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A Multi-Temporal Regulation Strategy for EV Aggregators Enabling Bi-Directional Energy Interactions in Ancillary Service Markets for Sustainable Grid Operation

Author

Listed:
  • Xin Ma

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Yubing Liu

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Chongyi Tian

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Bo Peng

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

Abstract

Amid rising load volatility and uncertainty, demand-side resources with regulation capabilities are increasingly engaged at scale in ancillary service markets, facilitating sustainable peak load mitigation and alleviating grid stress while reducing reliance on carbon-intensive peaking plants. This study examines the integration of electric vehicles (EVs) in peak regulation, proposing a multi-stage operational strategy framework grounded in the analysis of EV power and energy response constraints to promote both economic efficiency and environmental sustainability. The model holistically accounts for temporal charging and discharging behaviors under diverse incentive mechanisms, incorporating user response heterogeneity alongside multi-period market peak regulation demands while supporting clean transportation adoption. An optimization model is formulated to maximize aggregator revenue while enhancing grid sustainability and is solved via MATLAB(2021b) and CPLEX(20.1.0). The simulation outcomes reveal that the discharge-based demand response (DBDR) strategy elevates aggregator revenue by 42.6% and enhances peak regulation margins by 19.2% relative to the conventional charge-based demand response (CBDR). The hybridization of CBDR and DBDR yields a threefold revenue increase and a 28.7% improvement in peak regulation capacity, underscoring the efficacy of a joint-response approach in augmenting economic returns, grid flexibility, and sustainable energy management.

Suggested Citation

  • Xin Ma & Yubing Liu & Chongyi Tian & Bo Peng, 2025. "A Multi-Temporal Regulation Strategy for EV Aggregators Enabling Bi-Directional Energy Interactions in Ancillary Service Markets for Sustainable Grid Operation," Sustainability, MDPI, vol. 17(16), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7315-:d:1723546
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    1. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    3. Liu, Zhi-Feng & Liu, You-Yuan & Jia, Hong-Jie & Jin, Xiao-Long & Liu, Tong-Hui & Wu, Yu-Ze, 2025. "Bi-level energy co-optimization of regional integrated energy system with electric vehicle to generalized-energy conversion framework and flexible hydrogen-blended gas strategy," Applied Energy, Elsevier, vol. 390(C).
    4. Rezaei, Navid & Khazali, Amirhossein & Mazidi, Mohammadreza & Ahmadi, Abdollah, 2020. "Economic energy and reserve management of renewable-based microgrids in the presence of electric vehicle aggregators: A robust optimization approach," Energy, Elsevier, vol. 201(C).
    5. Shafie-khah, M. & Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, P. & Moghaddam, M.P. & Sheikh-El-Eslami, M.K. & Catalão, J.P.S., 2016. "Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability," Applied Energy, Elsevier, vol. 162(C), pages 601-612.
    6. Daneshzand, Farzaneh & Coker, Phil J & Potter, Ben & Smith, Stefan T, 2023. "EV smart charging: How tariff selection influences grid stress and carbon reduction," Applied Energy, Elsevier, vol. 348(C).
    7. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    8. Park, Sung-Won & Cho, Kyu-Sang & Hoefter, Gregor & Son, Sung-Yong, 2022. "Electric vehicle charging management using location-based incentives for reducing renewable energy curtailment considering the distribution system," Applied Energy, Elsevier, vol. 305(C).
    9. Fang, Daohong & Tang, Hao & Hatziargyriou, Nikos & Zhang, Tao & Chen, Wenjuan & Zhang, Qianli, 2024. "Dual-center control scheme and FF-DHRL-based collaborative optimization for charging stations under intra-day peak-shaving demand," Applied Energy, Elsevier, vol. 368(C).
    10. Siow Jat Shern & Md Tanjil Sarker & Mohammed Hussein Saleh Mohammed Haram & Gobbi Ramasamy & Siva Priya Thiagarajah & Fahmid Al Farid, 2024. "Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems," Energies, MDPI, vol. 17(22), pages 1-25, November.
    11. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Robust day-ahead scheduling of smart distribution networks considering demand response programs," Applied Energy, Elsevier, vol. 178(C), pages 929-942.
    12. Zheng, Yanchong & Niu, Songyan & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2019. "Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 424-439.
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