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

Shanli Wang, Bing Fang, Jiayi Zhang, Zewei Chen, Mingzhe Wen, Huanxiu Xiao and Mengyao Jiang ()
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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

Energies, 2025, vol. 18, issue 13, 1-24

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.

Keywords: CEEMDAN-LSTM; output forecast; EV charging station site selection; optimizing the carbon trading pricing mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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