基于NSGA-II风蓄协调的多目标优化调度研究

吴巍; 陈波; 于楠; 叶元; 刘胜; 夏家辉

doi:10.3969/j.issn.1009-1831.2019.02.009

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基于NSGA-II风蓄协调的多目标优化调度研究

节能与综合能效专辑 | 更新时间：2026-03-05

- 基于NSGA-II风蓄协调的多目标优化调度研究
- Research on multi⁃objective optimal dispatch for the coordination between wind energy and pumped storage based on NSGA⁃II
- 电力需求侧管理 2019年21卷第2期
- 作者机构：
  
  三峡大学电气与新能源学院,湖北,宜昌,443002
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目（51477090）
- DOI：10.3969/j.issn.1009-1831.2019.02.009
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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吴巍, 陈波, 于楠, 等. 基于NSGA-II风蓄协调的多目标优化调度研究[J]. 电力需求侧管理, 2019,21(2).

吴巍, 陈波, 于楠, et al. Research on multi⁃objective optimal dispatch for the coordination between wind energy and pumped storage based on NSGA⁃II[J]. 2019, 21(2).
吴巍, 陈波, 于楠, 等. 基于NSGA-II风蓄协调的多目标优化调度研究[J]. 电力需求侧管理, 2019,21(2). DOI： 10.3969/j.issn.1009-1831.2019.02.009.

吴巍, 陈波, 于楠, et al. Research on multi⁃objective optimal dispatch for the coordination between wind energy and pumped storage based on NSGA⁃II[J]. 2019, 21(2). DOI： 10.3969/j.issn.1009-1831.2019.02.009.

摘要

针对风电并网带来弃风与常规机组污染排放问题，在考虑经济效益、提高风电利用率的同时，建立一种多目标风电-抽水蓄能联合优化日运行模型，采用带精英策略的快速非支配排序遗传算法（non dominated sorting genetic algorithm II，NSGA II）进行机组特性分层对比，确定抽蓄机组与常规机组工况，以最大化抽蓄机组削峰填谷的效益和最小化常规机组耗煤与排放量为目标，寻求最优运行方式。通过算例对比3种不同的运行方式，证明该模型能够有效地减少弃风功率，煤耗量和排放量，同时与多目标粒子群算法（multi objective particle swarm optimization，MOPSO）对比显示出NSGA II求解的优越性。

Abstract

Aiming at the problems of wind power curtailment and pollutant emission of conventional units

and considering the economic benefits and improvement of wind energy utilization

a multi objective combined operation model of wind power and pumped storage is established. Non dominated sorting genetic algorithm II（NSGA II）is used to compare unit characteristics and determine the working conditions of the storage unit and the conventional unit. In order to maximize the benefit of peak shaving and valley filling of pumped storage units and minimize coal consumption and emission of conventional units

the optimum running mode is studied. Comparing three different running modes

the example proves that the model can effectively reduce the wind power curtailment

coal consumption and emissions. Meanwhile

comparing with multi objective particle swarm optimization

the superiority of NSGA II is shown.

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