基于IPSO的风光高渗透电网抽水蓄能电站容量优化

Capacity Optimization of Pumped Storage Station with High Windand Photovoltaic Penetration based on IPSO

针对现有的抽水蓄能电站容量优化方法较多忽略其较大的电能时空转移效益与增强系统调峰能力效益,文章在构建抽水蓄能电站提升新能源发电并网消纳能力提升的充放电数学模型的基础上,进一步综合考虑电站的电量效益、电能时空转移效益、环境效益、调峰效益以及电站的初始投建成本、运维成本与废弃电站处理成本,构建抽水蓄能电站容量优化数学模型,并采用改进的粒子优化群算法(Improved Particle Swarm Organization,IPSO)对模型进行求解.最后,以新疆某含大规模风电与光伏发电的区域电网运行数据为背景,仿真验证了本文所提出方法的正确性与有效性.

Aiming at the problem that the existing pumped storage station capacity optimization methods often ignore the benefits of time-space shifting and peak shaving capability enhancement, based on the charging-discharging mathematical model that develops new energy consumption capacity is established.Furthermore, considering the pumped storage station＇s benefit of generated electricity, time-space shifting,environment and peak shaving, and cost of initial construction, operation and maintenance and crippled power station disposal, mathematical model of pumped storage station capacity optimization is built, and it is solved by IPSO（Improved Particle Swarm Organization）. Lastly, according to the operation data of a regional power system with high wind and photovoltaic penetration in Xinjiang, the validity and effectiveness of the proposed method are verified.