中国电力 ›› 2018, Vol. 51 ›› Issue (2): 99-104,124.DOI: 10.11930/j.issn.1004-9649.201612076

• 新能源 • 上一篇    下一篇

风蓄联合系统的抽水蓄能容量优化

易琛1, 任建文1, 于佳2   

  1. 1. 新能源电力系统国家重点实验室(华北电力大学), 河北 保定 071003;
    2. 国网山东省电力公司菏泽供电公司, 山东 菏泽 274000
  • 收稿日期:2016-12-07 修回日期:2017-08-22 出版日期:2018-02-05 发布日期:2018-02-11
  • 作者简介:易琛(1993—),女,湖南岳阳人,硕士研究生,从事电力系统分析、运行与控制研究,E-mail:ncepuyc@126.com。
  • 基金资助:
    国家自然科学基金资助项目(51177043)。

Research on Capacity Optimization of Pumped-Storage Power Station with Wind Farm

YI Chen1, REN Jianwen1, YU Jia2   

  1. 1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Baoding 071003, China;
    2. Heze Power Supply Company of State Grid Shandong Electric Power Company, Heze 274000, China
  • Received:2016-12-07 Revised:2017-08-22 Online:2018-02-05 Published:2018-02-11
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No. 51177043).

摘要: 为了提高风蓄联合系统内风电场运行的经济性,有必要对抽水蓄能进行容量优化。首先计及风电特性给出了风蓄联合运行策略,以减小风电随机性对电网稳定性的影响;其次,给出了计算风电并网功率的数学模型,可作为调度员拟定发电计划的依据;以联合系统的安装和运行维护总成本最低为目标,结合机会约束规划理论建立了抽水蓄能容量优化模型,并采用混沌粒子群优化算法进行求解。算例分析表明了该方法的合理性。

关键词: 风电场, 抽水蓄能电站, 容量优化, 随机规划, 混沌粒子群优化算法

Abstract: Pump-storage capacity optimization is very important to improve wind farm operation economics. According to wind farm characteristics, an operation strategy of wind power-pumped storage system is proposed to reduce effect of wind fluctuations and randomness on system stability. A model is proposed to calculate reference value of wind farm output power, which is used by dispatcher to formulate generation plans. A stochastic optimization model for determining pumped-storage capacity is presented, which aims to minimize total cost of wind farm operation and pumped-storage installation and maintenance. The model is solved by chaotic particle swarm optimization algorithm. Analysis of examples show the effectiveness of proposed scheduling strategy.

Key words: wind farm, pumped-storage power station, capacity optimization, stochastic programming, chaotic particle swarm optimization algorithm

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