Economic Power Dispatch with Emission Constraint and Valve Point Loading Effect Using Moth Flame Optimization Algorithm

Abstract

Economic dispatch (ED) is an important optimization task in power system which involves determination of the optimal combination of power outputs for all generating units which will minimize the total fuel cost while satisfying load constraints. There are three criteria in solving the economic load dispatch problem. They are minimizing the total generator operating cost, total emission cost and multi-objective (cost and emission). This paper proposes Moth Flam Optimization (MFO) algorithm for solving the economic dispatch problem with valve point effects and emissions. It determines the optimal generation schedule of generating units by minimizing three criteria. Two test systems consisting of 6 generating units and isolated microgrid have been used to illustrate the effectiveness of the proposed MFO method. The results obtained from MFO is compared with different algorithms. The results show better global convergence and also gives good optimum solution by reducing system generation cost and emission cost.

Solving practical economic load dispatch problem using crow search algorithm

“…Thus many of the conventional methods are not efficient to find the solution of the ELD problem, especially when the practical conditions are considered. To overcome the limitations of the classical methods, various Evolutionary Algorithms (EAs) have been implemented to solve the ELD problem such as Social Spider Algorithm (SSA) [4], Genetic Algorithm (GA) [7], Chaotic Bat Algorithm (CBA) [8], Elephant Herding Optimization (EHO) [9], Backtracking Search Algorithm (BSA) [10], Moth Flame Algorithm (MFA) [11], etc. Most of these algorithms have fast convergence characteristics and high precision.…”

Assessment of hurricane versus sine-cosine optimization algorithms for economic/ecological emissions load dispatch problem

“…The ecological emission load dispatch (EELD) problem aims to consider the environmental impacts, the resulted emission, as well as FC. The mathematical formulation of the EELD problem has non‐convex objective functions and nonlinear constraints . The nonlinearities and nonconvex characteristics lead to undesired and slow convergence solutions that are pole on local optimum for any mathematical method …”

“…The mathematical formulation of the EELD problem has non-convex objective functions and nonlinear constraints. [2][3][4] The nonlinearities and nonconvex characteristics lead to undesired and slow convergence solutions that are pole on local optimum for any mathematical method. 2 In this line, power system planners and operators seek to minimize both EELD objectives simultaneously using any developed optimizer.…”

“…The previous EELD formulation ignores network constraints. 1,2 Furthermore, number of recent search-based optimization algorithms have been introduced to solve the EELD problem like real coded genetic algorithm, 7 niched Pareto genetic algorithm (NPGA), 8 non-dominated sorting genetic algorithm (NSGA), 9 multi-objective differential evolution (MODE), 10 moth-flame optimization algorithm, 11 crow search optimizer, 12 multiobjective particle swarm optimization (MOPSO), 13,14 seeker optimization algorithm, 15 and fruit fly optimizer. 16 Hybrid methods are developed for solving the economic dispatch (ED) problem such as fuzzy linear programming as in El-Ela et al 17,18 for predicted and emergency operation of power systems.…”

“…The economic power dispatch problem aims at covering the required power demand at minimum fuel costs (FC) while sustaining constraints of all committed generators . In Elsakaan, Yang et al, and Sayah and Hamouda, the economic dispatch problem was solved considering the non‐convex objective function with valve point effects. In the earlier economic dispatch work, the main objective aims at reducing of the overall FCs ignoring the system produced emission level.…”