Cooperative Bidding Strategy Of Pumped Storage Power Station And New Energy In Power Market

In September 2020,General Secretary Xi Jinping presented the concept of "green recovery" for the world economy on behalf of the Chinese government at the 75th United Nations General Assembly,and promised to achieve the "30.60" target,that is,China will reach its peak carbon emissions by 2030,and Strive to achieve carbon neutrality by 2060.At the same time,pumped storage is an important part of building a new power system with new energy as the main body,and it plays an important role in improving the utilization of new energy and serving carbon peaks and carbon neutrality.The "Opinions of the National Development and Reform Commission on Further Improving the Price Formation Mechanism of Pumped Storage" issued by the National Development and Reform Commission in May 2021 proposed that the price of electricity should be established in a competitive manner and gradually promote the entry of pumped storage power stations into the market.Therefore,with the advancement of my country’s power system reform and the gradual establishment and improvement of the power spot market,new energy represented by wind power and photovoltaics,and energy storage represented by pumped storage power plants,which account for a rapid increase in my country’s energy structure,Participating in power market competition and gaining income through market-oriented channels are the general trend.Therefore,this article first studies and analyzes the market feasibility and technical and economic feasibility of the pumped storage power station and the new energy power station represented by wind and solar to participate in the market bidding.Considering that limited by energy resource conditions,wind and light resources are unstable,with random,intermittent and anti-peak shaving characteristics,and the electricity market,especially the electric energy spot market,has high requirements for the stable output of power generation manufacturers.The volatility of the output of new energy power stations will produce more market deviation assessments,leading to weaker market competitiveness.At the same time,it is discussed that reasonable supporting energy storage facilities are considered to be the most important way to solve the instability of new energy power generation,which can achieve peak-shaving and valley-filling,which is urgently needed in the operation of the current power system.Furthermore,the feasibility of joint bidding for pumped storage power station and new energy power station is studied and analyzed,and the basic mode and composition structure of the joint operation system of pumped storage power station and new energy power station are introduced.Under the conditions of the electric energy market,the "pumped storage-new energy"joint bidding model established in this paper not only considers the stability of new energy output,but also from the perspective of new energy power stations,comprehensively considering the volatility and low margin Cost characteristics,with the goal of maximizing profit,based on the Q-Learning reinforcement learning algorithm,with the help of Pycharm software as the operating platform,a single bidding model for new energy power plants has been established.At the same time,from the perspective of the pumped storage power station,the traditional optimization method is improved,so that the original nonlinear discrete programming can be transformed from the original complex discrete nonlinear problem through two iterations through the introduction of loss coefficients.Converted to an approximate linear mixed integer programming problem,combined with the simulated power market,a "three-stage" bidding model for pumped storage power stations to participate in the electric energy spot market is constructed,which improves the accuracy and reduces the difficulty of calculation.Finally,the new energy power market bidding model and the pumped storage power market bidding model are organically combined to establish a two-tier consortium bidding model.And comprehensively considering other power market entities,a simulated electric energy spot market including thermal power,nuclear power,wind power,photovoltaics,and pumped storage has been established.Based on the Q-Learning reinforcement learning algorithm,the capacity allocation that maximizes the profit of the bidding consortium is obtained.Plan,and the feasibility of profit distribution within the consortium.The research content of this article has important reference significance for suppressing fluctuations and avoiding market deviation assessment when actual new energy power stations participate in power market bidding,and for pumped storage power stations and new energy to formulate power market bidding strategies scientifically and reasonably.At the same time,it has explored the way to promote research on improving the consumption of new energy through energy storage facilities.