The Optimal Energy Management of Virtual Power Plants by Considering Demand Response and Electric Vehicles

Chia-Sheng Tu and Ming-Tang Tsai ()
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Chia-Sheng Tu: School of Marine Mechatronics, Xiamen Ocean Vocational College, Xiamen 361101, China
Ming-Tang Tsai: Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan

Energies, 2025, vol. 18, issue 17, 1-18

Abstract: This paper aims to explore Virtual Power Plants (VPPs) in combination with Demand Response (DR) concepts, integrating solar power generation, Electric Vehicle (EV) charging and discharging, and user loads to establish an optimal energy management scheduling system. Willingness curves for load curtailment are derived based on the consumption patterns of industrial, commercial, and residential users, enabling VPPs to design DR mechanisms under Time-of-Use (TOU), two-stage, and critical peak pricing periods. An energy management model for a VPP is developed by integrating DR, EV charging and discharging, and user loads. To solve this model and optimize economic benefits, this paper proposes an Improved Wolf Pack Search Algorithm (IWPSA). Based on the original Wolf Pack Search Algorithm (WPSA), the Improved Wolf Pack Search Algorithm (IWPSA) enhances the key behaviors of detection and encirclement. By reinforcing the attack strategy, the algorithm achieves better search performance and improved stability. IWPSA provides a parameter optimization mechanism with global search capability, enhancing searching efficiency and increasing the likelihood of finding optimal solutions. It is used to simulate and analyze the maximum profit of the VPP under various scenarios, such as different seasons, incentive prices, and DR periods. The verification analysis in this paper demonstrates that the proposed method can not only assist decision makers in improving the operation and scheduling of VPPs, but also serve as a valuable reference for system architecture planning and more effectively evaluating the performance of VPP operation management.

Keywords: Virtual Power Plant; Demand Response; Electric Vehicles; Wolf Pack Search Algorithm (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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