Optimal Operation of Virtual Power Plants Based on Stackelberg Game Theory

• Published in: Energies (Basel) Vol. 17; no. 15; p. 3612
• Main Authors: Zhang, Weishi, He, Chuan, Wang, Haichao, Qian, Hanhan, Lin, Zhemin, Qi, Hui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Air conditioning; Algorithms; Alternative energy sources; Analysis; Case studies; Deep learning; deep reinforcement learning; Demand side management; Efficiency; Electric power-plants; Electric vehicles; Electricity; Energy management; Energy management systems; Energy storage; Game theory; Gas turbines; Green technology; Industrial efficiency; Natural gas; operation optimization; Optimization; Outdoors; Power plants; Prices; Prices and rates; Scheduling; Stackelberg game; Temperature; Turbines; virtual power plant; Water heaters; Wind power
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17153612

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.