Multi-objective optimization model of microgrid considering demand response and uncertainty of wind power and photovoltaic

杨力俊; 潘伟; 田闻旭

doi:10.3969/j.issn.1009-1831.2022.03.001

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Multi-objective optimization model of microgrid considering demand response and uncertainty of wind power and photovoltaic

更新时间：2026-03-05

- Multi-objective optimization model of microgrid considering demand response and uncertainty of wind power and photovoltaic
- Vol. 24, Issue 3, (2022)
- 作者机构：
  
  1. 国家电网有限公司,北京,100031
  2. 国网江苏省电力有限公司常州供电分公司,江苏,常州,213161
  3. 国能龙源电力技术工程有限责任公司,北京,100039
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.03.001
  CLC：
- Published：2022
- 稿件说明：
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杨力俊, 潘伟, 田闻旭. Multi-objective optimization model of microgrid considering demand response and uncertainty of wind power and photovoltaic[J]. 2022, 24(3).
DOI：

杨力俊, 潘伟, 田闻旭. Multi-objective optimization model of microgrid considering demand response and uncertainty of wind power and photovoltaic[J]. 2022, 24(3). DOI： 10.3969/j.issn.1009-1831.2022.03.001.

摘要

微电网旨在实现发电侧的灵活性和可靠性并存，兼顾可再生能源利用和用能稳定，基于此目的，构建了一个多能源、多储能结构、多类型负荷耦合的微电网系统，提出一种考虑微电网补偿成本、弃能成本、运营收入的多目标优化模型，并引入需求响应模型优化储能和用户参与调峰。为解决微电网中风光出力的不确定性，对风力发电和光伏出力场景进行削减，通过决策分析表将多目标转化为单目标模型，应用粗糙集理论进行目标函数权重计算。算例分析结果显示：场景缩减能够克服风光出力不确定性对系统调度的影响；需求响应有助于解决源荷两侧匹配度差的问题；需求响应和储能系统的协作效应，可以提高系统的稳定性。

Abstract

Microgrid is aimed at achieving the coexistence of flexibility and reliability on the power generation side

giving consideration to the forth putting of renewable energy and the stability of energy use. Based on this purpose

a microgrid with multi-energy

multi-energy storage structure and multi-type load is constructed. A multi-objective optimization model that considers microgrid compensation costs

abandonment costs

and operating income is proposed

and a demand response model to optimize energy storage and user participation in peak shaving are introduced. In order to deal with the uncertainty of wind and photovoltaic output

the WPP and PV output scenarios are reduced

the multi-objective model is transformed into a single - objective model through the decision analysis table

and the rough set theory is used to calculate the objective function weight. The analysis results of the calculation ex-ample show that scenario reduction can overcome the impact of uncertainty on system scheduling and demand response can solve the problem of poor matching between source and load. The interaction of DR and ESS can improve system sustainability.

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