Bi-level Optimization Configuration of Park-level Shared Energy Storage Considering User Differentiation and Stackelberg Game Pricing

HUANG Yuhe; ZHAO Jinbin; SHAN Jie; MAO Ling

doi:10.20204/j.sp.2026.04009

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Bi-level Optimization Configuration of Park-level Shared Energy Storage Considering User Differentiation and Stackelberg Game Pricing

更新时间：2026-04-20

- Bi-level Optimization Configuration of Park-level Shared Energy Storage Considering User Differentiation and Stackelberg Game Pricing
- Vol. 54, Issue 4, Pages: 70-77(2026)
- 作者机构：
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- DOI：10.20204/j.sp.2026.04009
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- Published：2026
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黄宇和,赵晋斌,单杰,毛玲.计及用户差异化和主从博弈定价的园区共享储能双层优化配置[J].智慧电力,2026,54(4):70-77.

doi:10.20204/j.sp.2026.04009
黄宇和,赵晋斌,单杰,毛玲.计及用户差异化和主从博弈定价的园区共享储能双层优化配置[J].智慧电力,2026,54(4):70-77. DOI： 10.20204/j.sp.2026.04009.

doi:10.20204/j.sp.2026.04009 DOI：

摘要

为提高储能运营商经济效益、改善园区用户用电行为，构建了一种考虑用户差异化和主从博弈定价的园区共享储能双层优化配置模型。首先，分析园区用户在主体类型、用电行为及需求等方面的差异，建立园区典型差异化用户模型，并设计了基于主从博弈的园区交易机制。其次，建立双层优化配置模型，上层以共享储能运营商日净利润最大化为目标，下层以园区用户总日运行成本最小化为目标。最后，以浙江省某园区为例进行算例分析。分析结果表明，相较于用户未配置储能，园区用户总日运行成本降低18.96%；相较于用户自建储能，总储能容量配置降低32.25%；相较于传统固定分时电价交易模式，动态投资回收期从6.47年缩短至4.79年。

Abstract

In order to enhance the economic benefits of energy storage operators and improve the electricity consumption behavior of park users

a bi-level optimization configuration model for park-level shared energy storage is constructed

considering user differentiation and Stackelberg game pricing. First

the differences among park users in terms of entity types

electricity consumption behaviors

and demands are analyzed. Typical differentiated user models for the park are established

and a park trading mechanism based on the Stackelberg game is designed. Second

a bi-level optimization configuration model is developed. The upper level aims to maximize the daily net profit of the shared energy storage operator

while the lower level aims to minimize the total daily operational cost of park users. Finally

a case study of a park in Zhejiang Province is conducted. The results show that compared with the scenario where users do not configure energy storage

the total daily operational cost of park users is reduced by 18.96%. Compared with the scenario where users build their own energy storage

the total energy storage capacity configuration is reduced by 32.25%. Compared with the traditional fixed time-of-use pricing transaction model

the dynamic investment payback period is shortened from 6.47 years to 4.79 years.

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