Research On Modeling And Optimal Scheduling Of Virtual Power Plant In Market Environment

With the proposal of carbon peaking and carbon neutrality goals,the development of renewable energy has become even more rapid.The number of distributed renewable energy units connected to the grid is increasing day by day,whose dispersion and randomness bring new challenges to the operation of the power system.By aggregating the distributed resources on the distribution network side,the virtual power plant can become an aggregator with a certain degree of control,which contributes to the safe and stable operation of the power system.With the deepening of China’s electricity market reform,on December 27,2021,the National Development and Reform Commission revised two rules to clarify the status of virtual power plants as market players from the policy level,virtual power plants can also participate in market transactions as equal market players,so it is of great significance to study the modeling and optimal scheduling of virtual power plants in the market environment.At this stage,there is less research on the optimal scheduling of virtual power plants in the market environment in China,and the modeling and strategy requirements for their participation in the market differ according to the resource agency model within the virtual power plant.In addition,distributed trading will be the focus of China’s next market-oriented reform promotion.With the increase of the number of virtual power plants on the distribution network side,P2 P trading among multiple virtual power plants can realize power balancing among virtual power plants,promote energy consumption in close proximity,and realize the improvement of total social welfare,while the domestic research in this field is still in its initial stage.Therefore,this paper focuses on the modeling and optimal scheduling of virtual power plants with different resource agency models in the market environment and the modeling and optimal scheduling of multiple virtual power plants under P2 P trading,aiming to improve the above-mentioned gap.Firstly,the modeling and optimal scheduling of the virtual power plant based on direct resource dispatch in market environment are studied.The optimal scheduling model of such virtual power plant participating in day-ahead spot market and real-time balance market is established.The risk brought by the uncertainty of renewable energy output and market price is measured by using the conditional value-at-risk method,then the optimal scheduling strategy of virtual power plants with different risk preference is studied.Based on Shapley’s value method and stand-alone contribution theory,the contribution of distributed resources in virtual power plant to the overall income and risk is quantitatively analyzed,which provides a basis for the virtual power plant to allocate inside resources.Then,the modeling and optimal scheduling of multi-prosumer virtual power plant in market environment are studied based on Stackelberg game model.The model is established with the virtual power plant operator as the leader and internal prosumers as the followers.The conditional value at risk method is adopted to manage the market risks faced by the virtual power plant and the KKT condition is used to transform the model into a single-level model,which successfully solve the problem.The proposed model can take into account the benefits of both the virtual power plant and prosumers.The model can also provide optimal scheduling strageties of virtual power plant operators with different risk preference.Finally,the modeling and optimal scheduling of multiple virtual power plants considering P2 P transactions are studied.The P2 P transaction model of multiple virtual power plants in the day-ahead stage is established.The uncertainty of renewable energy is treated by using the the distributionally robust optimization theory,then the optimal scheduling problem is extablished.The ambiguity set is constructed based on Wasserstein distance,and the proposed optimization model is transformed into a convex optimization problem according to strong duality theory.A distributed optimization strategy based on ADMM algorithm is proposed to solve the market clearing model,which can protect the privacy of virtual power plants.The proposed model can promote energy consumption in close proximity,enhance the total social welfare,and take into account the conservativeness and economy of the optimal scheduling strategy of the virtual power plant.