基于GPU的拉格朗日乘子优化动态经济调度

A GPU-based Lagrange Multiplier Optimization for Dynamic Economic Dispatch

针对在某种程度上,通过调整电力系统的短期和超短期调度可以减少限电的问题,建立了数学模型动态经济调度(DED)。由于调整计算需要在短时间完成,因此引入并行计算技术来加速其拉格朗日乘子优化的DED求解过程,并使用图形处理器单元(GPU)以相同的指令线程并行执行多组数据,最后结合IEEE可靠性系统的案例,检验其模型和算法的有效性与效率。

To some extent,the curtailment could be reduced by adjust short-term and ultra-short-term dispatching of power systems.In this paper»a mathematic model dynamic economic dispatch(DED)is set up.Computation of the adjustment is needed to be completed in very short time.Therefore,the technology of heterogeneous computing is introduced to accelerate the DED solving process of Lagrange Multiplier optimization,and graphic processor unit(GPU)is used to execute parallel computing multiple data with the same instruction threads.A case based on IEEE reliability system is used to test effectiveness and efficiency of the model and the algorithm.