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Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

机译:考虑风能和交互式负荷的电力系统多目标最优调度

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style="text-align:justify;"> With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.
机译:style =“ text-align:justify;”>随着可再生能源的快速大规模发展,新能源电力运输或消费的缺乏以及电网调峰能力的不足变得越来越严重。针对严重的风电削减问题,在传统的日前调度模型上研究了交互式负荷的特性,以减轻风电波动的影响。建立了具有最小运行成本和功率损耗的多目标最优调度模型。当发电侧和需求侧都参与资源分配时,可获得最佳的潮流分配。应用量子粒子群算法(QPSO)将多目标优化问题转化为单目标优化问题。 IEEE 30总线系统的仿真结果证明,该方法可以有效降低运营成本和电网损耗,同时增加了风能的消耗。

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