Produzierende Unternehmen stehen aufgrund steigender Preise für elektrische Energie großen Herausforderungen gegenüber. Der durchschnittliche Strompreis für die Industrie stieg in Deutschland von 12,07 ct pro kWh im Jahr 2010 auf 21,38 ct pro kWh im Jahr 2021. Batteriespeicherlösungen bieten vielfältige Anwendungsfälle, um Industriebetriebe energiewirtschaftlich zu optimieren. Dazu gehören beispielsweise die intensive Netznutzung, oder eine Kombination aus der atypischen Netznutzung und der Primärregelleistungsvermarktung. Der vorliegende Beitrag beschreibt zunächst diese Anwendungsfälle und geht im Anschluss auf unterschiedliche Typen von Batteriespeichersystemen ein, die hierfür eingesetzt werden können. Es wird daraufhin ein exemplarisches Batteriespeichersystem bei einem Industrieunternehmen vorgestellt. Der Beitrag schließt mit der Beschreibung eines innovativen, auf künstlicher Intelligenz basierenden Ansatzes zur zuverlässigen Steuerung von Batteriespeichersystemen, um einer hohen Komplexität des Gesamtsystems zu begegnen.
The industry is facing major challenges due to rising prices for electrical energy. The average electricity price for industry in Germany rose from 12.07 ct per kWh in 2010 to 19.09 ct per kWh in 2021. Battery storage solutions offer a wide range of applications to optimize industrial companies in terms of energy management. These are, for example, the intensive grid use or a combination of atypical grid use and the marketing of primary balancing power. This article describes these use cases and explains different types of suitable battery storage systems. In the following, an exemplary battery storage system at an industrial company is presented. The article closes with the description of an innovative approach based on artificial intelligence. This enables the control of battery storage systems in the case of challenges due to the high complexity of the overall system.
Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.
Battery energy storage systems (BESS) are used for a variety of applications, with their economic benefit often being the decisive factor for deployment. A multitude of physico-chemical aging mechanisms lead to capacity fade over a BESS life cycle. The models that are used to describe this capacity fade are prone to inherent model errors. Through a holistic techno-economic modelling approach, we investigate the impact of battery degradation modelling uncertainty on the economic benefit of representative BESS applications. Here, it is shown how improved parameter fit quality can reduce the resulting economic uncertainty. Furthermore, we highlight that the consideration of degradation modelling uncertainty is especially crucial when: (i) the cash flow highly depends on the available battery capacity, (ii) a fixed, e.g. warranty mandated, state of health limit acts as the threshold for battery end-of-life, (iii) long evaluation periods and low discount rates are the focus of economic evaluation.
The amount of deployed battery energy storage systems (BESS) has been increasing steadily in recent years. For newly commissioned systems, lithium-ion batteries have emerged as the most frequently used technology due to their decreasing cost, high efficiency, and high cycle life. As a result of a multitude of cell internal aging mechanisms, lithium-ion batteries are subject to degradation. The effects of degradation, in particular decreasing capacity, increasing resistance, and safety implications, can have significant impact on the economics of a BESS. Influenced by aging stress factors such as the state of charge, charge–discharge rate, cycle count, and temperature, the extent of degradation is directly affected by the operating conditions. Significant amount of literature can be found that focuses on aging aware operation of BESSs. In this review, we provide an overview of relevant aging mechanisms as well as degradation modeling approaches, and deduce the key aspects from the state of the art in those topics for BESS operation. Following that, we review and categorize methods that aim to increase BESS lifetime by accounting for battery degradation effects in the operation strategy. The literature shows that using empirical or semi-empirical degradation models as well as the exact solution approach of mixed integer linear programming are particularly common for that purpose, as is the method of defining aging costs for the objective function. Furthermore, through a simulation case study, we identify the most relevant stress factors that influence degradation for the key applications of self consumption increase, peak shaving, and frequency containment reserve.
Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty
(2022)
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.
