Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Hajra Khan, Imran Fareed Nizami, Saeed Mian Qaisar, Asad Waqar, Moez Krichen and Abdulaziz Turki Almaktoom
Additional contact information
Hajra Khan: Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
Imran Fareed Nizami: Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
Saeed Mian Qaisar: Electrical and Computer Engineering Department, Effat University, Jeddah 22332, Saudi Arabia
Asad Waqar: Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
Moez Krichen: Department of Information Technology, Faculty of Computer Science and Information Technology (FCSIT), Al-Baha University, Al-Baha 65528, Saudi Arabia
Abdulaziz Turki Almaktoom: Supply Chain Management Department, Effat University, Jeddah 22332, Saudi Arabia

Energies, 2022, vol. 15, issue 21, 1-22

Abstract: Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.

Keywords: battery autonomy; battery size; feature selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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