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Multi-objective Optimal Load Distribution Based on Decomposition-Coordination Method of Large Scale Systems

机译:基于大规模系统分解协调方法的多目标最优载荷分布

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This paper divides a model of complicated system which includes numbers of fossil-fueled power plants into a two-tier structure model with coupled subsystems, using decomposition-coordination method of large scale systems. The method consists of Lagrangian relaxation, sub-gradient algorithm and particle swarm optimization (PSO). First, an optimal load distribution model on economy and pollution is proposed. Second, the model is decomposed via Lagrangian relaxation, and PSO is adopted to obtain the optimal solution of each subsystem. Third, the sub-gradient algorithm is used to update the Lagrangian multiplier to coordinate the coupling relation among subsystems. Finally, through iteration of sub-gradient algorithm and PSO, the simulation example realizes the goal that the power plants can obtain the maximum economic efficiency in the case of the minimum pollutant emissions.
机译:本文划分了一种复杂系统的模型,该模型包括使用大规模系统的分解协调方法,包括耦合子系统的双层结构模型中的化石燃料发电厂的数量。该方法包括拉格朗日放松,子梯度算法和粒子群优化(PSO)。首先,提出了一种经济和污染的最佳载荷分配模型。其次,该模型通过拉格朗日放松分解,采用PSO获得每个子系统的最佳解决方案。第三,子梯度算法用于更新拉格朗日乘法器以协调子系统之间的耦合关系。最后,通过迭代亚梯度算法和PSO,模拟示例实现了发电厂可以在最小污染物排放的情况下获得最大经济效率的目标。

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