Optimal Planning and Operation of Battery Energy Storage Systems in Smart Grids Using Improved Genetic Algorithm Based Intelligent Optimization Tool

2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE) Pub Date : 2018-10-01 DOI:10.23919/ICUE-GESD.2018.8635735

Kannathat Mansuwan, P. Jirapong, Sattawat Burana, P. Thararak

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引用次数: 5

Abstract

In this paper, an improved genetic algorithm (IGA) implemented with reliable power system analysis tool is developed to determine the optimal planning and operation of battery energy storage system (BESS) in smart grid with photovoltaic (PV) generation. The main objectives are maximizing benefit from energy losses reduction and energy shaving enhancement, while minimizing the investment cost. Double layers optimization technique is implemented for determining the BESS siting and sizing in the first layer, while the maximum energy shaving is calculated in the second layer. The IGA implemented in MATLAB and DIgSILENT programs utilizing an automatic data exchange process is utilized for solving the optimal solution. This approach is tested on a practical 22 kV distribution network of Thailand to present the effectiveness of decision-making support tool. The simulation results show that the optimal BESS planning results in mitigating PV intermittency and improvement in smart grid efficiency.

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基于改进遗传算法的智能优化工具在智能电网中电池储能系统的优化规划与运行

本文提出了一种基于可靠电力系统分析工具的改进遗传算法(IGA)，用于确定光伏发电智能电网中电池储能系统(BESS)的最优规划和运行。主要目标是最大限度地减少能源损失和提高能源剃须，同时最大限度地降低投资成本。采用双层优化技术，在第一层确定BESS的位置和尺寸，在第二层计算最大能量剃须。在MATLAB和DIgSILENT程序中实现IGA，利用自动数据交换过程求解最优解。通过对泰国22kv配电网的实际测试，验证了决策支持工具的有效性。仿真结果表明，优化后的BESS规划能够有效缓解光伏发电间歇性，提高智能电网效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE)

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