Dynamic pricing and optimal scheduling of multi-virtual power plants based on master-slave game
Under the background of"double carbon",distributed renewable energy and flexible resources such as energy storage and demand response develop rapidly.Virtual power plants integrate distributed resources efficiently through control technology,which improves the power generation efficiency of distributed energy.With the social capital entering the power market,different virtual power plants will belong to different investors,forming a multi-agent game pattern.According to the investment preferences of investors,virtual power plants will be composed of resources with different flexibility.In order to give consideration to the interests of virtual power plant operators and virtual power plants,a two-level master-slave game model between operators and multi-virtual power plants is constructed.Considering the interaction between upper pricing and lower output,the dynamic pricing of operators and the optimal operation and scheduling of virtual power plants are studied.In the lower layer,aiming at the minimum operating cost of each virtual power plant,the optimal scheduling models of multiple virtual power plants including electric energy storage,demand response and hydrogen energy storage are established respectively.The upper layer takes the operator's profit as the goal,and combines the lower layer's output plan to dynamically formulate the purchase and sale price of virtual power plants.Particle Swarm Optimization(PSO)is used to solve the game model iteratively.Through the analysis of an example,the model can give consideration to the interests of multi-agents,effectively improve the operators'income and reduce the operating cost of virtual power plants.
master-slave gamemultiple virtual power plantsmultiple flexible resourcesdynamic pricingoptimized scheduling
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