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.
A comparative study of particle swarm optimization and genetic algorithm
