Improved Multiobjective Particle Swarm Optimization for Environmental/Economic Dispatch Problem in Power System

2011 ◽  
pp. 49-56
Author(s):  
Yali Wu ◽  
Liqing Xu ◽  
Jingqian Xue
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Economic ◽  
Power System ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Economic Dispatch Problem

scholarly journals Perbandingan Metode Gaussian Particle Swarm Optimization (GPSO) dan Lagrange Multiplier pada Masalah Economic Dispatch

2012 ◽  
Vol 3 (1) ◽  
pp. 228
Author(s):  
Siti Komsiyah
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Power ◽  
Lagrange Multiplier ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Economic Planning ◽  
Electric Power System ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Economic Dispatch Problem

On electric power system operation, economic planning problem is one variable to take into account due to operational cost efficiency. Economic Dispatch problem of electric power generation is discussed in this study to manage the output division on several units based on the the required load demand, with minimum operating cost yet is able to satisfy equality and inequality constraint of all units and system. In this study the Economic Dispatch problem which has non linear cost function is solved using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving is inspired by swarm intelligent and probabilities theories. To analize its accuracy, the Economic Dispatch solution by GPSO method is compared with Lagrange Multiplier method. From the test result it is proved that GPSO method gives economic planning calculation better than Lagrange Multiplier does.

scholarly journals A Novel Parallel SPEA2 for Solving the Environmental-Economic Dispatch Problem: Competitive vs Cooperative Approach

2020 ◽  
Author(s):  
João Pedro Augusto Costa ◽  
Omar Andres Carmona Cortes ◽  
Osvaldo Ronald Saavedra
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Environmental Economic ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Pareto Frontier ◽  
Parallel Model ◽  
Swarm Optimization ◽  
Cooperative Approach ◽  
Economic Dispatch Problem

This paper aims to compare two different parallel approaches (cooperative and competitive) of the SPEA2 for solving the environmental-economic dispatch problem. The idea is to solve the problem by executing the SPEA2 algorithm along with three different meta-heuristics (Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution) to perform changes in the population. The different meta-heuristics work in parallel using two different approaches. The first one is the competitive approach, in which meta-heuristics compete for producing the best set of candidate solutions for solving the problem. Whereas, the cooperative approach selects the new population merging all individuals from all meta-heuristics, then selecting the solution set for the Pareto frontier. The proposal was implemented in C++ using MPI in a master-slave parallel model. Two  study cases were used: the first one with six generators and the second one with forty generators. Results showed that the cooperative approach presented the best Pareto frontier for the case of 40 generators.

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