Research On The Optimization Strategy Of Time-of-use Electricity Price Based On Demand Response

With the construction of China’s smart grid and the gradual deepening of the power system reform,demand-side resources are being re-recognized in the competition of the power market.demand response means that demand-side users make corresponding according to the price signals and incentives are given by the supply side.It is also clear that the core element of electricity sales service is electricity price,and the reasonable formulation of electricity price is of great significance in resource allocation,energy-saving and emission reduction,enterprise development,and residents’ life.The peak-valley time-sharing tariff guides users to actively change the electricity consumption in different periods through the price difference between peak and off-peak periods,to achieve the purpose of cutting peaks and filling valleys.Due to the rapid development of intelligent measurement systems,the response of customers to electricity prices is greatly improved,and the response of different types of customers to electricity prices varies,so the pricing strategy of electricity sales based on accurate modeling of demand-side customer response has become an urgent research issue.The research work done on this topic in this paper is as follows.Firstly,the raw electricity load data collected is pre-processed and normalized to get the typical daily load curve of users;the K-means algorithm is used to cluster users,and all users are divided into 7 categories,the sample size of each category is counted,the category less than half of the maximum number of categories is judged as the minority category,and WGAN-GP is used to expand the data of the minority category to be comparable with the maximum category;the clustering The labels obtained are used as the output of the SVM model to establish a typical daily load curve classification model,and the expanded typical daily load curve data set is classified.Next,the basic theory of price elasticity of electricity demand is introduced and the method of finding the price elasticity coefficient is given.Then,the method of dividing peak,flat,and valley periods are given.The fuzzy affiliation function is used to obtain the peak and valley affiliation according to the calculation,and the peak and valley periods are divided by fuzzy clustering to obtain the peak and valley moment division results.Finally,a game theory approach to optimize the time-sharing tariff strategy is proposed.A cost model for the grid company caused by fluctuations in customer demand and a customer satisfaction model under the difference between nominal demand and actual consumption is proposed.Utility functions are designed for the power company and the customers,and Nash equilibrium is derived using the inverse solution method.The arithmetic example shows that the method can effectively balance customer demand,reduce costs for the power company,and improve customer benefits by setting the optimal time-of-use tariff.The increase in social welfare measures shows that the market efficiency is improved through time-sharing tariffs.