Improved time varying inertia weight PSO for solved economic load dispatch with subsidies and wind power effects

Abstract

This article presents a new approach to economic load dispatch (ELD) problems by the considering the cost functions, impact renewable energy as wind turbin and subsidies. Economic dispatch is the short-term determination of the optimal output of a number of electricity generation facilities, to meet the system load, at the lowest possible cost, subject to transmission and operational constraints. The main goal in the deregulated system is subsidies and analysis performance on government to minimize the total fuel cost while satisfying the load demand and operational constraints. The practical ELD problems have nonsmooth cost functions with equality and inequality constraints, which make the problem of finding the global optimum difficult when using any mathematical approaches. Accordingly, particle swarm optimization with time-varying inertia weight (PSO-TVIW) used for solving this problem. The effectiveness of the proposed strategy is applied over real-world engineering problem and highly constrained. Obtained results indicate that PSO-TVIW can successfully solve this problem. V C 2014 Wiley Periodicals, Inc. Complexity 21: 40-49, 2016

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

“…Moreover, not only dynamics of the power stage but also, if required, dynamics associated with the electric power generation should be considered. This is, by itself, an important current research topic [11][12][13][14][15][16][17][18][19].…”

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

“…The PSO algorithm was first introduced by Kennedy and Eberhart, [32][33][34] which is inspired from the conduct of bird swarms via a humble concept of social collaboration, and it was quickly and effectively utilized in the context of the population-based optimization methods. 32,33,[35][36][37][38][39][40][41][42] In a problem with N particles, in which a particle is a probable answer of a practical problem in a seeking area with D dimension, velocity/location of particle i in the jth dimension at the tth step time is updated using the PSO method by…”

“…The expressed criteria of the PSO equations in (58) and (59), which are mostly due to promoting lack of diversity of particles, lead to untimely convergence to the local minimum. 32,33 So, the diversity rise is regarded as a good manner of scramming from local minimum, although sic politic mostly gives growth to slower convergence to optimum answer. 32,33 Most of the PSO variables are suggested for increasing the diversity, lacking the cooperation of the ordinary rapid convergence of the PSO method.…”

An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

“…Computational intelligence (CI) theories such as evolutionary algorithms [22,23], artificial neural networks [24], cognitive map analysis [25], Physarum solver [26][27][28], fuzzy sets [29][30][31], belief function [32][33][34], PSO [35][36][37], and so on [38], have been widely used to cope the complex problems including the permutation flow shop problem [39], supply chain network [40,41], traveling salesman problem [42], pattern recognition [43][44][45][46], power system [47], product design and manufacturing [48], and so on [49][50][51][52]. Recently, based on this progress in CI, many nature inspired approaches have been proposed to solve test selection optimization problem, such as the greedy strategy [53], the genetic algorithm [54,55], the evolutionary algorithm [56,57], and so on [58].…”