大规模间歇式电源接入电网多目标鲁棒优化调度  

Multi-Objective Robust Optimized Scheduling of Power Grid Connected With Large-Scale Intermittent Power Sources

作　　者：谢鹏[1] 彭春华[1] 于蓉[1] 

机构地区：[1]华东交通大学电气与电子工程学院,江西省南昌市330013

出　　处：《电网技术》2014年第6期1479-1484,共6页Power System Technology

基　　金：国家自然科学基金项目(51167005;61165005)~~

摘　　要：随着间歇式电源接入电网的比例不断提升,间歇式电源出力的波动性和随机性会给电力系统优化调度带来较大影响。针对大规模间歇式电源出力的不确定性,提出将鲁棒优化理论引入到含大规模间歇式电源的电力系统优化调度中;构建了含间歇式电源的电力系统多目标动态鲁棒优化调度模型,以尽可能实现常规火力发电成本最低、污染气体排放量最少的综合优化目标;设计了一种新的多目标复合型微分进化算法,该算法通过将帕累托非劣排序、种群分割、复合微分进化和种群重组等一系列操作引入微分进化算法有效兼顾了微分进化过程中个体多样性与收敛速度。算例分析结果表明,提出的算法和模型能够很好地解决大规模间歇式电源接入电网多目标鲁棒优化调度问题,所得优化调度结果具有比较好的可靠性和经济性。With unceasing increase of grid-connected intermittent power sources,the fluctuation and randomness of the output of intermittent power sources bring negative influence to power grid optimized scheduling.In allusion to the uncertainty of the output of large-scale intermittent power sources it is proposed to leading robust optimization theory into the optimized scheduling of power grid connected with large-scale intermittent power sources to construct a dynamic multi-objective robust optimized scheduling model for power grid with intermittent power sources,and taking as low as possible generating costs of conventional thermal power plants and the least pollution emissions for the synthetical optimization object a new multi-objective compound differential evolution algorithm is designed,in which a series of operations such as Pareto non-dominated sorting,population partition,compound differential evolution and population restructuring are led into differential evolution algorithm,thus both individual diversity and convergence speed during the differential evolution are effectively considered.Case study results show that the proposed algorithm and model can well cope with the multi-objective robust optimized scheduling for power grid connected with large-scale intermittent power sources and the obtained optimized scheduling result possesses better reliability and economy.

关 键 词：间歇式电源 鲁棒优化 多目标优化调度 多目标复合型微分进化算法 帕累托最优前沿 

分 类 号：TM721[电气工程—电力系统及自动化]