杨亦玘, 郑鹏远, 毛冉, 秦海杰, 王雅琳. 不确定裕度分段量化和弃风弃光分段惩罚的孤岛型微电网经济调度算法[J]. 现代电力, 2023, 40(1): 73-81. DOI: 10.19725/j.cnki.1007-2322.2021.0238
引用本文: 杨亦玘, 郑鹏远, 毛冉, 秦海杰, 王雅琳. 不确定裕度分段量化和弃风弃光分段惩罚的孤岛型微电网经济调度算法[J]. 现代电力, 2023, 40(1): 73-81. DOI: 10.19725/j.cnki.1007-2322.2021.0238
YANG Yiqi, ZHENG Pengyuan, MAO Ran, QIN Haijie, WANG Yalin. Islanded Microgrid Economic Dispatch Based on Segmented Quantization of Uncertainty Margin and Segmented Penalty of Curtailed Wind and Solar Power[J]. Modern Electric Power, 2023, 40(1): 73-81. DOI: 10.19725/j.cnki.1007-2322.2021.0238
Citation: YANG Yiqi, ZHENG Pengyuan, MAO Ran, QIN Haijie, WANG Yalin. Islanded Microgrid Economic Dispatch Based on Segmented Quantization of Uncertainty Margin and Segmented Penalty of Curtailed Wind and Solar Power[J]. Modern Electric Power, 2023, 40(1): 73-81. DOI: 10.19725/j.cnki.1007-2322.2021.0238

不确定裕度分段量化和弃风弃光分段惩罚的孤岛型微电网经济调度算法

Islanded Microgrid Economic Dispatch Based on Segmented Quantization of Uncertainty Margin and Segmented Penalty of Curtailed Wind and Solar Power

  • 摘要: 针对孤岛微电网在运行过程中受新能源出力及负荷功率不确定性的影响,提出基于不确定裕度分段量化和弃风弃光分段惩罚的孤岛型微电网经济调度算法。日前计划阶段,利用基于不确定裕度分段量化的不确定集,并采用列约束生成算法,将原问题分为主问题和子问题进行交互迭代求解,得到在“最恶劣场景”下的最优经济调度方案。日内调度阶段,保持日前计划得到的储能系统出力,对传统能源出力、需求响应负荷、新能源弃风弃光量进行调整优化。最终,通过算例仿真验证了该算法在不同的场景下的经济性和鲁棒性。

     

    Abstract: In view of the fact that during its operation the islanded microgrid was affected by the output of new energy and the uncertainty of load power, an economic dispatching algorithm for island microgrid was proposed based on the segmented quantization of margin of uncertainty and the penalty of curtailed wind and photovoltaic (abbr. PV) power. Firstly, in the day-ahead planning stage, utilizing the uncertain set based on the segmented quantization of margin of uncertainty and adopting the column constraint generation algorithm, the primal problem was divided into the main problem and the sub-problem to conduct the interactive iterative solution. Thereby, the most optimal economic scheduling scheme under the "worst case scenario" was obtained. Secondly, in the intra-day scheduling stage, the output of energy storage system obtained from the day-ahead planning was kept, and the output of traditional energy, the demand response load and the curtailed quantity of wind and PV power in the new energy were adjusted and optimized. Finally, both economy and robustness of the proposed algorithm under different scenarios are verified by example simulation.

     

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