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Electric vehicles, load response, and renewable energy synergy: A new stochastic model for innovation strategies in green energy systems

Author

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  • Yang, Chengying
  • Zhao, Yao
  • Li, Xuetao
  • Zhou, Xiao

Abstract

This paper presents a novel stochastic model for optimizing the integration of electric vehicles (EVs) in load response programs within smart grids. The model addresses uncertainties in renewable energy generation, load demand, EV behavior, and grid reliability, focusing on time-based (e.g., time-of-use (TOU)) and incentive-based (e.g., regulation services) response strategies. By enabling intelligent scheduling of EV charging and discharging, the model enhances grid stability and facilitates the efficient use of renewable energy resources. The proposed model is designed to optimize system operation under different conditions, accounting for various levels of EV penetration, parking behaviors, and risk factors associated with energy generation and grid infrastructure. It provides a robust framework for managing grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services, which reduces operational costs while balancing the supply-demand equation. The model considers various scenarios, including residential, commercial, and industrial areas, with differing levels of EV adoption and risk exposure. The proposed modified artificial flora optimization (MAFO) algorithm is also presented as a solution for complex optimization challenges. Simulation results highlight its effectiveness in optimizing G2V and V2G services. For example, the model achieves a 52 % reduction in peak loads, a 48 % reduction in total operational costs, and up to 49 % cost savings for EV aggregators participating in load response programs. It also shows a significant improvement in load balancing, reducing mismatches between supply and demand by 35 %. Furthermore, the stochastic model efficiently manages reserves, leading to a 15 % increase in the use of renewable energy resources, while maintaining a risk threshold below 1 % for load-supply mismatches.

Suggested Citation

  • Yang, Chengying & Zhao, Yao & Li, Xuetao & Zhou, Xiao, 2025. "Electric vehicles, load response, and renewable energy synergy: A new stochastic model for innovation strategies in green energy systems," Renewable Energy, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:renene:v:238:y:2025:i:c:s096014812401958x
    DOI: 10.1016/j.renene.2024.121890
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    1. Samuel Borroy Vicente & Gregorio Fernández & Noemi Galan & Andrés Llombart Estopiñán & Matteo Salani & Marco Derboni & Vincenzo Giuffrida & Luis Hernández-Callejo, 2024. "Assessment of the Technical Impacts of Electric Vehicle Penetration in Distribution Networks: A Focus on System Management Strategies Integrating Sustainable Local Energy Communities," Sustainability, MDPI, vol. 16(15), pages 1-21, July.
    2. Mohd Bilal & Pitshou N. Bokoro & Gulshan Sharma & Giovanni Pau, 2024. "A Cost-Effective Energy Management Approach for On-Grid Charging of Plug-in Electric Vehicles Integrated with Hybrid Renewable Energy Sources," Energies, MDPI, vol. 17(16), pages 1-35, August.
    3. shafiei, Mohammad & Ghasemi-Marzbali, Ali, 2023. "Electric vehicle fast charging station design by considering probabilistic model of renewable energy source and demand response," Energy, Elsevier, vol. 267(C).
    4. Song, Aoye & Zhou, Yuekuan, 2023. "A hierarchical control with thermal and electrical synergies on battery cycling ageing and energy flexibility in a multi-energy sharing network," Renewable Energy, Elsevier, vol. 212(C), pages 1020-1037.
    5. Tahir, Mustafa & Hu, Sideng & Khan, Tahir & Zhu, Haoqi, 2024. "Multi-energy station design for future electric vehicles: A synergistic approach starting from scratch," Applied Energy, Elsevier, vol. 372(C).
    6. Laugs, Gideon A.H. & Benders, René M.J. & Moll, Henri C., 2024. "Maximizing self-sufficiency and minimizing grid interaction: Combining electric and molecular energy storage for decentralized balancing of variable renewable energy in local energy systems," Renewable Energy, Elsevier, vol. 229(C).
    7. Mahmoud Ghofrani, 2024. "Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids," Sustainability, MDPI, vol. 16(10), pages 1-15, May.
    8. Lamnatou, Chr. & Chemisana, D. & Cristofari, C., 2022. "Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment," Renewable Energy, Elsevier, vol. 185(C), pages 1376-1391.
    9. Gu, Gaofeng & Feng, Tao, 2020. "Heterogeneous choice of home renewable energy equipment conditioning on the choice of electric vehicles," Renewable Energy, Elsevier, vol. 154(C), pages 394-403.
    10. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    11. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    12. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    13. Hassan, Qusay & Khadom, Anees A. & Algburi, Sameer & Al-Jiboory, Ali Khudhair & Sameen, Aws Zuhair & Alkhafaji, Mohamed Ayad & Mahmoud, Haitham A. & Awwad, Emad Mahrous & Mahood, Hameed B. & Kazem, Hu, 2024. "Implications of a smart grid-integrated renewable distributed generation capacity expansion strategy: The case of Iraq," Renewable Energy, Elsevier, vol. 221(C).
    14. Álvarez-Arroyo, César & Vergine, Salvatore & de la Nieta, Agustín Sánchez & Alvarado-Barrios, Lázaro & D’Amico, Guglielmo, 2024. "Optimising microgrid energy management: Leveraging flexible storage systems and full integration of renewable energy sources," Renewable Energy, Elsevier, vol. 229(C).
    15. Luo, Jianing & Yuan, Yanping & Joybari, Mahmood Mastani & Cao, Xiaoling, 2024. "Development of a prediction-based scheduling control strategy with V2B mode for PV-building-EV integrated systems," Renewable Energy, Elsevier, vol. 224(C).
    Full references (including those not matched with items on IDEAS)

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