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

Author

Listed:
  • Hajra Khan

    (Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Imran Fareed Nizami

    (Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Saeed Mian Qaisar

    (Electrical and Computer Engineering Department, Effat University, Jeddah 22332, Saudi Arabia
    Communication and Signal Processing Lab, Energy and Technology Center, Effat University, Jeddah 22332, Saudi Arabia
    These authors contributed equally to this work.)

  • Asad Waqar

    (Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Hajra Khan & Imran Fareed Nizami & Saeed Mian Qaisar & Asad Waqar & Moez Krichen & Abdulaziz Turki Almaktoom, 2022. "Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches," Energies, MDPI, vol. 15(21), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7865-:d:951235
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    References listed on IDEAS

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    1. Chuang Sun & An Qu & Jun Zhang & Qiyang Shi & Zhenhong Jia, 2022. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Variational Mode Decomposition and Machine Learning Algorithm," Energies, MDPI, vol. 16(1), pages 1-15, December.
    2. Moez Krichen & Yasir Basheer & Saeed Mian Qaisar & Asad Waqar, 2023. "A Survey on Energy Storage: Techniques and Challenges," Energies, MDPI, vol. 16(5), pages 1-29, February.
    3. Michał Gocki & Agnieszka Jakubowska-Ciszek & Piotr Pruski, 2022. "Comparative Analysis of a New Class of Symmetric and Asymmetric Supercapacitors Constructed on the Basis of ITO Collectors," Energies, MDPI, vol. 16(1), pages 1-16, December.

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