基于QPSO微电网混合储能系统容量优化配置  

Capacity optimization configuration of microgrid hybrid energy storage system based on QPSO

作　　者：王皓 刘毅力[1] Wang Hao;Liu Yili(School of Electronic Information,Xi'an Polytechnic University,Xi'an 710048,China)

机构地区：[1]西安工程大学电子信息学院,西安710048

出　　处：《国外电子测量技术》2022年第8期167-173,共7页Foreign Electronic Measurement Technology

基　　金：国家自然科学基金(61871318)项目资助。

摘　　要：储能装置配置的容量不仅影响微电网的经济运行,而且影响供电的可靠性。研究具有典型负载、可再生能源和由电池和超级电容组成的混合储能系统(HESS)的孤立微电网,使用了优化HESS容量的量子行为粒子群优化(QPSO)算法。根据超级电容和电池各自的功率补偿能力,利用低通滤波器原理,采用合理的能量调度策略,避免电池频繁充放电。考虑每种储能类型的额定功率,修正各自的补偿功率。通过判断充电状态是否达到极限,再次修正数值。孤立微电网的仿真结果验证了HESS优化容量配置方法的有效性。以西北地区一个独立微电网为例,验证了该方法的有效性。QPSO与传统粒子群算法(PSO)的比较表明,其可以更快地找到最优解,HESS具有更低的日常开销。The configured capacity of the energy storage device not only affects the economic operation of the microgrid, but also affects the reliability of the power supply. This paper studies isolated microgrids with typical loads, renewable energy sources and a hybrid energy storage system(HESS) consisting of batteries and supercapacitors, using a quantum behavioral particle swarm optimization(QPSO) algorithm to optimize HESS capacity. According to the respective power compensation capabilities of the supercapacitor and the battery, the principle of low-pass filter is used, and a reasonable energy scheduling strategy is adopted to avoid frequent charging and discharging of the battery. Correct the respective compensation power considering the rated power of each energy storage type. By judging whether the state of charge has reached the limit, the value is corrected again. The simulation results of the isolated microgrid verify the effectiveness of the HESS optimal capacity allocation method. In this paper, an isolated microgrid in Northwest China is taken as an example to verify the effectiveness of the method. The comparison between QPSO and traditional particle swarm algorithm(PSO) shows that it can find the optimal solution faster, and HESS has lower overhead.

关 键 词：混合储能 容量配置 微电网 量子行为粒子群优化 

分 类 号：TM6[电气工程—电力系统及自动化]