Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm

Julian Garcia-Guarin, Diego Rodriguez, David Alvarez, Sergio Rivera, Camilo Cortes, Alejandra Guzman, Arturo Bretas, Julio Romero Aguero and Newton Bretas
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Julian Garcia-Guarin: Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
Diego Rodriguez: Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
David Alvarez: Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
Sergio Rivera: Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
Camilo Cortes: Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
Alejandra Guzman: Electrical Engineering Department, Universidad Nacional de Colombia, Bogotá 110111, Colombia
Arturo Bretas: Electrical Engineering Department, University of Florida, Gainesville, FL 32601, USA
Julio Romero Aguero: Quanta Technology, Houston, TX 77056, USA
Newton Bretas: Department of Electrical and Computer Engineering, University of Sao Paulo, São Paulo 12652, Brazil

Energies, 2019, vol. 12, issue 16, 1-13

Abstract: Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

Keywords: operation in uncertain environments; energy metaheuristic optimization; smart microgrid; VNS-DEEPSO algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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