Open AccessDifferential evolution algorithm based on PCA to determine the objective function to optimize the configuration of microgrid
Author(s) -
Yun-Hu Wang,
Ruijuan Xu,
Qing Duan,
Guobao Zhang,
Yongming Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2003/1/012015
Subject(s) - microgrid , mathematical optimization , stability (learning theory) , computer science , pareto principle , multi objective optimization , grid , principal component analysis , set (abstract data type) , pareto optimal , basis (linear algebra) , function (biology) , differential evolution , component (thermodynamics) , algorithm , mathematics , artificial intelligence , machine learning , evolutionary biology , biology , physics , geometry , thermodynamics , programming language
Recently, microgrids are increasingly used in our lives. The purpose of this paper is to solve the uncertainties of multi-objective decision-making and the instability of algorithms in the optimal configuration of traditional microgrids. This paper proposed a differential evolution algorithm based on PCA to determine the objective function. Firstly, a multi-objective optimization model of the micro-grid was established on the basis of fully considering the economy, environmental protection and stability of the micro-grid. Next, the first principal component of the multi-objective was extracted based on PCA combining innovatively the Pareto optimal solution set, and the multi-objective optimization problem was transformed into a single-objective optimization problem. Finally, the experimental simulation result shows that the algorithm proposed in this paper can comprehensively consider the economy, environmental protection and stability of the microgrid on the basis of ensuring the efficiency and stability of the algorithm to realize the optimized configuration of the microgrid.