A bi-level energy storage planning method for enhancing the hosting region of distribution networks

WANG Ziyao; YU Xueying; LI Huaqiang; LIU Yang; XU Lixiong

doi:10.19783/j.cnki.pspc.251376

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A bi-level energy storage planning method for enhancing the hosting region of distribution networks

更新时间：2026-07-19

- A bi-level energy storage planning method for enhancing the hosting region of distribution networks
- Power System Protection and Control Vol. 54, Issue 14, (2026)
- 作者机构：
  
  四川大学电气工程学院,四川,成都,610065
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.251376
  CLC：
- Published：2026
- 稿件说明：
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WANG Ziyao, YU Xueying, LI Huaqiang, et al. A bi-level energy storage planning method for enhancing the hosting region of distribution networks[J]. Power System Protection and Control, 2026, 54(14).
DOI：

WANG Ziyao, YU Xueying, LI Huaqiang, et al. A bi-level energy storage planning method for enhancing the hosting region of distribution networks[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.251376.

摘要

随着高比例分布式能源渗透及随机性负荷的广泛接入，配电网运行呈现出显著的时序耦合与不确定性。现有研究多局限于静态承载力评估及可行域安全校核，缺乏对系统整体承载空间的量化与优化方法。为此，提出一种面向配电网承载域提升的储能双层规划方法。首先，构建融合承载力需求与可行域状态空间的配电网承载域模型。其次，提出基于降维投影的承载域体积与灵敏度指标，量化多时段耦合下的整体可行区域规模以及储能配置的边际效益。在此基础上，建立以最大化承载域体积和最小化经济成本为目标的双层规划模型。上层采用改进粒子群算法(improved particle swarm optimization

IPSO)优化容量配置，下层利用门控循环单元-近端策略优化(gated recurrent unit-proximal policy optimization

GRU-PPO)框架求解动态承载域。算例结果表明，所提方法能准确刻画可行域结构特征，显著提升系统在源荷双重不确定性下的承载能力。

Abstract

With the high penetration of distributed energy resources and the widespread integration of stochastic loads

the operation of distribution networks exhibits significant temporal coupling and uncertainty. Existing research is often limited to static hosting capacity assessment and feasible operating region verification

while lacking effective methods to quantify and optimize the overall hosting region of the system. To address this issue

a bi-level energy storage planning method for enhancing the hosting region of distribution networks is proposed. First

a distribution network hosting region model is constructed by integrating hosting capacity requirements with feasible operating state space. Second

a dimension-reduction projection-based hosting region volume metric and sensitivity index are developed to quantify the overall size of the feasible operating region under multi-period coupling and to evaluate the marginal benefits of energy storage deployment. On this basis

a bi-level planning model is established aiming at maximizing the hosting region volume while minimizing the overall economic costs. The upper level problem employs an improved particle swarm optimization (IPSO) algorithm to determine the optimal energy storage capacity allocation

while the lower level utilizes a gated recurrent unit-proximal policy optimization (GRU-PPO) framework to solve the dynamic hosting region. Case study results demonstrate that the proposed method accurately characterizes the structural features of the feasible operating region and significantly enhances the system's hosting capacity under the dual uncertainties of source and load.

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references

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