An Optimal Storage Capacity Allocation Method for Wind and Solar Energy Bases Through Production Simulation

LIU Chunming; WANG Junwang; LI Ruiyue; LIU Shanhe; GU Pan

doi:10.19725/j.cnki.1007-2322.2023.0316

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An Optimal Storage Capacity Allocation Method for Wind and Solar Energy Bases Through Production Simulation

更新时间：2025-12-11

- An Optimal Storage Capacity Allocation Method for Wind and Solar Energy Bases Through Production Simulation
- Issue 6, Pages: 1258-1267(2025)
- 作者机构：
  
  1. 华北电力大学电气与电子工程学院, 北京市昌平区,102206
  2. 中共国网北京电力公司党校, 北京市西城区,100041
  3. 国网经济技术研究院有限公司, 昌平区,北京市,102209
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0316
  CLC：
- Published：2025
- 稿件说明：
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LIU Chunming, WANG Junwang, LI Ruiyue, et al. An Optimal Storage Capacity Allocation Method for Wind and Solar Energy Bases Through Production Simulation[J]. 2025, (6): 1258-1267.
DOI：

LIU Chunming, WANG Junwang, LI Ruiyue, et al. An Optimal Storage Capacity Allocation Method for Wind and Solar Energy Bases Through Production Simulation[J]. 2025, (6): 1258-1267. DOI： 10.19725/j.cnki.1007-2322.2023.0316.

摘要

风、光等可再生能源发电大规模并网带来消纳难题，在新能源基地合理配置储能有利于提高消纳率，然而储能成本居高不下严重制约了系统的经济性，如何确定最优储能容量成为一个亟待解决的问题。首先基于生产模拟的方法建立了双层优化配置模型对风光能源基地的最优储能容量进行研究。首先利用生成对抗网络对风光荷历史数据进行无监督学习，生成符合数据分布规律的海量样本数据，作为生产模拟的输入；然后在内层配置模型中考虑储能装置的各项成本效益，以净收益最大为目标优化储能运行，并将结果反馈至外层，外层以全寿命周期下以储能投资回报率最大为目标优化储能容量。通过内外层迭代完成全寿命周期仿真计算，并以全寿命周期收益及弃电惩罚成本评价配置结果；最后以甘肃某大型新能源基地为例进行仿真研究，验证了所提方法的合理性和有效性。

Abstract

The large-scale integration of wind

solar and other renewable energy power generation into the grid poses challenges in terms of consumption. The rational allocation for energy storage in new energy bases is conducive to improving the consumption rate. However

the high cost of energy storage seriously restricts the system economy. How to determine the optimal energy storage capacity has become an urgent issue in need of resolution. Based on the method of production simulation

in this paper we establish a bi-level optimal configuration model

aiming to investigate the optimal energy storage capacity of the wind and solar energy bases. Firstly

we employ the Generative Adversarial Network for unsupervised learning on the historical data of wind

photovoltaic and loads

and generate massive sample data that conform to the data distribution law. These data are then used as input for production simulation. The energy storage device’s cost-benefit is evaluated within the inner configuration model to optimize its operation with the goal of maximizing the net present value. The results are then feed into the outer layer

which optimizes the energy storage capacity with the goal of maximizing the energy storage return on investment throughout its entire life cycle. The entire life cycle simulation calculation is completed through the inner and outer layer iterations

and the configuration results are evaluated by the life cycle benefits and power abandonment penalty costs. Finally

a large-scale new energy base in Gansu Province is taken as an example to conduct simulation research

thereby verifying the rationality and effectiveness of the method in this paper.

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