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Open AccessArticle

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

1
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, China
2
Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(8), 2694; https://doi.org/10.3390/su10082694
Received: 4 July 2018 / Revised: 20 July 2018 / Accepted: 26 July 2018 / Published: 1 August 2018
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. View Full-Text
Keywords: sustainable; individual greedy strategy; microgrid capacity optimization; multi-population difference evolution algorithm sustainable; individual greedy strategy; microgrid capacity optimization; multi-population difference evolution algorithm
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Zhang, H.-J.; Feng, Y.-B.; Lin, K.-P. Application of Multi-Species Differential Evolution Algorithm in Sustainable Microgrid Model. Sustainability 2018, 10, 2694.

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