基于改进蚁群算法的多目标储能系统规划与配置研究

Wang Deshun, Zhao Yumeng, Tao Qiong, Xue Jinhua, Ye Jelei
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引用次数: 12

摘要

高磁导率可再生能源出力超过配电网的消纳能力,导致潮流逆流,电压上升甚至过电压。提高配电网中可再生能源的本地利用率越来越受到人们的重视。局部消耗率与输出功率特性与负载特性的匹配程度有关。储能系统具有能量的双向流动特性,为配电网局部吸收可再生能源提供了更大的灵活性。但目前储能系统成本较高,盲目增加储能容量以提高当地可再生能源消纳的经济效益较差。因此,降低储能系统的建设成本是优化储能容量规划和配置的关键。本文以典型日的充放电能量平衡为约束条件。建立了基于容量最小和充放电功率的双目标储能动态规划与配置的求解模型。通过基于分层排序法的改进蚁群算法,在储能容量最优目标约束下寻找储能功率的最优解,实现多目标优化规划与配置。针对多目标储能系统的规划与配置,通过算例验证了改进蚁群优化算法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Planning and Configuration of Multi-objective Energy Storage System Solved by Improved Ant Colony Algorithm
High-permeability renewable energy output exceeds consumptive capability of distribution network, which leads to tidal current countercurrent and voltage rise or even overvoltage. Improving the local consumption rate of renewable energy in distribution network is getting more and more attention. The local consumption rate is related to the matching degree of the power output characteristics and the load characteristics. Energy storage system has bi-directional flow characteristics of energy, which provides more flexibility for the distribution network to absorb renewable energy locally. However, the cost of energy storage system is high at present, and the economic efficiency of blindly increasing energy storage capacity to improve local renewable energy consumption is poor. Therefore, reducing construction costs of energy storage system is the key to optimizing planning and configuration of energy storage capacity. In this paper, the energy balance of charge and discharge in a typical day is taken as constraints. A solution model of energy storage dynamic planning and configuration based on bi-objective with minimum capacity and charging and discharging power is established. Through an improved ant colony algorithm based on stratified sequencing method, the multi-objective optimal planning and configuration is realized by finding the optimal solution of energy storage power in the optimal target constraint of energy storage capacity. For planning and configuration of multi-objective energy storage system, the feasibility and effectiveness of the improved ant colony optimization algorithm are verified by an example.
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