Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem
Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem
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DOI：
10.1007/s00521-019-04151-7
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发表时间：
2019-04
影响因子：
6
通讯作者：
Y. A. Gherbi;F. Lakdja;Hamid Bouzeboudja;F. Z. Gherbi
中科院分区：
文献类型：
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作者：
Y. A. Gherbi;F. Lakdja;Hamid Bouzeboudja;F. Z. Gherbi
计算机和控制软件的发展促进了电网的创新。这种发展必然与几个方面有关：能源、经济、环境等。在控制和决策中引入人工智能软件技术对未来网络的研究和发展至关重要。本文研究了多准则优化的元启发式算法。这些标准正朝着经济/环境调度的方向发展，解决生产成本的影响和有毒气体的排放等相互竞争的目标。这需要某种形式的冲突解决来达成解决方案。这就是为什么我们需要有效的优化算法。萤火虫算法和蝙蝠算法是受自然界启发的两种最新的元启发式算法。本文对这两种方法进行了研究，并将其应用于求解约束条件下的多目标优化问题。在本工作的最后，提出了萤火虫算法和蝙蝠算法的杂交。这种杂交的目的是结合两种方法的优点，从而提高它们的性能。通过6台、10台和20台发电机的网络测试，验证了该方法的有效性；根据约束条件对多个功率需求进行测试；并考虑有源传输损耗的可变性。
The development of computers and control software has contributed to the innovation of electrical networks. This development is necessarily linked to several concerns: energy, economic, environmental, etc. The introduction of the techniques of artificial intelligence software in the control and decision is essential in research and in the development of tomorrow’s networks. This paper deals with multi-criteria optimization metaheuristics. These criteria are moving toward the economic/environmental dispatch that addresses the impact of the cost of production and the emission of toxic gases such as competing objectives. This requires some form of conflict resolution to reach a solution. That is why we need effective optimization algorithms. The firefly algorithm and bat algorithm are two recent metaheuristics inspired by nature. Both methods have been studied and adapted to solve our multi-objective optimization problem within the constraints. At the end of this work, the hybridization of the firefly algorithm and bat algorithm was proposed. The purpose of this hybridization is to combine the advantages of both methods and thus improve their performance. The effectiveness of this new method was demonstrated by applying it on different network tests of 6, 10, and 20 generators; testing with several power demands in accordance with constraints; and considering the variability of active transmission losses.