DPV and HESS Joint Optimal Configuration in Distribution Network Considering Renewable Energy Consumption

段晓磊; 王宇飞; 魏云峰; 李艳琼; 崔欣

doi:10.19725/j.cnki.1007-2322.2023.0441

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DPV and HESS Joint Optimal Configuration in Distribution Network Considering Renewable Energy Consumption

更新时间：2026-02-02

- DPV and HESS Joint Optimal Configuration in Distribution Network Considering Renewable Energy Consumption
- Vol. 43, Issue 1, Pages: 181-192(2026)
- 作者机构：
  
  1. 国网冀北电力有限公司张家口供电公司,河北省,张家口市,075000
  2. 北京华辕杰成科技有限公司, 北京市朝阳区,100021
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0441
  CLC： 1
- Published：2026
- 稿件说明：
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段晓磊, 王宇飞, 魏云峰, et al. DPV and HESS Joint Optimal Configuration in Distribution Network Considering Renewable Energy Consumption[J]. 2026, 43(1): 181-192.
DOI：

段晓磊, 王宇飞, 魏云峰, et al. DPV and HESS Joint Optimal Configuration in Distribution Network Considering Renewable Energy Consumption[J]. 2026, 43(1): 181-192. DOI： 10.19725/j.cnki.1007-2322.2023.0441.

摘要

高比例新能源发展愿景给电力系统规划和运行带来诸多难题，亟需从配套储能等灵活性资源方面协同考虑。因此，提出一种考虑新能源消纳的配电网中分布式光伏及混合储能的联合优化配置方法，以满足提高新能源消纳、降低储能配置成本与提升系统灵活性等要求。首先，基于分布式光伏（distributed photovoltaic

DPV）和负荷的概率分布模型，利用拉丁超立方采样法（Latin hypercube sampling

LHS）均匀采样，随后基于Cholesky分解法提取DPV出力和负荷需求的时间相关性信息，构造分布式光伏接入后的典型系统运行场景。其次，考虑混合储能各部分在功率灵活匹配与能量时空转移方面的差异化特性，采用完备均值方法对储能管理系统所采集到的待转移DPV功率曲线按波动频率进行分解，再利用皮尔逊积矩相关系数对分解后各分量进行重构，得到高、中、低频分量，给出混合储能系统中各设备的功率分配策略。然后，以安装分布式光伏容量最大和综合投资成本最小为上层优化目标，以在各典型场景下系统运行成本最小、新能源消纳最大为下层优化目标建立双层优化模型，并采用粒子群和数学规划算法对模型上下层进行求解，得到分布式光伏和混合储能系统的最优配置容量以及各场景下混合储能的功率分配优化运行结果。仿真结果验证了所提策略和方法的有效性。

Abstract

The vision of a high proportion of renewable energy development has posed numerous challenges to the planning and operation of power systems

necessitating the simultaneous construction of flexible resources such as ESS. Therefore

a joint optimal allocation method for DPV and HESS in the distribution network is proposed

taking into account the renewable energy consumption. This method is designed to meet the requirements of improving renewable energy consumption

reducing energy storage allocation cost

and improving system flexibility. Firstly

we utilize the LHS method based on the probability distribution of DPV and load to make sure sample uniformly. Subsequently

the time correlation information between DPV output and load demand is extracted based on the Cholesky decomposition method

and typical system operation scenarios after distributed photovoltaic access are constructed. Secondly

considering the characteristics such as flexible power matching and energy spatiotemporal transfer of each part in HESS

the DPV power curve to be transferred is decomposed according to the fluctuation frequency by the EMS using the method of ELMD. After that

PPMCC is employed to reconstruct the decomposed components to obtain high

medium and low frequency components. Subsequently

a power allocation strategy of each device in HESS is formulated. A two-layer optimization model is thus established with the maximum installed distributed photovoltaic capacity and the minimum investment cost as the upper optimization objectives

while the minimum system operating cost and the maximum renewable energy consumption in each typical scenario as the lower optimization objectives. The PSO and mathematical programming algorithms are utilized to solve the upper and lower layers of this model

enabling us to obtain the optimal configuration capacity of DPV and HESS as well as the optimal operation results of power allocation of HESS in each scenario. The simulation results verify the effectiveness of this proposed strategy and method.

关键词

Keywords

references

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