基于相关机会目标规划的风光储联合发电系统储能调度策略

ZHAO Shuqiang

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基于相关机会目标规划的风光储联合发电系统储能调度策略

更新时间：2025-08-13

- 基于相关机会目标规划的风光储联合发电系统储能调度策略
- Automation of Electric Power Systems Vol. 39, Issue 14, Pages: 30-36(2015)
- 作者机构：
  
  新能源电力系统国家重点实验室, 华北电力大学,北京市,102206
- 作者简介：
- 基金信息：
- DOI：
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- Published：2015
- 稿件说明：
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ZHAO Shuqiang. [J]. Automation of electric power systems, 2015, 39(14): 30-36.
DOI：

ZHAO Shuqiang. [J]. Automation of electric power systems, 2015, 39(14): 30-36. DOI：

摘要

为改善风电、光伏发电出力的不确定性

使风光储联合发电系统具有可调度性

将风电和光伏发电的出力表示为确定性的出力和具有模糊性的误差之和。利用相关机会目标规划方法

以风光储调度出力曲线与计划出力曲线匹配程度最大

储能电池充放电功率最平缓和储能电池可持续工作性最佳为目标

建立了风光储联合发电系统储能调度模型。利用将模糊模拟、神经网络与改进粒子群算法嵌套结合形成新型混合智能算法对模型进行了求解

给出了联合发电系统日前储能调度计划。算例分析表明

新算法能够较好地求解高维数不确定规划问题

其收敛速度快

全局和局部搜索能力强。

Abstract

In order to reduce the output uncertainty of wind and photovoltaic power generation and make the wind-solar-battery hybrid power system schedule

the outputs of wind and photovoltaic power generation are processed into certainty variables and fuzzy error sum. An energy storage model is built for wind-solar-battery hybrid power system

in which the maximum matching degree between the planning output curve and the scheduling output curve

the most smoothness of charge and discharge power in energy storage battery and the most optimal sustainable work ability of the energy storage battery are taken as objectives using the dependent-chance goal programming method. By combining fuzzy simulation

neural networks with improved particle swarm optimization algorithm

a new hybrid intelligent algorithm is proposed to solve the model

and its recently day schedule is given. Finally

numerical examples verify the effectiveness of the proposed method in solving high-dimensional uncertain programming problems with fast convergence speed and satisfactory global and local searching ability. This work is supported by Fundamental Research Funds for the Central Universities (No. 12MS106).

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Related Institution

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