微电网电池储能消纳弃风优化模型

杨效嘉; 杨俊友

doi:10.16513/j.cnki.10-1427/tk.2018.06.004

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分布式能源 ›› 2018, Vol. 3 ›› Issue (6) : 25-30. DOI: 10.16513/j.cnki.10-1427/tk.2018.06.004

有源配电网运行分析与控制

微电网电池储能消纳弃风优化模型

杨效嘉 ,
杨俊友

作者信息 +

Optimization Model of Wind Power Consumption by Energy Storage in Micro-Grid

Author information +

文章历史 +

摘要

随着风电渗透率在电力系统中的不断增大，风电无法并网的现象越发严重，弃风问题亟待解决。针对储能系统如何配合电网消纳弃风问题，建立多源储能电站优化调度模型。模型以系统运行成本最小为目标，相比于其他调度模型，其改进之处在于增加弃风成本和蓄电池运行成本，量化了弃风经济损失和蓄电池损耗。采用Matlab/Simulink仿真软件，通过改进粒子群优化算法对调度模型进行优化求解，对比分析配置储能装置前后系统的经济性。仿真结果表明配置蓄能电池后，各发电机组能灵活地参与电力系统调度，能有效提升系统风电消纳能力，降低弃风电量，具有良好的经济性。

Abstract

With the increasing of wind power penetration in the power system, the phenomenon that wind power cannot be connected to the grid becomes more and more serious, and the problem of wind abandonment needs to be solved urgently. Aiming at the problem of how the energy storage system cooperates with the abandonment of wind in the power grid, this paper builds up an optimal scheduling model including multi source and energy storage power station. The proposed model aims at the minimum running cost of the system. Compared with other scheduling models, the improvement of the model is to increase the cost of wind abandonment and battery operation, and quantify the economic loss of wind abandonment and battery loss. Matlab/Simulink simulation software is used to optimize the scheduling model through improved particle swarm optimization algorithm, and the economy of the system before and after energy storage device configuration is compared and analyzed. Simulation results show that after the energy storage battery is configured, each generator set can flexibly participate in the power system scheduling, effectively improve the wind power consumption capacity of the system, and reduce the abandoned wind power, which has good economic benefits.

导出引用

杨效嘉, 杨俊友. 微电网电池储能消纳弃风优化模型[J]. 分布式能源. 2018, 3(6): 25-30 https://doi.org/10.16513/j.cnki.10-1427/tk.2018.06.004

Xiaojia YANG, Junyou YANG. Optimization Model of Wind Power Consumption by Energy Storage in Micro-Grid[J]. Distributed Energy Resources. 2018, 3(6): 25-30 https://doi.org/10.16513/j.cnki.10-1427/tk.2018.06.004

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