Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

Yajing Gao,Xiaojie Zhou,Fushen Xue,Jiafeng Ren,Zheng Zhao

doi:10.3390/en11051063

Abstract

The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users’ historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on “meteorological similarity day (MSD)” to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm- back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer’s bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.

Highlights

Making a comprehensive survey of the market-oriented electricity reforms of various countries— the national conditions and the paths of reform are all different—opening up the electricity-side market, and giving users the right of free choice have always been core factors in electricity market reform
Aiming at the optimal bidding strategy of the power retailer which was researched in this paper, paper, we obtain the following conclusions through modelling, solution, and analysis: we obtain the following conclusions through modelling, solution, and analysis: In order to guide the retailer to make purchase of electricity better, this paper introduced the
It can be seen that the wavelet neural network (WNN) prediction model based on meteorological similarity day proposed in this paper can improve the prediction accuracy to a the meteorological similarity day proposed in this paper can improve the prediction accuracy to a certain extent

Summary

Introduction

Making a comprehensive survey of the market-oriented electricity reforms of various countries— the national conditions and the paths of reform are all different—opening up the electricity-side market, and giving users the right of free choice have always been core factors in electricity market reform. The gradual opening up of the retail-side market is mainly by introducing the competition through releasing users’ options, allowing users to freely choose to trade with power-retailing or power-generating enterprises, or participating in the wholesale market directly [1,2]. EU countries in 2007 completed the reform of the full liberalization of user options. The USA has achieved retail competition in some states, including its residents. Japan has postponed the retail competition reform that allowing residents to choose their own options. After the earthquake, they formulated a plan for a new power reform and implemented a full-scale retail competition after

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