Open Access

ARTICLE

Modeling, Simulation, and Risk Analysis of Battery Energy Storage Systems in New Energy Grid Integration Scenarios

Xiaohui Ye^1,*, Fucheng Tan¹, Xinli Song², Hanyang Dai², Xia Li², Shixia Mu², Shaohang Hao²

1 School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China
2 Power System Department, Electric Power Research Institute, Beijing, 100192, China

* Corresponding Author: Xiaohui Ye. Email:

(This article belongs to the Special Issue: Advanced Modelling, Operation, Management and Diagnosis of Lithium Batteries)

Energy Engineering 2024, 121(12), 3689-3710. https://doi.org/10.32604/ee.2024.055200

Received 20 June 2024; Accepted 12 September 2024; Issue published 22 November 2024

Abstract

Energy storage batteries can smooth the volatility of renewable energy sources. The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system (BESS). However, the current modeling of grid-connected BESS is overly simplistic, typically only considering state of charge (SOC) and power constraints. Detailed lithium (Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions. Additionally, there is a lack of real-time batteries risk assessment frameworks. To address these issues, in this study, we establish a thermal-electric-performance (TEP) coupling model based on a multi-time scale BESS model, incorporating the electrical and thermal characteristics of Li-ion batteries along with their performance degradation to achieve detailed simulation of grid-connected BESS. Additionally, considering the operating characteristics of energy storage batteries and electrical and thermal abuse factors, we developed a battery pack operational risk model, which takes into account SOC and charge-discharge rate (C_r), using a modified failure rate to represent the BESS risk. By integrating detailed simulation of energy storage with predictive failure risk analysis, we obtained a detailed model for BESS risk analysis. This model offers a multi-time scale integrated simulation that spans month-level energy storage simulation times, day-level performance degradation, minute-scale failure rate, and second-level BESS characteristics. It offers a critical tool for the study of BESS. Finally, the performance and risk of energy storage batteries under three scenarios—microgrid energy storage, wind power smoothing, and power grid failure response—are simulated, achieving a real-time state-dependent operational risk analysis of the BESS.

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Keywords

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