Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept

Nisitha Padmawansa, Kosala Gunawardane (), Samaneh Madanian and Amanullah Maung Than Oo
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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

Energies, 2023, vol. 16, issue 12, 1-18

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.

Keywords: digital twin; battery energy storage health monitoring; microgrid digital twin (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 (2)

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