Optimal Operation of Virtual Power Plants Based on Stackelberg Game Theory

Weishi Zhang, Chuan He, Haichao Wang, Hanhan Qian, Zhemin Lin and Hui Qi ()
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Weishi Zhang: Anhui Power Exchange Center Co., Ltd., Hefei 230022, China
Chuan He: Anhui Power Exchange Center Co., Ltd., Hefei 230022, China
Haichao Wang: Anhui Power Exchange Center Co., Ltd., Hefei 230022, China
Hanhan Qian: Anhui Power Exchange Center Co., Ltd., Hefei 230022, China
Zhemin Lin: Anhui Power Exchange Center Co., Ltd., Hefei 230022, China
Hui Qi: Anhui Power Exchange Center Co., Ltd., Hefei 230022, China

Energies, 2024, vol. 17, issue 15, 1-15

Abstract: As the scale of units within virtual power plants (VPPs) continues to expand, establishing an effective operational game model for these internal units has become a pressing issue for enhancing management and operations. This paper integrates photovoltaic generation, wind power, energy storage, and constant-temperature responsive loads, and it also considers micro gas turbines as auxiliary units, collectively forming a typical VPP case study. An operational optimization model was developed for the VPP control center and the micro gas turbines, and the game relationship between them was analyzed. A Stackelberg game model between the VPP control center and the micro gas turbines was proposed. Lastly, an improved D3QN (Dueling Double Deep Q-network) algorithm was employed to compute the VPP’s optimal operational strategy based on Stackelberg game theory. The results demonstrate that the proposed model can balance the energy complementarity between the VPP control center and the micro gas turbines, thereby enhancing the overall economic efficiency of operations.

Keywords: virtual power plant; Stackelberg game; deep reinforcement learning; operation optimization (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: 2024
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