Environmental/economic power dispatch using multiobjective evolutionary algorithms

By Mohammad Ali Abido. IEEE Power Engineering Society General Meeting, 2003

828 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bc9efe24888694edb7c8267011538e1376ab5fac

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Citations in the corpus

Economic-emission dispatch problem: A semi-definite programming approach (2014, methodology): “More recent approaches use evolutionary algorithms [14, 15] including particle swarm optimization (PSO) method [16, 17], genetic algorithms (GA) [9], bacterial foraging (BF) method [18, 19] and differential evolution [20].”
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch (2008, methodology): “Nondominated sorting genetic algorithm (NSGA) [13], niched Pareto genetic algorithm (NPGA) [14], strength Pareto evolutionary algorithm (SPEA) [2], multiobjective stochastic search technique (MOSST) [15], Tabu Search [16], NSGA II [17], [18], multiobjective particle swarm optimization (MOPSO) [20], ”
Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study (2016, background): “The emission rate (kg/h) of atmospheric pollutants such as sulphur oxides SO2, carbon oxides CO2, and nitrogen oxides NO and NO2 can be modeled separately; the total emission of the ith unit can be expressed as [66]: Ei(P) = i + ςiP + iP(2) + ie iP (14) where i, ςi, i, i and i are nonnegative coeffi”
The Exact Solution of the Environmental/Economic Dispatch Problem (2012, neutral): “cations to solve the EED, [7]–[9] and [10] applied several algo-”
NSGA and SPEA Applied to Multiobjective Design of Power Distribution Systems (2006, background): “This type of algorithms has obtained very good results recently in applications to complex problems in electrical engineering [20]–[22].”