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Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept

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  • Nisitha Padmawansa

    (Department of Electrical and Electronic Engineering, Auckland University of Technology, WS Building, 34 St. Paul Street, Auckland 1142, New Zealand)

  • Kosala Gunawardane

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Samaneh Madanian

    (Department of Computer Science and Software Engineering, Auckland University of Technology, WS Building, 34 St. Paul Street, Auckland 1142, New Zealand)

  • Amanullah Maung Than Oo

    (School of Engineering, Macquarie University, Sydney, NSW 2109, Australia)

Abstract

Globally, renewable energy-based power generation is experiencing exponential growth due to concerns over the environmental impacts of traditional power generation methods. Microgrids (MGs) are commonly employed to integrate renewable sources due to their distributed nature, with batteries often used to compensate for power fluctuations caused by the intermittency of renewable energy sources. However, sudden fluctuations in the power supply can negatively impact battery performance, making it challenging to select an appropriate battery energy storage system (BESS) at the design stage of an MG. The cycle count of a battery in relation to battery stress is a useful measure for determining the general health of a battery and can aid in BESS selection. An accurate digital replica of an MG is required to determine the required cycle count and stress levels of a BESS. The Digital Twin (DT) concept can be used to replicate the dynamics of the MG in a virtual environment, allowing for the estimation of required cycle numbers and applied stress levels to a BESS. This paper presents a Microgrid Digital Twin (MGDT) model that can estimate the required cycle count and stress levels of a BESS without considering any unique battery type. Based on the results, designers can select an appropriate BESS for the MG, and the MGDT can also be used to roughly estimate the health of the currently operating BESS, allowing for cost-effective predictive maintenance scheduling for MGs.

Suggested Citation

  • Nisitha Padmawansa & Kosala Gunawardane & Samaneh Madanian & Amanullah Maung Than Oo, 2023. "Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept," Energies, MDPI, vol. 16(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4540-:d:1164809
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    1. Magnus Værbak & Joy Dalmacio Billanes & Bo Nørregaard Jørgensen & Zheng Ma, 2024. "A Digital Twin Framework for Simulating Distributed Energy Resources in Distribution Grids," Energies, MDPI, vol. 17(11), pages 1-36, May.

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