Abstract
The essential goal of multi-area economic environmental dispatch (MAEED) is to determine the optimum power generation schedule of each unit and power transfer between the areas in order to minimize fuel costs and pollutant emissions, when the generation, power balance and tie-line limits are satisfied. This paper focuses on developing multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem, of which the goal is to simultaneously minimize the total fuel cost and emission considering valve point effects and multi-fuel options. The proposed MOSSA combines squirrel search algorithm along with Pareto-dominance theory to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to preserve the distribution diversity of Pareto-optimal solutions as the evolution continues. In addition, a fuzzy decision maker is used to select the best compromised solution from the obtained Pareto frontiers. Furthermore, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach with price penalty factors, artificial bee colony and exchange market algorithm. Different case studies are performed on 10-unit with three-area system, 40-unit with four-area system and 140-unit real Korean power system considering valve point effects and multi-fuel options which testify the supremacy of the suggested approach. The comparisons with state-of-the-art approaches suggest that MOSSA can generate more competitive trade-off solutions for solving the MAEED problems.
Highlights
Economic load dispatch (ELD) performs a crucial function in operation planning of modern power systems
The main motivation of this paper is to propose and develop a new multi-objective squirrel search algorithm (MOSSA) that has its own feasibility and performance capacity to determine the Pareto-optimal solutions of multi-area economic environmental dispatch (MAEED) problems in power systems
If the number of non-dominated squirrel individuals exceeds the size of depository, a measure known as crowding distance (CD) is determined for all individuals in the depository
Summary
PLi r1, r2 and r3 multi-objective squirrel search algorithm non-dominated sorting genetic algorithm opposition-based krill herd algorithm Pareto optimal frontiers prohibited operating zone particle swarm optimization real-coded genetic algorithm ratio to non-dominated index shuffled differential evolution spacing metric squirrel search algorithm teaching learning-based optimization valve point loading weighted sum approach cost coefficients of jth generation unit in ith area line loss coefficients skimming separation drag power mean value of edi cost coefficients of the VPL effect of generator j in area i. Βij, γij ηij, δij ρ β current iteration number tie line power stream from area i to area z location of squirrel individual that reached the hickory tree number of non-dominated solutions in population X emission coefficients of generator j in area i emission coefficients of the VPL effect of jth generation unit in ith area density of air constant
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