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首页> 外文期刊>Electric power systems research >Dynamic economic emission dispatch based on group search optimizer with multiple producers
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Dynamic economic emission dispatch based on group search optimizer with multiple producers

机译：基于具有多个生产者的组搜索优化器的动态经济排放分配

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摘要

This paper presents a new method for dynamic economic emission dispatch (DEED) of power systems, using a novel multi-objective evolutionary algorithm, group search optimizer with multiple producers (GSOMP) that includes a constraint handling scheme introduced to deal with complex constraints. The DEED is divided into 24 decomposed DEEDs, which are then solved hour by hour in the time sequence. A technique for order preference similar to an ideal solution (TOPSIS), is then developed to determine the final solution from the Pareto-optimal solutions considering a decision maker's preference. The performance of GSOMP has been evaluated on the DEEDs of the IEEE 30-bus and 118-bus systems, respectively, in comparison with those of multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm-II (NSGA-II). The simulation results show that DEED is well solved by the proposed method as a set of widely distributed Pareto-optimal solutions can be obtained and that GSOMP has better convergence performance than MOPSO and NSGA-ll and consumes much less time than NSGA-ll. All the NO_x, CO_2 and SO_2 are integrated into the emission objective function of the DEED, on which the solution obtained can have relatively low emission of each pollutant.
机译：本文提出了一种电力系统动态经济排放调度（DEED）的新方法，该方法采用了一种新颖的多目标进化算法，即具有多个生产者的组搜索优化器（GSOMP），其中包括了一种可处理复杂约束的约束处理方案。将DEED分为24个分解的DEED，然后按时间顺序逐小时求解。然后开发一种类似于理想解决方案（TOPSIS）的订单偏好技术，以考虑决策者的偏好从帕​​累托最优解决方案中确定最终解决方案。与多目标粒子群优化器（MOPSO）和非支配排序遗传算法II（NSGA-）相比，分别在IEEE 30总线和118总线系统的DEED上评估了GSOMP的性能。 II）。仿真结果表明，所提出的方法可以很好地解决DEED问题，因为可以获得一组分布广泛的Pareto最优解，并且GSOMP具有比MOPSO和NSGA-II更好的收敛性能，并且比NSGA-II花费更少的时间。所有的NO_x，CO_2和SO_2都集成到DEED的排放目标函数中，在该函数中，所获得的解决方案可以使每种污染物的排放量相对较低。

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