考虑负荷季节特性的电价型需求响应最优定价策略

doi:10.12204/j.issn.1000-7229.2023.01.007

电力建设 ›› 2023, Vol. 44 ›› Issue (1): 55-63.doi: 10.12204/j.issn.1000-7229.2023.01.007

• 双碳目标驱动下的能源电力经济与市场机制·栏目主持文福拴教授、刘敦楠教授· • 上一篇下一篇

考虑负荷季节特性的电价型需求响应最优定价策略

高原¹(), 杨贺钧¹(), 郭凯军²(), 马英浩¹()

1.新能源利用与节能安徽省重点实验室(合肥工业大学),合肥市 230009
2.国网安徽省电力有限公司阜阳供电公司,安徽省阜阳市 236018

收稿日期:2022-03-31 出版日期:2023-01-01 发布日期:2022-12-26
通讯作者: 杨贺钧 E-mail:hfutgy@126.com;cquyhj@126.com;13805580508@163.com;yinghao_ma@126.com
作者简介:高原(1995),男,硕士研究生,主要研究方向为电力系统规划与可靠性和需求响应,E-mail:hfutgy@126.com。
郭凯军(1969),男,高级工程师,主要研究方向为电力系统及其自动化,E-mail:13805580508@163.com。
马英浩(1989),男,博士,讲师,主要研究方向为电力系统规划与可靠性,E-mail:yinghao_ma@126.com。
基金资助:
安徽省自然科学基金项目(2108085UD08);中央高校基本科研业务费专项资金资助项目(PA2021KCPY0053)

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

GAO Yuan¹(), YANG Hejun¹(), GUO Kaijun²(), MA Yinghao¹()

1. Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving (Hefei University of Technology), Hefei 230009, China
2. Fuyang Power Supply Company of State Grid Anhui Electric Power Co., Ltd., Fuyang 236018, Anhui Province, China

Received:2022-03-31 Online:2023-01-01 Published:2022-12-26
Contact: YANG Hejun E-mail:hfutgy@126.com;cquyhj@126.com;13805580508@163.com;yinghao_ma@126.com

摘要/Abstract

摘要：

实施峰谷分时电价策略能够有效降低负荷峰谷差同时节约电网投资,但不同季节的负荷特性具有显著差异性,其影响峰谷分时电价最优策略的制定,因此文章提出了一种考虑负荷季节特性的峰谷分时电价定价策略及时段划分模型。首先,描述所提出的需求响应架构;其次,采用k均值方法获取各季节典型日的负荷曲线,并采用改进的移动边界技术对各季节典型日负荷曲线进行时段划分,通过设置时段划分约束因子,并采用戴维森堡丁指数(Davies-Bouldindex, DBI)作为目标函数建立峰谷时段划分优化模型;然后,构建考虑负荷季节特性的需求价格弹性矩阵,以及考虑负荷季节特性的峰谷分时电价优化模型,并采用粒子群优化(particle swarm optimization, PSO)算法求解模型。采用RTS测试系统提供的负荷序列样本对所提算法和模型进行验证分析,验证了所提方法和模型的有效性以及正确性。

关键词: 负荷季节特性, 峰谷时段划分, 分时电价策略, 需求价格弹性

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

中图分类号:

TM73

高原, 杨贺钧, 郭凯军, 马英浩. 考虑负荷季节特性的电价型需求响应最优定价策略[J]. 电力建设, 2023, 44(1): 55-63.

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.

图/表 9

图1 电价型需求响应最优决策模型流程

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

图2 基于粒子群优化算法的峰谷分时电价优化算法流程

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

图3 各个季节负荷曲线聚类结果

Fig.3 Clustering results of load curves in each season

表1 不同时段划分方法分析

Table 1 Analysis of results of different period partitioning methods

图4 春、秋季节典型日负荷曲线时段划分结果(本文提出方法)

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

图5 春、秋季节典型日负荷曲线时段划分结果(移动边界技术)

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

图6 适应值随迭代次数变化的收敛过程

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

图7 考虑不同负荷季节数优化得到的峰、平、谷时段电价与初始电价对比

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

图8 分时电价前后考虑不同负荷季节数优化得到的负荷曲线对比

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

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考虑负荷季节特性的电价型需求响应最优定价策略

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

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