Stationäre Batteriespeicher gewinnen sowohl im privaten als auch im gewerblichen Bereich weiterhin an Relevanz:
Fortschritte in Zell- und Systemtechnologie erlauben innovative und kostengünstigere Lösungen, neue Geschäftsfelder werden erschlossen und rechtliche Rahmenbedingungen sind im stetigen Wandel. Dieser Artikel fasst die wichtigsten Entwicklungen und Branchen-Trends für 2021 in Deutschland zusammen und untermauert diese anhand von Umfragen des Experten-Fachforums „BVES Fachforum Batteriespeicher“. Zusätzlich zu einer Markt- und Kostenprognose erlaubt die Auswertung einen Blick auf mögliche neue Anwendungsfelder sowie eine Branchenempfehlung zu Handlungsfeldern in den Bereichen Recht und Regulatorik.
The growing global electricity demand and the upcoming integration of charging options for electric vehicles is creating challenges for power grids, such as line over loading. With continuously falling costs for lithium-ion batteries, storage systems represent an alternative to conventional grid reinforcement. This paper proposes an operation strategy for battery energy storage systems, targeted at industrial consumers to achieve both an improvement in the distribution grid and electricity bill savings for the industrial consumer. The objective is to reduce the peak power at the point of common coupling in existing distribution grids by adapting the control of the battery energy storage system at individual industrial consumer sites. An open-source simulation tool, which enables a realistic simulation of the effects of storage systems in different operating modes on the distribution grid, has been adapted as part of this work. Further information on the additional stress on the storage system is derived from a detailed analysis based on six key characteristics. The results show that, with the combined approach, both the local peak load and the global peak load can be reduced, while the stress on the energy storage is not significantly increased. The peak load at the point of common coupling is reduced by 5.6 kVA to 56.7 kVA and the additional stress for the storage system is, on average, for a six month simulation, period only 1.2 full equivalent cycles higher.
SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems
(2022)
The increasing feed-in of intermittent renewable energy sources into the electricity grids worldwide is currently leading to technical challenges. Stationary energy storage systems provide a cost-effective and efficient solution in order to facilitate the growing penetration of renewable energy sources. Major technical and economical challenges for energy storage systems are related to lifetime, efficiency, and monetary returns. Holistic simulation tools are needed in order to address these challenges before investing in energy storage systems. One of these tools is SimSES, a holistic simulation framework specialized in evaluating energy storage technologies technically and economically. With a modular approach, SimSES covers various topologies, system components, and storage technologies embedded in an energy storage application. This contribution shows the capabilities and benefits of SimSES by providing in-depth knowledge of the implementations and models. Selected functionalities are demonstrated, with two use cases showing the easy-to-use simulation framework while providing detailed technical analysis for expert users. Hybrid energy storage systems consisting of lithium-ion and redox-flow batteries are investigated in a peak shaving application, while various system topologies are analyzed in a frequency containment reserve application. The results for the peak shaving case study show a benefit in favor of the hybrid system in terms of overall cost and degradation behavior in applications that have a comparatively low energy throughput during lifetime. In terms of system topology, a cascaded converter approach shows significant improvements in efficiency for the frequency containment reserve application.
Lithium-ion cells are subject to degradation due to a multitude of cell-internal aging effects, which can significantly influence the economics of battery energy storage systems (BESS). Since the rate of degradation depends on external stress factors such as the state-of-charge, charge/discharge-rate, and depth of cycle, it can be directly influenced through the operation strategy. In this contribution, we propose a model predictive control (MPC) framework for designing aging aware operation strategies. By simulating the entire BESS lifetime on a digital twin, different aging aware optimization models can be benchmarked and the optimal value for aging cost can be determined. In a case study, the application of generating profit through arbitrage trading on the EPEX SPOT intraday electricity market is investigated. For that, a linearized model for the calendar and cyclic capacity loss of a lithium iron phosphate cell is presented. The results show that using the MPC framework to determine the optimal aging cost can significantly increase the lifetime profitability of a BESS, compared to the prevalent approach of selecting aging cost based on the cost of the battery system. Furthermore, the lifetime profit from energy arbitrage can be increased by an additional 24.9% when using the linearized calendar degradation model and by 29.3% when using both the linearized calendar and cyclic degradation model, compared to an energy throughput based aging cost model. By examining price data from 2019 to 2022, the case study demonstrates that the recent increases in prices and price fluctuations on wholesale electricity markets have led to a substantial increase of the achievable lifetime profit.