工业工程

工业工程 ›› 2021, Vol. 24 ›› Issue (6): 108-115.doi: 10.3969/j.issn.1007-7375.2021.06.014

• 实践与应用 • 上一篇    下一篇

基于分布式光伏发电的多目标分时电价优化策略

杨坤1, 伏跃红2, 江志斌3   

  1. 1. 上海交通大学 机械与动力工程学院，上海 200240;
    2. 国网电子商务有限公司，北京 100053;
    3. 上海交通大学 中美物流研究院，上海 200030
  • 收稿日期:2020-07-01 发布日期:2022-01-24
  • 作者简介:杨坤(1995—)，男，山东省人，硕士研究生，主要研究方向为绿色电力运作管理
  • 基金资助:
    国网电子商务有限公司资助项目(SGEC-2019-FF05-YD)

A Multi-objective Time-of-use Pricing Optimization Strategy for the Grid with Distributed Photovoltaic Power Generation

YANG Kun1, FU Yuehong2, JIANG Zhibin3   

  1. 1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. State Grid Electronic Commerce Co., Ltd., Beijing 100053, China;
    3. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, China
  • Received:2020-07-01 Published:2022-01-24

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摘要： 现有电力定价研究大多为峰谷分时定价，时段划分方式单一且大多采用传统非支配排序遗传算法-II求解多目标问题。针对这个问题，提出一种基于分布式光伏发电的多目标分时电价优化策略。建立用电量与电价响应模型，基于等效负荷进行时段划分，以负荷方差最小，等效负荷的峰谷差最小，用户满意度指数最大为目标，建立多目标非线性分布式光伏分时定价模型，并提出基于邻域搜索的多目标遗传算法求解。数值实验结果表明，供电稳定性提高了37.77%，分布式光伏发电的利用率提高了4.51%，用户满意度为74.3%；且提出的求解算法要优于常用的非支配排序遗传算法-II，表明本文提出的定价策略是有效的。

关键词: 分布式光伏, 分时电价, 多目标优化, 用户满意度, 需求响应

Abstract: In previous researches, most electricity pricing strategies were time-of-use price, and the traditional non-dominated sorted genetic algorithm-II is mostly used to solve the multi-objective problem. To solve the problem of the fluctuation of distributed photovoltaic grid connection, a multi-objective time-of-use pricing optimization strategy is proposed for the grid with distributed photovoltaic power generation. Firstly, the response model of electricity consumption and electricity price is established, and the time period is divided based on the equivalent load. The multi-objective nonlinear distributed photovoltaic time-of-use pricing model is established with the objective of minimum load variance, minimum peak-valley difference of equivalent load, and maximum user satisfaction index. A multi-objective genetic algorithm combined with a neighborhood search algorithm is proposed to solve the complex problem and obtain the optimal pricing strategy. As shown in the numerical experiment, the pricing strategy proposed improves the power supply stability by 37.77%, and improves the utilization rate of distributed photovoltaic power generation by 4.51%, with user satisfaction improved to 74.3%. In addition, the proposed algorithm outperforms the widely used non-dominated sorted genetic algorithm-II. These results show that the proposed pricing strategy is effective.

Key words: distributed photovoltaic, time-of-use pricing, multi-objective optimization, user satisfaction, demand response

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