Power System Reliability and Maintenance
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Abstract
Abstract
Battery Energy Storage System (BESS) models for reliability evaluation, as well as other power system simulation studies, are typically characterized by two key variables: State of Charge (SOC), representing the stored energy in MWh, and Injected Power (PB), indicating charging or discharging power in MW. These variables strongly depend on the operational role of the BESS within the system. Ideally, long-term time series data—spanning at least one year—from real installations would be available to support accurate modeling. However, due to the relatively recent deployment of BESS technologies, such data is often limited or unavailable. To address this limitation, this paper proposes a methodology for generating synthetic time series of SOC and PI for a specific BESS configuration and operational objective. The approach is based on an optimization framework that determines the optimal operation of the BESS to meet a predefined objective. The generated data is subsequently used to develop a BESS model suitable for composite reliability assessment using non-sequential Monte Carlo simulation.
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@article{Falco2026Bess,
title = {Bess Model for Composite Reliability Assessment by Non-Sequential Monte Carlo Simulation Based on Historic or Synthetic Data},
author = {Djalma Falcão and Mateus Vaz and Thiago Masseran and Carmen Borges and Paulo Esmeraldo and João Aires and Tiago Amaral and Vinicius Lopes and Roberta Souza and Claudio Carvalho},
journal = {Preprints.org},
year = {2026},
doi = {10.20944/preprints202607.0450.v1},
url = {https://doi.org/10.20944/preprints202607.0450.v1}
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