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Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging

Author

Listed:
  • Hu, Likun
  • Cao, Yi
  • Yin, Linfei

Abstract

As the infiltration rate of renewable energy generation (REG) is increasing, the power balance of power systems needs to be maintained urgently. Electric vehicle (EV) as a widely distributed demand-side flexibility resource can be utilized to address the supply-demand balance of the power system. Current EV charging management strategies do not consider user-side demand flexibility and cannot accurately guide users to charge. To address the problem that EV charging demand cannot be accurately directed, this study proposes a fractional-order long-term price guidance mechanism (FLPGM) for EV charging with a bidirectional prediction based on an attention mechanism (AM). The FLPGM combines an AM with a bidirectional gated recurrent unit (BiGRU) and a long-short term memory (LSTM) and applies a high-precision fractional-order stochastic dynamic differentiation method that considers the effects of community health index, EV types, commuting distance, holidays, and power status on charging demand. FLPGM can equate customer charging demand with REG and reduce the cost of charging to customers, resulting in a balance between supply and demand in a long-term electricity market environment. In this study, FLPGM is applied to an electric vehicle charging model (EVCM) for experiments. The test results show that: the charging load of EVs under the FLPGM matches the energy supply by 98.85%; the EVCM under the FLPGM has a charging cost of only 67.61% of the cost in the natural state and can save 79.93% of the potential savings; the EVCM under the FLPGM maintains the balance between supply and demand while reducing charging costs for users, maximizing the benefits for all parties.

Suggested Citation

  • Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging," Energy, Elsevier, vol. 293(C).
  • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224004110
    DOI: 10.1016/j.energy.2024.130639
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    References listed on IDEAS

    as
    1. Yin, WanJun & Qin, Xuan, 2022. "Cooperative optimization strategy for large-scale electric vehicle charging and discharging," Energy, Elsevier, vol. 258(C).
    2. Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
    3. Zhang, Chao & Yin, Wanjun & Wen, Tao, 2024. "An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid," Energy, Elsevier, vol. 287(C).
    4. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    5. Oosthuizen, Anna Maria & Inglesi-Lotz, Roula & Thopil, George Alex, 2022. "The relationship between renewable energy and retail electricity prices: Panel evidence from OECD countries," Energy, Elsevier, vol. 238(PB).
    6. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    7. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    8. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
    9. Kaur, Amrit Pal & Singh, Mukesh, 2023. "Time-of-Use tariff rates estimation for optimal demand-side management using electric vehicles," Energy, Elsevier, vol. 273(C).
    10. Lv, Zhihan & Wang, Nana & Lou, Ranran & Tian, Yajun & Guizani, Mohsen, 2023. "Towards carbon Neutrality: Prediction of wave energy based on improved GRU in Maritime transportation," Applied Energy, Elsevier, vol. 331(C).
    11. Singh, Bharat & Kumar, Ashwani, 2023. "Optimal energy management and feasibility analysis of hybrid renewable energy sources with BESS and impact of electric vehicle load with demand response program," Energy, Elsevier, vol. 278(PA).
    12. Yin, Linfei & Qiu, Yao, 2022. "Long-term price guidance mechanism of flexible energy service providers based on stochastic differential methods," Energy, Elsevier, vol. 238(PB).
    13. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    14. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    15. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    16. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
    17. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    18. Dehghan, Hamed & Amin-Naseri, Mohammad Reza, 2022. "A simulation-based optimization model to determine optimal electricity prices under various scenarios considering stakeholders’ objectives," Energy, Elsevier, vol. 238(PC).
    19. Yin, Linfei & Qiu, Yao, 2022. "Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers," Energy, Elsevier, vol. 255(C).
    20. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    21. Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
    22. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    23. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    24. Welzel, Fynn & Klinck, Carl-Friedrich & Pohlmann, Yannick & Bednarczyk, Mats, 2021. "Grid and user-optimized planning of charging processes of an electric vehicle fleet using a quantitative optimization model," Applied Energy, Elsevier, vol. 290(C).
    25. Zhu, Yansong & Liu, Jizhen & Hu, Yong & Xie, Yan & Zeng, Deliang & Li, Ruilian, 2024. "Distributionally robust optimization model considering deep peak shaving and uncertainty of renewable energy," Energy, Elsevier, vol. 288(C).
    26. Shao, Zhen & Yang, Yudie & Zheng, Qingru & Zhou, Kaile & Liu, Chen & Yang, Shanlin, 2022. "A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis," Applied Energy, Elsevier, vol. 327(C).
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