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

Hui-Juan Zhang, Yi-Bo Feng and Kuo-Ping Lin
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Hui-Juan Zhang: 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
Yi-Bo Feng: 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
Kuo-Ping Lin: Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan

Sustainability, 2018, vol. 10, issue 8, 1-17

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.

Keywords: sustainable; individual greedy strategy; microgrid capacity optimization; multi-population difference evolution algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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