Search

In this paper, a technique based on nondominated sorting genetic algorithm-II (NSGA-II) is presented for solving the service restoration problem in an electric power distribution system. Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multiobjective, multiconstraint optimization problem. In contrast to the conventional genetic-algorithm (GA)-based approach, this approach does not require weighting factors for the conversion of such a multiobjective optimization problem into an equivalent single objective function optimization problem. In this work, various practical distribution system operation issues, such as the presence of priority customers, presence of remotely controlled, as well as manually controlled switches, etc. have also been considered. Based on the simulation results on four different distribution systems, the performance of the NSGA-II-based scheme has been found to be significantly better than that of a conventional GA-based method. Besides, to reduce the software runtime of the NSGA-II algorithm, a faster version of NSGA-II has also been implemented. [ABSTRACT FROM AUTHOR]

Abstract: This paper integrated the ideas of Direct Search and Signal-to-Noise Ratio (SNR) to develop a Novel Direct Search (NDS) method for solving the non-convex economic dispatch problems. NDS consists of three stages: Direct Search (DS), Global SNR (GSNR) and Marginal Compensation (MC) stages. DS provides a basic solution. GSNR searches the point with optimization strategy. MC fulfills the power balance requirement. With NDS, the infinite solution space becomes finite. Furthermore, a same optimum solution can be repeatedly reached. Effectiveness of NDS is demonstrated with three examples and the solutions were compared with previously published results. Test results show that the proposed method is simple, robust, and more effective than many other previously developed algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Recently, a new class of stochastic optimization algorithm called SOMA (self-organizing migrating algorithm) was proposed in the literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a “social environment”. This paper proposes a SOMA approach combined with a cultural algorithm (CSOMA) technique based on normative knowledge as an alternative method to solving the economic load dispatch problem of thermal generators with the valve-point effect. The classical SOMA and CSOMA approaches are validated for two test systems consisting of 13 and 40 thermal generators whose non-smooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of the CSOMA present best results when compared with results of others optimization methods found in the literature in solving load dispatch problems with the valve-point effect. [ABSTRACT FROM AUTHOR]