SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems

Möller, Marc; Kucevic, Daniel; Collath, Nils; Parlikar, Anupam; Dotzauer, Petra; Tepe, Benedikt; Englberger, Stefan; Jossen, Andreas; Hesse, Holger C.

doi:10.1016/j.est.2021.103743

Cited by 19 publications

(24 citation statements)

References 64 publications

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“…Then, from the storage profile, several KPIs are extracted summarizing the overall impact of the input profile in the BSS. In this research, the Simulation Tool for Stationary Energy Storage Systems (SimSES) [19] is used for these purposes.…”

Section: General Descriptionmentioning

confidence: 99%

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Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems

et al. 2023

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Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems.

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Section: General Descriptionmentioning

confidence: 99%

“…Therefore, due to this reduced computation time, many battery operation conditions can be tested and compared. Some examples of simulators based on equivalent electric models are summarized in [19].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems

et al. 2023

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“…SimSES is a time-series based open source tool that allows to perform holistic techno-economic simulations and analyses for battery energy storage systems. 27 Here, we consider three applications: a commercial frequency containment reserve application (FCR), a commercial peak-shaving application (PS), and a residential PV-battery system performing a self-consumption increase service (SCI). These three applications are described in detail in previous work.…”

Section: Dynamic Aging Validation and Simulation Of Real-world Usementioning

confidence: 99%

“…14 Non-linear or physics-based models may be used to optimize dispatch using an algorithmic or gradientfree approach. 10,23,24 Accessibility of battery degradation models, and their use in applications such as those cited above, is improved by recent publication of open-access battery models, including physics-based models such as SLIDE 25 and PyBAMM, 26 as well as technoeconomic modeling tools using the reduced-order battery lifetime models studied in this work, such as SimSES developed by Technical University of Munich, 27 and the System Advisor Model developed by the National Renewable Energy Lab. 28 One of the primary challenges for utilizing battery lifetime models is the relationship between accelerated aging data and realworld use.…”

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confidence: 99%

“…two different time steps, incurring some simulation error. SimSES, which is used in this work to simulate the lifetime of battery energy storage systems, avoids this issue by calculating degradation once per day, only calculating cycling degradation once at least a half-EFC of charge-throughput has occured 27. Determining a "best-practice" approach from all of these options would require a huge variety of longterm aging data under dynamic use.…”

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confidence: 99%

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Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Gasper

Collath²,

Hesse³

et al. 2022

J. Electrochem. Soc.

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Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

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Lithium‐Free Redox Flow Batteries: Challenges and Future Prospective for Safe and Efficient Energy Storage

Kanti Hazra,

Kim,

Meskher

et al. 2024

Batteries & Supercaps

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Considering the costs, waste, and impact on the environment of current energy consumption, accurate, cost‐effective, and safely deployed energy storage systems are required. Lithium‐free redox flow batteries (RFBs) are a feasible solution. RFBs can store enormous amounts of energy effectively and are increasingly used for large‐scale applications. The use of RFBs has significantly enhanced the performance of energy storage systems and effectively reduced the costs and wastage of energy storage operations. Vanadium‐based RFBs are an emerging energy‐storage technology being explored for large‐scale deployment owing to their numerous benefits, including zero cross‐contamination, scalability, flexibility, extended life cycle, and nontoxic working state. This study describes the fundamental operating principles of redox flow battery‐based systems as well as the design considerations and constraints placed on each component. It discusses recent progress in the design and deployment of RFBs for energy‐related applications and the remaining obstacles and prospects. Finally, this study highlights the enormous potential of RFBs and suggests some solutions to scale up the use of RFBs in the near future.

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SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems

Cited by 19 publications

References 64 publications

Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems

Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems

Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Lithium‐Free Redox Flow Batteries: Challenges and Future Prospective for Safe and Efficient Energy Storage

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