Internal pricing driven dynamic aggregation of virtual power plant with energy storage systems

He Meng, Hongjie Jia, Tao Xu, Nikos Hatziargyriou, Wei Wei and Rujing Wang

Energy, 2025, vol. 321, issue C

Abstract: Virtual power plant (VPP) has emerged as an energy service platform that can monitor, forecast, schedule and trade heterogeneous distributed flexible resources in an aggregated approach across various locations to enhance the flexibility and economics of power systems. One of the major goals of VPP is to maximize revenue for its resources that belong to different stakeholders. Therefore, it can be considered as a game with multiple players, various pricing strategies and payoffs. To incentivize the participation of distributed energy resources (DERs), including energy storage systems (ESSs), an internal pricing driven dynamic aggregation model of VPP is established based on a Stackelberg game. An improved artificial fish swarm algorithm (AFSA) and a mixed integer quadratic programming (MIQP) method are utilized to find the equilibrium solution of the model and to determine the internal transaction prices, dynamic aggregation and dispatching schemes of VPP. Two operation modes, VPP direct control ESS and photovoltaic (PV)-ESS joint operation, are compared to investigate the aggregation approaches and associated economics.

Keywords: Virtual power plant; Energy storage system; Stackelberg game; Artificial fish swarm algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:321:y:2025:i:c:s0360544225011120

DOI: 10.1016/j.energy.2025.135470

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