Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO2 Emissions Using the Antlion Optimization Algorithm

• Published in: Arabian journal for science and engineering (2011) Vol. 46; no. 10; pp. 9995 - 10006
• Main Authors: Ocampo-Toro, J. A., Garzon-Rivera, O. D., Grisales-Noreña, L. F., Montoya-Giraldo, O. D., Gil-González, W.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2021; Springer Nature B.V
• Subjects: Alternative energy sources; Approximation; Carbon dioxide; Diesel generators; Direct current; Distributed generation; Energy costs; Engineering; Generators; Genetic algorithms; Humanities and Social Sciences; Methodology; multidisciplinary; Numerical methods; Optimization techniques; Particle swarm optimization; Power dispatch; Production costs; Renewable energy sources; Research Article-Electrical Engineering; Science; Solar energy; Wind power; Metaheuristic optimization; emission; Economic power dispatch; Energy cost reduction; Antlion optimization; Optimal power flow; Direct current network; Master–slave algorithm
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-021-05831-0

In this study, we present a master–slave methodology to solve the problem of optimal power dispatch in a direct current (DC) microgrid. In the master stage, the Antlion Optimization (ALO) method solves the problem of power dispatch by the Distributed Generators (DGs); in the slave stage, a numerical method based on successive approximations (SA) evaluates the load flows required by the potential solutions proposed by the ALO technique. The objective functions in this paper are the minimization of energy production costs and the reduction of CO 2 emissions produced by the diesel generators in the microgrid. To favor energy efficiency and have a lower negative impact on the environment, the DC microgrids under study here include three DGs (one diesel generator and two generators based on renewable energy sources, i.e., solar energy and wind power) and a slack bus connected to a public electrical grid. The effectiveness of the proposed ALO–SA methodology was tested in the 21- and 69-bus test systems. We used three other optimization techniques to compare methods in the master stage: particle swarm optimization, continuous genetic algorithm, and black hole optimization. Additionally, we combined SA with every method to solve the load flow problem in the slave stage. The results show that, among the methods analyzed in this study, the proposed ALO–AS methodology achieves the best performance in terms of lower energy production costs, less CO 2 emissions, and shorter computational processing times. All the simulations were performed in MATLAB.