Optimal Pricing Strategy of Electricity Price Demand Response Considering Seasonal Characteristics of Load

doi:10.12204/j.issn.1000-7229.2023.01.007

Abstract

Abstract:

The implementation of peak-valley time-of-use (TOU) price strategy can effectively reduce the peak-valley difference of load and save investment for power grid, but the load characteristics in different seasons are quite different, which affects the formulation of the optimal peak-valley TOU price strategy. Therefore, this paper mainly studies the peak-valley TOU price pricing strategy and period partitioning model considering multiple seasonal characteristics. Firstly, the demand response architecture proposed in this paper is described in combination with the main innovations of this paper. Secondly, the k-means method is adopted to obtain the load curve of typical days in each season, and the improved moving boundary technology is adopted to partition the load curve of typical days in each season. The optimization model for peak-valley period partitioning is established by setting the period partitioning constraint factors and adopting the Davies-Bouldin index (DBI) as the objective function. Then, the price elasticity of demand considering seasonal characteristics and the pea-valley TOU price optimization model considering multiple seasonal characteristics are established, and the particle swarm optimization (PSO) algorithm is used to solve the model. RTS is used to verify and analyze the algorithm and model, which verifies the effectiveness and correctness of the method and model proposed in this paper.

This work is supported by Natural Science Foundation of Anhui Province (No. 2108085UD08) and Fundamental Research Funds for the Central Universities (No. PA2021KCPY0053).

Key words: seasonal characteristics of load, peak-valley period partitioning, TOU pricing strategy, price elasticity of demand

CLC Number:

TM73

GAO Yuan, YANG Hejun, GUO Kaijun, MA Yinghao. Optimal Pricing Strategy of Electricity Price Demand Response Considering Seasonal Characteristics of Load[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(1): 55-63.

Figures/Tables 9

Fig.1 Flow chart of optimal decision model of electricity price demand response

Fig.2 Flow chart of peak-valley TOU price optimization algorithm based on PSO algorithm

Fig.3 Clustering results of load curves in each season

Table 1 Analysis of results of different period partitioning methods

Fig.4 Period partitioning results of typical daily load curve in spring and autumn (proposed method)

Fig.5 Period partitioning results of typical daily load curve in spring and autumn (the moving boundary technology)

Fig.6 Convergence process of fitness changing with the number of iterations

Fig.7 Comparison of peak, flat and valley electricity prices and initial electricity prices optimized by considering different load seasons

Fig.8 Comparison of load curves obtained by considering the optimization of different load seasons before and after TOU

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