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首页> 外文期刊>Research journal of applied science, engineering and technology >Risk Reserve Constrained Economic Dispatch of Wind Power Penetrated Power System Based on UPSMC and SAGA Algorithms
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Risk Reserve Constrained Economic Dispatch of Wind Power Penetrated Power System Based on UPSMC and SAGA Algorithms

机译:基于UPSMC和SAGA算法的风电渗透电力系统风险储备约束经济调度

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摘要

A short-term Economic Dispatch (ED) model with risk constraint for wind penetrated power systems was built to face the challenge of scheduling spinning reserves brought from wind energies. The proposed model utilizes the probability of spinning reserve shortage as measurement of system risk and evaluates the risk by an Unequal Probabilities Sampling based Monte Carlo (UPSMC) method. A Genetic Algorithm (GA) improved with Simulated Annealing (SA) strategy is presented as SAGA to solve the problem. By comparing simulation results under different wind penetrations and risk constraints, coal consumptions will not always decrease with wind penetration and risk constraint but for most times. In addition, unit risk benefit has a trend to increase with wind penetration and decrease with risk constraint while contribution of unit wind generation has the contrary character. Simulation results also show that the proposed sampling method could improve the sampling efficiency and the SAGA method had better performance than traditional GA.
机译:建立了具有风险约束的风电系统短期经济调度(ED)模型,以应对调度由风能带来的旋转储备的挑战。该模型利用旋转储备不足的概率作为系统风险的度量,并通过基于蒙特卡洛(UPSMC)的不等概率抽样方法评估了风险。为了解决该问题,提出了一种采用模拟退火(SA)策略改进的遗传算法(GA)作为SAGA。通过比较不同风速和风险约束条件下的模拟结果,煤炭消耗量不会总是随风速和风险约束条件而降低,而是在大多数情况下会降低。此外,单位风险收益具有随风的渗透而增加而随着风险约束而减小的趋势,而单位风的产生则具有相反的特征。仿真结果还表明,所提出的采样方法可以提高采样效率,并且SAGA方法具有比传统GA更好的性能。

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