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

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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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