IEAT - Institut für Energie- und Antriebstechnik
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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 use of natural gas has continuously increased and reached 24.7% of the worldwide primary energy supply in 2020. The same trend applies to Liquefied Natural Gas (LNG), which contributed to 52% of overall natural gas trades in the same year. In this context, the recovery of the cold energy available at LNG receiving terminals during the process of regasification is of a critical importance.
This paper addresses the integration of the regasification process with an Organic Rankine Cycle (ORC) in order to exploit the available LNG cold energy, by condensing the organic fluid. In addition, a gas turbine exploits differences between regasification and distribution pressures. The analysis covers different organic fluids and two ORC heating source configurations: a) a low-temperature one, using seawater, and b) a high-temperature one, using exhaust gas. In addition, the integration of a natural gas-fired topping gas turbine, which uses the LNG cold energy by compressor inlet air cooling, was simulated. The performance of a medium size regasification terminal (50 kg/s) was evaluated as a function of both the regasification and the natural gas distribution pressures.
Dedicated models have been developed using Aspen Plus software to simulate the regasification process and the integrated topping cycles (Organic Rankine and Brayton), and their mutual energy integrations.
The analysis shows that ORC power outputs from 2 MW up to 4.5 MW in case a) and from 6 MW up to 9 MW in case b) can be reached. The topping gas turbine benefits from the inlet air cooling and can add a power output of 35 MW to 40 MW. R125 was the best working fluid for a low-temperature ORC, while R600a showed the best performance for a high-temperature application.
Temporal Fusion Transformer for thermal load prediction in district heating and cooling networks
(2022)
Accurate forecasting of thermal loads is a critical factor for operating district heating and cooling networks economically,efficiently and with minimized emissions. If thermal loads are known with high accuracy in advance, use of renewable energiescan be maximized, and fossil generation, in particular in peaking units, can be avoided. Machine learning has already provento be an efficient tool for time series forecasting in this context. One recent advancement in machine learning is the "TemporalFusion Transformer" (TFT), which shows especially good results in the area of time series forecasting. This paper examinesthe performance of TFT in the concrete context of thermal load forecasting for district heating and cooling networks. First,a brief summary of differences between TFT and other machine learning methods is given. Secondly, it is described how themethod can be adopted to train a machine learning model for thermal load forecasting. The data to train and evaluate the neuralnetwork is based on 8 years of hourly operating data made available from the district heating network of the city of Ulm inGermany. The presented technique is used to produce 72 hours of heating load forecasts for three different district heating gridsin the city of Ulm. The results are compared to forecasts of other machine learning methods that have been previously madeas part of the publicly funded research project "deepDHC", in order to evaluate if TFT is an improvement to further reduceforecasting uncertainties.
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.
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.
The market ramp-up of electromobility is shifting vehicle-to-grid (V2G) issues into the focus of research and industry. Electric vehicles (EVs) have the potential to support the trend towards renewable energies in their role as storage units during idle times. To participate in balancing power and energy markets, EVs are pooled via aggregators. Instead of a random composition, aggregators can smartly compose their pools and add only those vehicles that actually contribute to the pool’s performance, gaining advantages over competitors. The optimization methods presented in this paper form optimized pool combinations based on the power and energy capability profiles of commercial EVs. Genetic algorithms are used to determine the revenues of the possible pools per participating EV. The use cases analyzed are the provision of balancing power on the frequency containment reserve (FCR) market of Central Europe and energy arbitrage trading on the European power exchange intraday continuous and day-ahead auction spot markets. The results show that through smart pool composition, an aggregator can increase revenue per vehicle by up to seven-fold across the markets compared to randomly assembled pools. In the Central European market, for example, the potential V2G revenues on the FCR market (380 €) exceeded those of arbitrage trading (28 € − 203 €) in 2020. In a simulation, we show the increased degradation of the vehicle battery in V2G operation compared to sole use for mobility with a smart charging strategy. However, the additional revenue can make V2G financially worthwhile, depending on costs for measuring equipment, bidirectional charging stations, and aggregator costs.
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.
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.
Efficient operation of district heating networks requires a precise forecasting of the thermal loads and an optimised dispatch strategy for the available generation and storage portfolio. This paper presents a holistic modelling and optimisation approach: first, detailed process modelling and optimisation of power plants and thermal storages; second, a numerical model for dispatch optimisation; and third, machine-learning-based load forecasting. The work is based on operating data from the district heating network of the city of Ulm in Germany. The paper presents the modelling, validation and simulation results of stationary and instationary process simulation for a biomass-fired combined heat and power plant. The analysis identifies a potential to integrate additional renewable power by “power-to-heat” technologies into different parts of the process. The economic benefit is quantified by mixed-integer linear programming optimisation applied to the district heating network. In order to allow for real-time dispatch optimisation, a machine-learning-based thermal load forecasting method was developed and evaluated, based on a 72-h forecast horizon. In addition, the economic impact of prediction uncertainties is analysed with the numerical dispatch optimisation tool.