考虑最佳期望输出与荷电状态的风电场储能容量优化方法

ZHANG Feng

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考虑最佳期望输出与荷电状态的风电场储能容量优化方法

更新时间：2025-08-13

- 考虑最佳期望输出与荷电状态的风电场储能容量优化方法
- Automation of Electric Power Systems Vol. 38, Issue 24, Pages: 12-19(2014)
- 作者机构：
  
  山东大学电气工程学院,山东省,济南市,250061
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014
- 稿件说明：
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ZHANG Feng. [J]. Automation of electric power systems, 2014, 38(24): 12-19.
DOI：

ZHANG Feng. [J]. Automation of electric power systems, 2014, 38(24): 12-19. DOI：

摘要

风电场储能配置成本与功率波动平抑效果相互制衡，而储能容量的最优化则是解决上述问题的重要方式。为此，首先以并网功率目标值偏移量方差最小为优化目标，计算最佳期望输出，消除功率波动平抑输出目标值设定的主观性，并作为储能容量最优化的理论前提。其次，引入储能荷电状态（SOC）参量，基于模糊控制理论

根据SOC和充放电状态适时调整充放电功率，构建储能系统充放电策略，有效抑制过度充放。最后，由SOC关联的惩罚成本与运行成本之和最小建立优化模型，实现兼顾调度决策需求、储能系统运行寿命和经济性的储能容量最优化。利用改进的粒子群优化算法对相关优化问题进行求解。通过山东某风电场实际运行数据进行验证，对比分析结果表明了该方法的有效性。

Abstract

The cost of energy storage allocation contradicts the smoothing effect of power fluctuation

while energy storage capacity optimization is a feasible way to solve this problem. Firstly

by taking the minimum target value offset variance as the optimal object

the optimal expected output is calculated to eliminate the subjectivity during the determination of output target value of power fluctuation smoothing. Such a line of thought is regarded as the theoretical basis of energy storage capacity optimization. Secondly

considering the parameter of state of charge (SOC) and based on the fuzzy control theory

the charge and discharge power is adjusted timely according to the SOC and the charge-discharge state. Then the charge and discharge strategy for the energy storage system (ESS) is devised while excessive charge or discharge is obviously restrained. Finally

the optimization mathematical model is developed to minimize the sum of penalty cost and running cost

while the energy storage capacity optimization balancing the dispatching decision demand

ESS service life and operating economy are realized. The improved particle swarm optimization (PSO) algorithm is adopted for optimization calculation

with the effectiveness of the method shown by actual wind farm data.

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