • Shanli Wang

    (Hainan Power Grid Co., Ltd., Haikou 570203, China)

  • Bing Fang

    (Hainan Power Grid Co., Ltd., Haikou 570203, China)

  • Jiayi Zhang

    (Hainan Power Grid Co., Ltd., Haikou 570203, China)

  • Zewei Chen

    (Hainan Power Grid Co., Ltd., Haikou 570203, China)

  • Mingzhe Wen

    (Hainan Power Grid Co., Ltd., Haikou 570203, China)

  • Huanxiu Xiao

    (Hainan Power Grid Co., Ltd., Haikou 570203, China)

  • Mengyao Jiang

    (Institute of New Energy, Wuhan 430206, China)

Abstract

The integration of renewable energy and electric vehicle (EV) charging stations into distribution systems presents critical challenges, including the inherent variability of renewable generation, the complex behavioral patterns of EV users, and the need for effective carbon emission mitigation. To address these challenges, this paper proposes a novel distribution system planning method based on the carbon pricing optimization mechanism. First, to address the strong randomness and volatility of renewable energy, a prediction model for renewable energy output considering climatic conditions is established to characterize the output features of wind and solar power. Subsequently, a charging station model is constructed based on the behavioral characteristics of electric vehicle users. Then, an optimized carbon trading price mechanism incorporating the carbon price growth rate is introduced into the carbon emission cost accounting. Based on this, a joint planning model for the power and transportation systems is developed, aiming to minimize the total economic cost while accounting for renewable energy integration and electric vehicle charging station deployment. In the case study, the proposed model is validated using the actual operational data of a specific region and a modified IEEE 33-node system, demonstrating the rationality and effectiveness of the model.

Suggested Citation

  • Shanli Wang & Bing Fang & Jiayi Zhang & Zewei Chen & Mingzhe Wen & Huanxiu Xiao & Mengyao Jiang, 2025. "Joint Planning of Renewable Energy and Electric Vehicle Charging Stations Based on a Carbon Pricing Optimization Mechanism," Energies, MDPI, vol. 18(13), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3462-:d:1692282
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    References listed on IDEAS

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    1. Despoina Kothona & Aggelos S. Bouhouras, 2022. "A Two-Stage EV Charging Planning and Network Reconfiguration Methodology towards Power Loss Minimization in Low and Medium Voltage Distribution Networks," Energies, MDPI, vol. 15(10), pages 1-17, May.
    2. Soyoung Park & Solyoung Jung & Jaegul Lee & Jin Hur, 2023. "A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms," Energies, MDPI, vol. 16(3), pages 1-12, January.
    3. Qunli Wu & Huaxing Lin, 2019. "Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    4. Shiqian Wang & Bo Liu & Qiuyan Li & Ding Han & Jianshu Zhou & Yue Xiang, 2025. "EV Charging Behavior Analysis and Load Prediction via Order Data of Charging Stations," Sustainability, MDPI, vol. 17(5), pages 1-16, February.
    5. Xuejun Li & Jiaxin Qian & Changhai Yang & Boyang Chen & Xiang Wang & Zongnan Jiang, 2024. "New Power System Planning and Evolution Path with Multi-Flexibility Resource Coordination," Energies, MDPI, vol. 17(1), pages 1-20, January.
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