A regional hierarchical energy storage planning method based on improved particle swarm optimization

LU Xiaoyan; WANG Jinyou; MA Peihua; WU Xiong; YU Zuwang

doi:10.3969/j.issn.1672-3643.2026.03.002

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A regional hierarchical energy storage planning method based on improved particle swarm optimization

更新时间：2026-10-10

- A regional hierarchical energy storage planning method based on improved particle swarm optimization
- Ningxia Electric Power Issue 3, (2026)
- 作者机构：
  
  1. 宁夏宁电电力设计有限公司,宁夏,银川,750004
  2. 西安交通大学电气工程学院,陕西,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1672-3643.2026.03.002
  CLC：
- Online First：21 July 2026，
  
  Published：2026
- 稿件说明：
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LU Xiaoyan, WANG Jinyou, MA Peihua, et al. A regional hierarchical energy storage planning method based on improved particle swarm optimization[J]. Ningxia Electric Power, 2026, (3).
DOI：

LU Xiaoyan, WANG Jinyou, MA Peihua, et al. A regional hierarchical energy storage planning method based on improved particle swarm optimization[J]. Ningxia Electric Power, 2026, (3). DOI： 10.3969/j.issn.1672-3643.2026.03.002.

摘要

我国西北地区建设了大规模新能源基地

为促进区域新能源有效消纳

本文提出基于改进粒子群算法的区域层级储能规划方法。首先

基于典型日方法获取储能规划初始解作为粒子群输入;其次

基于改进粒子群算法开展储能双层规划得到精确解

其中

上层考虑省级-市级-单元级规划架构

下层进行生产模拟;最后

通过中国某省算例进行方法可行性分析

结果表明本文所提储能规划方法可以有效促进新能源消纳

提升系统经济性。

Abstract

Large-scale renewable energy bases are being constructed in Northwest China. To promote regional renewable energy integration

this paper proposes a regional hierarchical energy storage planning method based on an improved particle swarm optimization (IPSO) algorithm. First

the initial solution for energy storage planning is obtained through the typical day method via scenario generation and used as the input for the IPSO algorithm. Then

a bi-level energy storage planning is carried out based on the IPSO algorithm to derive the optimal solution: the upper level adopts a provincial-municipal-unit hierarchical planning framework

while the lower level performs production simulation. Finally

the feasibility and effectiveness of the proposed method are verified through a case study of a certain province in China. The results indicate that the proposed method can significantly enhance renewable energy integration capacity and improve overall system economics.

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