A new optimization algorithm for multi-objective Economic/Emission Dispatch

• Published in: International journal of electrical power & energy systems 2013-03, Vol.46, p.283-293
• Main Authors: Niknam, Taher, Mojarrad, Hasan Doagou, Firouzi, Bahman Bahmani
• Format: Article
• Language: English
• Subjects: Applied sciences; Electrical engineering. Electrical power engineering; Electrical machines; Electrical power engineering; Exact sciences and technology; Interactive fuzzy safety method; Miscellaneous; Multi-objective optimization; Operation. Load control. Reliability; Power networks and lines; Regulation and control; Tribe-Modified Differential Evolution (Tribe-MDE)
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2012.10.001

► Present an interactive Tribe-MDE algorithm to solve the multi-objective EED problem. ► Consider generator valve-point loading effects. ► Propose a modified DE algorithm (MDE) based on self-adaptive parameter control are applied. This paper presents an innovative Tribe-Modified Differential Evolution (Tribe-MDE) for solving multi-objective Environmental/Economic Dispatch (EED) problems. By using this method the multi-objective problem will be changed into a mini-max problem on this first stage and then will be solved using the Tirbe-MDE algorithm. The operator, a person who is competent with respect to the problem, can initiate all the necessary actions to determine an optimal solution. Because of the fact that all of Pareto obtained solutions are of the same level of preference an operator will be able to affect his/her opinions with respect to different conflicting objectives. Using this methodology the operator can achieve the optimal solution. The DE algorithm is an advanced stochastic method that can prepare the necessary preliminaries for an operator to solve the EED problem. DE method has several advantages including its few control variables, local searching capability, fast results, easy using process and simple structure and using the control variables logically has a significant effect on the results. The subject of this paper is studying the modified DE algorithm which is founded on self-adaptive control. In this method a diversity-preserving method is used to solve the premature convergence problem and for proving the effectiveness of this method in solving EED problems the presented algorithms is applied on different systems.