Sizing with Technical Indicators of Microgrids with Battery Energy Storage Systems: A Systematic Review

Andrea Vasconcelos, Amanda Monteiro (), Tatiane Costa, Ana Clara Rode, Manoel H. N. Marinho, Roberto Dias Filho and Alexandre M. A. Maciel
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Andrea Vasconcelos: Edson Mororo Moura Institute of Technology, Recife 51020-280, Brazil
Amanda Monteiro: Edson Mororo Moura Institute of Technology, Recife 51020-280, Brazil
Tatiane Costa: Edson Mororo Moura Institute of Technology, Recife 51020-280, Brazil
Ana Clara Rode: AES Brasil, R&D and Innovation, Sao Paulo 04578-000, Brazil
Manoel H. N. Marinho: Edson Mororo Moura Institute of Technology, Recife 51020-280, Brazil
Roberto Dias Filho: Edson Mororo Moura Institute of Technology, Recife 51020-280, Brazil
Alexandre M. A. Maciel: Department of Computer Engineering, Polytechnic College, University of Pernambuco, Recife 50720-001, Brazil

Energies, 2023, vol. 16, issue 24, 1-26

Abstract: Worldwide, governmental organizations are restructuring energy policies, making them cleaner, encouraging transformation and energy transition by integrating renewable sources, engaging in environmental preservation, and, notably, meeting the growing demand for sustainable energy models, such as solar and wind energy. In the electricity sector, reducing carbon emissions is crucial to facilitating the integration of microgrids (MGs) with renewable sources and Battery Energy Storage Systems (BESSs). This work constitutes a systematic review that thoroughly analyzes the sizing of MGs with BESSs. The unpredictability and variability of renewable sources justify the complexity of this analysis and the loads connected to the system. Additionally, the sizing of a BESS depends primarily on the application, battery technology, and the system’s energy demand. This review mapped and identified existing computational and optimization methodologies for structured sizing in technical indicators of an MG with a BESS based on articles published between 2017 and 2021. A protocol was defined in which articles were filtered in multiple stages, undergoing strategic refinements to arrive at the final articles to address the Research Questions (RQs). The final number of articles was 44, and within these, technical indicators related to the RQs were addressed, covering the most relevant works and comparing them technically, including how each explains the objective and result of their work. The rejected articles did not meet the criteria established by the defined protocol, such as exclusion criteria, quality criteria, and RQs. In conclusion, studies employing the integration of machine learning coupled with optimization techniques exhibit a significant contribution to results, as historical data can aid machine learning for data prediction.

Keywords: battery energy storage system; battery; renewable sources; microgrid; review; sizing; technical indicators (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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