Bess Model for Composite Reliability Assessment by Non-Sequential Monte Carlo Simulation Based on Historic or Synthetic Data

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 are 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 a framework that determines the 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.