Optimal Strategy of Integrated Energy Retail Market Based on VaR Theory

doi:10.12204/j.issn.1000-7229.2021.06.002

Abstract

Abstract:

With the promotion of integrated energy system, the coupling of multiple energies in terms of supply, transmission and use increases, the forms of energy use are gradually diversified, and the connotation of retail market is constantly enriched under the reform of energy market. Under the traditional retail market of single energy, independent supply and pricing method cannot effectively guide users to use energy and improve the economic efficiency. Therefore, it is urgent to study the pricing strategy for retail market of integrated energy system. In this paper, optimal retail pricing method for the comprehensive energy park on the basis of value at risk (VaR) theory is proposed considering multi-energy load, randomness of spot price and demand response. A two-stage model of static retail pricing and day-ahead optimal scheduling is established to study the optimal static retail pricing strategy of integrated energy service providers. The first stage is a retail pricing optimization model that takes into account day-ahead energy load and spot price randomness. The second stage is the day-ahead optimal scheduling model under the optimal retail price in the first stage. The particle swarm optimization (PSO) linear programming algorithm is used to solve the two-stage model iteratively. The results show that VaR-based retail pricing can effectively reduce the risk of integrated energy service providers participating in the retail market.

Key words: integrated energy spot market, integrated energy service provider, demand response, value at risk

CLC Number:

TM-9

GUO Zuogang, XU Min, LI Ruizhi, YUAN Zhiyong, TAN Yingjie, CHEN Baiyuan, LEI Jinyong, LIU Nian. Optimal Strategy of Integrated Energy Retail Market Based on VaR Theory[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(6): 9-16.

Figures/Tables 10

Fig.1 Flowchart of static retail pricing algorithm

Fig.2 Flow chart of calling a subroutine

Table 1 Elastic parameters of retail electric load

Table 2 Elastic parameters of retail heat load

Table 3 Probability distribution parameter of retail load

Fig.3 Optimal number of particle iterations

Fig.4 Probability distribution of Mday

Fig.5 Day-ahead scheduling of a typical scenario

Fig.6 Probability distribution of Mday under 20 scenarios

Fig.7 The probability distribution of Mday under actual pricing

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