RESEARCH ARTICLE


Self-adaptive Genetic Algorithm and Fuzzy Decision Based Multi-objective Optimization in Microgrid with DGs



Shanyi Xie1, *, Ruicong Zhai1, Xianhu Liu2, Baoguo Li2, Kai Long3, Qian Ai4
1 Electric Power Research Institute of Guangdong Grid Company, China
2 Guangdong Grid Company of Jiangmen Power Supply Bureau, China
3 Shanghai Huali Software System Company Limited, China
4 Shanghai Jiao Tong University, China


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©Xie et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Electric Power Research Institute of Guangdong Grid Company, China; E-mail: xieshanyi@126.com


Abstract

Microgrid is one practical infrastructure to integrate Distributed Generations (DGs) and local loads. Its optimal operating strategy has aroused great attention in recent years. This paper mainly focuses on the multi-objective optimization of DGs in microgrid by using self-adaptive genetic algorithm (GA) and fuzzy decision. Five objective functions are taken into account comprising voltage offset, transmission loss, construction cost, purchase cost and the environmental cost. In the algorithm, self-adaptation in population size, mutation probability, selection and standardization of objective functions is developed to enhance the speed and efficiency of the algorithm. Moreover, fuzzy decision is applied to determine the final solution. Simulation results show this algorithm can effectively find the optimal solution and improve the real-time control of microgrid, which implies the possibility of potential applications in microgrid energy management system.

Keywords: Distributed Generation, Fuzzy decision, Multi-objective optimization, Pareto solution, Self-adaptation genetic algorithm.