Application of Multi-Species Differential Evolution Algorithm in Sustainable Microgrid Model

Abstract

The safety and stability of microgrid (MG) operations are closely related to the capacity of distributed energy resources. A conventional MG model usually adopts investment cost as an objective function. Recently, the issue of environmental protection has been gradually emphasized. Therefore, the objective function of the proposed sustainable microgrid (SMG) model in this study considers the investment cost and environmental protective cost and the decision variable is the capacity of the distributed power. Moreover, weather and electric power load data from the National Centers for Environmental Information database (2010) were analyzed in Matlab program for the case study of Alabaster city, United States of America (USA). For the sake of a stable and economical SMG operation, this study also attempts to use a multi-objective capacity optimal model for effectively solving SMG under a multi-population differential evolution (MPDE) algorithm with dominant population (DP), which can improve the convergence speed in an SMG model. At the same time, considering that different scheduling strategies will also affect the optimization results, two strategies are proposed for the priority order of distributed generation sources. The optimization results under the two scheduling strategies show that the validation of the MPDE algorithm in SMG capacity optimization problems can economize investment costs and enable an environmentally friendly power supply.

A comparative study for two Novel Optimization Algorithms used to solve Microgrid Energy Management problem considering Energy Storage System

“…Li et al proposed a revolutionary Time-of-Use (TOU) pricing model-based Demand Response (DR) program to obtain optimal EMS of an MG utilizing an Improved Memetic Algorithm (IMA) [16]. In [17], authors used DE in order to deal with the MG capacity optimization issues in a gridconnected MG. The load shedding was optimally planned to use Fuzzy Adaptive Particle Swarm Optimization (FAPSO) in [18] in an islanded MG considering voltage stability index.…”

Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

“…On the contrary, another approach is to find optimal locations and sizes by optimizing several objective functions simultaneously, based on Pareto optimal fronts that yield to non-dominated solutions [15,24,29,30]. Sheng et al [15] propose the use of an improved version of the well-known non-dominated sorting genetic algorithm II (iNSGA-II) to solve the multi-objective optimization with consideration of line loss, voltage regulation and voltage stability margin.…”