Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/5/1063 |
| _version_ | 1798034460473556992 |
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| author | Yajing Gao Xiaojie Zhou Jiafeng Ren Zheng Zhao Fushen Xue |
| author_facet | Yajing Gao Xiaojie Zhou Jiafeng Ren Zheng Zhao Fushen Xue |
| author_sort | Yajing Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:43:41Z |
| format | Article |
| id | doaj.art-9cd1c32bba434dca858e66e04ce4cc35 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T20:43:41Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9cd1c32bba434dca858e66e04ce4cc352022-12-22T04:04:06ZengMDPI AGEnergies1996-10732018-04-01115106310.3390/en11051063en11051063Electricity Purchase Optimization Decision Based on Data Mining and Bayesian GameYajing Gao0Xiaojie Zhou1Jiafeng Ren2Zheng Zhao3Fushen Xue4State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, ChinaDepartment of automation, North China Electric Power University, Baoding 071003, ChinaState Grid Jiangsu Electric Power Co., Ltd., Suzhou Power Supply Branch, Suzhou 215004, ChinaThe 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.http://www.mdpi.com/1996-1073/11/5/1063power retailerload forecastingfuzzy clusteringprice forecastingBayesian game |
| spellingShingle | Yajing Gao Xiaojie Zhou Jiafeng Ren Zheng Zhao Fushen Xue Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game Energies power retailer load forecasting fuzzy clustering price forecasting Bayesian game |
| title | Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game |
| title_full | Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game |
| title_fullStr | Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game |
| title_full_unstemmed | Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game |
| title_short | Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game |
| title_sort | electricity purchase optimization decision based on data mining and bayesian game |
| topic | power retailer load forecasting fuzzy clustering price forecasting Bayesian game |
| url | http://www.mdpi.com/1996-1073/11/5/1063 |
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