基于改进粒子群算法的梯级水风光短期调峰优化调度
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国家自然科学基金(52077120)


Optimal Scheduling of Cascaded Hydro-Wind-Photovoltaic Short Term Peak Shaving Based on Improved Particle Swarm Optimization Algorithm
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    摘要:

    针对大规模风电、光电直接并入电网对系统调峰带来的负面影响,同时伴随高弃风、高弃光率的问题,本文提出通过利用水电输出通道将湖北一梯级水电站与附近风电场、光电场并入电网联合调峰的解决办法。首先分析了梯级水电站、风电和光电联合调度的必要性和可行性,提出了梯级水-风-光联合调峰策略,构建了以系统余留负荷均方差最小为目标函数的短期调度模型,最后利用收缩因子和改进粒子群算法求解。通过算例仿真得到,风光与梯级水电站共同参与系统调峰,调峰效果更好,改进后的PSO有更好的寻优精度和收敛速度。给未来实施梯级水电站与周围风、光电场联合调峰策略提供了参照。

    Abstract:

    In view of the negative impact of large-scale wind power and photovoltaics directly integrated into the power grid on peak shaving of the system, accompanied by the problems of high wind abandonment and high abandonment rate, a joint peak shaving solution is proposed to combine a cascade hydropower station in Hubei with nearby wind farms and a photovoltaic field into the power grid by using the hydropower output channel. Firstly, the necessity and feasibility of joint dispatch of cascade hydropower stations, wind power and photovoltaics were analyzed, and the cascade water-wind-light joint peak shaving strategy was proposed. A short-term dispatch model with the minimum mean square error of the system remaining load as the objective function was constructed. Finally, the shrinkage factor and improved particle swarm algorithm optimization were used to solve the problem. Through the simulation, the wind and solar power stations and the cascade hydropower stations jointly participate in the peak shaving of the system, the peak shaving effect is better. The improved PSO has better optimization accuracy and convergence speed. It provides a reference for implementing the combined peak shaving strategy of the cascade hydropower station with surrounding wind and photovoltaic field in the future.

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张艳华,黄静梅,黄景光,等. 基于改进粒子群算法的梯级水风光短期调峰优化调度[J]. 科学技术与工程, 2022, 22(12): 4993-5000.
Zhang Yanhua, Huang Jingmei, Huang Jingguang, et al. Optimal Scheduling of Cascaded Hydro-Wind-Photovoltaic Short Term Peak Shaving Based on Improved Particle Swarm Optimization Algorithm[J]. Science Technology and Engineering,2022,22(12):4993-5000.

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  • 收稿日期:2021-08-19
  • 最后修改日期:2022-01-25
  • 录用日期:2021-12-03
  • 在线发布日期: 2022-05-07
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