Demand response dispatching strategy for intelligent community based on multi⁃group co⁃evolution genetic algorithm

杨斌; 陈振宇; 阮文骏; 陆子刚; 黄奇峰

doi:10.3969/j.issn.1009-1831.2019.02.003

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Demand response dispatching strategy for intelligent community based on multi⁃group co⁃evolution genetic algorithm

更新时间：2026-03-05

- Demand response dispatching strategy for intelligent community based on multi⁃group co⁃evolution genetic algorithm
- Vol. 21, Issue 2, (2019)
- 作者机构：
  
  1. 国网江苏省电力有限公司,南京,210024
  2. 国网江苏省电力有限公司电力科学研究院,南京,210019
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2019.02.003
  CLC：
- Published：2019
- 稿件说明：
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杨斌, 陈振宇, 阮文骏, et al. Demand response dispatching strategy for intelligent community based on multi⁃group co⁃evolution genetic algorithm[J]. 2019, 21(2).
DOI：

杨斌, 陈振宇, 阮文骏, et al. Demand response dispatching strategy for intelligent community based on multi⁃group co⁃evolution genetic algorithm[J]. 2019, 21(2). DOI： 10.3969/j.issn.1009-1831.2019.02.003.

摘要

针对包含多种用能资源的智能小区中需求响应调度问题，首先建立储能、光伏和电动汽车的优化模型，目标函数设置为系统和电网的交换电量和总运行费用最小。然后利用多种群协同进化遗传算法优化结果。最后，以某智能小区为例，利用MATLAB工具对负荷模型进行仿真分析，验证所建模型的正确性和可行性，结果表明所设计的策略提高了光伏消纳率，降低了小区的运行费用。

Abstract

For the demand response scheduling problems in an intelligent community containing multiple energy resources

the optimal model is firstly established concerning energy storage

photovoltaic and electric vehicles. The objective function is the minimum exchange quantity and total operation cost between the system and the grid. Then the multi?group collaborative purification genetic algorithm is used to obtain the optimization result. Finally

taking a smart community as an example

MATLAB tool is used to simulate the load model and verify its correctness and feasibility. It shows that the proposed strategy improves the PV consumption rate and reduces the operating cost.

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