Research on control method of distributed energy storage system to improve photovoltaic consumption

doi:10.19799/j.cnki.2095-4239.2021.0573

Abstract

Abstract:

The features of distributed energy storage are decentralization and flexibility, and the coordination of multiple distributed energy storage can address the problem of single energy storage's poor adjustment ability and limited range, as well as boost the capacity of new energy consumption and increase the pace of new energy use. This research establishes a photovoltaic power station, two distributed energy storage system models, examines the output of the photovoltaic power station, and uses the features of the energy storage system to track the photovoltaic output to develop an objective function with the minimum output of the dispersed energy storage system as the goal, combined with the power generation system. The linearly decreasing inertia weight particle swarm optimization algorithm is used to target the existing constraints and the distributed energy storage system's power balance requirements, state of energy constraints, power, and capacity constraints. Next, determine the most effective distributed energy storage system. Through simulation analysis, this method can improve the photovoltaic absorption capacity, reduce the number of actions of the energy storage system, and further increase the life of the energy storage system.

Key words: distributed energy storage, energy storage output, power balance, linearly decreasing inertia weight particle swarm optimization algorithm

CLC Number:

F 407.6

Wei ZENG, Junjie XIONG, Suliang MA, Yuliang TAN, Jianlin LI. Research on control method of distributed energy storage system to improve photovoltaic consumption[J]. Energy Storage Science and Technology, 2022, 11(10): 3268-3274.

Figures/Tables 10

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算法	特点
蚁群算法	蚁群算法中个体直接使用的是局部信息而非全局信息，搜索时间长，整个算法较为成熟，已成体系，收敛性能佳
遗传算法	实际问题中，很多问题都带有约束条件，其本身也是一个非线性问题，而遗传算法则可以很好地契合这类问题，它的挖掘性能强，但开拓能力较弱，注重寻找全局最优，仅需评估试验解的正确性，但其算法过程较为复杂，包括复制、组合、突变等过程
模拟退火算法	模拟退火算法的参数设置需要人为的调整，因此人为因素可能造成计算结果的差异
粒子群优化算法	粒子群优化算法所需的参数只有少数几个，算法的搜索和搜索空间的维度之间的相关性不强，原理简单，容易实现，运用协同搜索，收敛速度快，更容易找到全局最优值

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