Research On Electricity Purchase And Sale Strategies Of Electricity Sellers Considering User Demand Response In The Electricity Market

Since China’s power system began to deepen reforms in recent years,a large number of e-commerce vendors have sprung up across the country.As a key member of the electricity market,how to choose the optimal percentage of the total electricity purchased in each electricity trading market and how to choose the electricity sales strategy that maximizes the benefits of the electricity retailer? E-commerce sellers’ focus in making decisions on purchasing and selling electricity.However,as the reform continues to deepen in the future,the uncertainty of the transaction price in the spot market will also increase.How can e-commerce retailers predict the spot price more accurately to make the best strategy for supply and sale of electricity?.In addition,in a market-oriented environment,in order to seek lower electricity consumption costs,users will optimize their electricity consumption by responding to demand response plans issued by retail e-commerce companies.When researching the electricity supply and sales strategy of retail e-commerce companies,this paper takes user demand response,user selection behavior,spot electricity price uncertainty,and distributed power uncertainty as the primary research issues.The primary research content includes the following points:First,consider the user’s demand response capability under real-time electricity prices,establish a load regulation capability model,and formulate user demand response optimization methods under real-time electricity prices.Secondly,considering the real-time electricity selling price in the electricity spot trading market,the characteristics of user load and the uncertainty of electricity price forecasting,the electricity price forecasting model based on GA optimized rbf layers is selected.In order to improve the prediction accuracy,the cloud model similarity calculation is used to filter the sample data.The case study shows that the real data of the PJM power market is used to predict the spot electricity price,which confirms that the model proposed in this paper can improve the prediction accuracy compared with the general model.Next,taking into account the impact of user demand response and user electricity consumption behavior in the electricity retailer ’ s decision to purchase and sell electricity,this article assumes that the retailer adopts a real-time electricity price load adjustment capability model,and introduces a market share model to describe the user’s power sales.The independent choice of the retailer,the introduction of the regret matching mechanism describes the user’s participation rate in selecting the demand response strategy published by the retailer;thus,a two-layer optimization model of the retailer ’ s power supply and sales strategy that considers the user’s demand response and user behavior is established.Selecting the US PJM market data to analyze the calculation examples,the results show that considering user behavior can effectively improve the ability of the retailer to control the entire power supply and sales strategy,which is closer to practical applications,and has certain practical application value.