Optimal Integration of Distributed Generators into DC Microgrids Using a Hybrid Methodology: Genetic and Vortex Search Algorithms

This paper addresses the problem of optimal location and sizing of distributed generators (DGs) in direct current (DC) grids. To solve it, we propose an optimization approach with an objective function that aims to reduce power losses due to energy transport, while considering all the constraints that represent DC grids in a distributed generation environment. For the mathematical formulation of the problem, we used a mixed-integer nonlinear programming (MINLP) model, which allowed us to evaluate the impact of all possible configurations (i.e., location and size of DGs in the DC network) on the objective function and the constraints. The solution method proposed here is a master–slave strategy that implements a hybrid solution methodology that combines a genetic algorithm (GA) and the vortex search algorithm (VSA). The GA is in charge of solving the location problem in the master stage, and the VSA is responsible for sizing the DGs in the slave stage. To evaluate the effectiveness and robustness of the proposed GA/VSA methodology, we employed two test systems (i.e., 21 and 69 buses) considering a maximum penetration of distributed generation equal to 40% of the power generated by the slack buses. Furthermore, we also implemented nine other hybrid methodologies based on metaheuristic techniques (proposed in the literature for solving the problem addressed here) to make comparisons. All the solution methods used and proposed in this paper are based on sequential programming to avoid the need for specialized software and thus reduce the complexity and cost of the solutions. The effectiveness of the proposed solution was evaluated in two scenarios: (1) peak power demand and (2) variation in power generation and demand associated with photovoltaic generation and user demand in Medellín, Colombia. The results demonstrate that the GA/VSA methodology achieved the best results in terms of solution quality and processing times in all the test scenarios proposed in this study. © 2022, King Fahd University of Petroleum & Minerals

Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm

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. © 2021, King Fahd University of Petroleum & Minerals.Ocampo-Toro, J. A., Garzon-Rivera, O. D., Grisales-Noreña, L. F., Montoya-Giraldo, O. D., & Gil-González, W. (2021). Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm. Arabian Journal for Science and Engineering, 46(10), 9995-10006