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This work discusses the grid-level suitability for stationary battery energy storage systems based on lithium ion technology in general, focusing on the integration of such systems in the low-voltage grid-level in Europe. The vast majority of recent research regarding stationary battery storage systems focuses on single-electricity-grid voltage levels, whereas the interaction of different electricity grid-levels has been widely neglected. Challenges in distribution grids mostly originate from the quantity of challenges in low-voltage grids, stationary battery energy storage systems that are operated in low-voltage grids may reduce these challenges. Therefore, this work investigates stationary battery energy storage systems installed in low-voltage grids and their effects on superimposed grid-levels. Simulation results show that grid challenges, addressed by battery storage systems in low-voltage grids, have positive multiplicative impacts on upper grid levels, reducing local grid demand and lowering power peak stress. A proposal is presented for a low-voltage grid battery storage system that can be operated to fulfill the aforementioned functionality and yield the benefits via a multi-purpose or multi-tasking battery storage system.
Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.
The energy transition in the mobility sector is well underway. The electrification of road transport is resulting in a shift of the energy demand from the oil and gas sector to the electricity grid. Increasingly aggressive targets for low charging times for Electric Vehicles (EVs) are slated to raise the demand for High-Power Charging (HPC). This is likely to lead to bottlenecks and overloading in vulnerable sections of the electricity grid. Battery Assistance (BA) is a promising grid integration measure for High-Power Charging (HPC) to mitigate these problems. As decarbonization is the primary objective of the energy transition, the determination and comparison of the Global Warming Potential (GWP) footprints for HPC stations with BA is crucial. A comprehensive mathematical framework for the modelling and quantification of GWP footprints for HPC has been developed. The Levelized Emissions of Energy Supply (LEES) methodology has been extended and generalized to handle energy from the grid. A new state variable for the Battery Energy Storage System (BESS) — the State of Carbon Intensity (SOCI) has been introduced to calculate the operation phase GWP footprint of the BESS. The energy consumption GWP footprint for the load is also described by a new quantity — the Load Energy Consumption (LEC) emissions. The effect of incorporation of local Photovoltaic Solar (PV) generation in the energy flows is also investigated. An optimized Energy Management System (EMS) strategy with rolling horizon optimization to minimize emissions has been implemented to regulate energy flows in scenarios with BA and local PV generation. The Levelized Emissions of Energy Supply (LEES) values are obtained for all simulated scenarios and compared against a baseline rule-based EMS strategy. In combination with on-site PV generation, BA could achieve a reduction of 24% in the LEES vis-á-vis the baseline strategy. For reference, two scenarios with Grid Reinforcement (GR) for the grid section with and without local PV generation have also been simulated. With Grid Reinforcement (GR), a reduction of over 2% can be achieved with respect to the baseline EMS strategy for BA. Grid Reinforcement (GR) in conjunction with local PV generation can bring about a further reduction of about 6% with respect to the baseline EMS strategy for BA.
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.
Feature-conserving gradual anonymization of load profiles and the impact on battery storage systems
(2023)
Electric load profiles are highly relevant for battery storage research and industry as they determine system design and operation strategies. However, data obtained from electrical load measurements often cannot be shared or published due to privacy concerns. This paper presents a methodology to gradually anonymize load profiles while conforming to various degrees of anonymity. It segregates the original load profile into base and peak sequences and extracts features from each of the sequences. With the help of the features, a synthetic, anonymized load profile is created. Different levels of anonymization can be selected, which transform the original profile to the desired extent. A random permutation of the peak sequences or base sequences is used to achieve this transformation. Exemplary profiles from a household and an electric vehicle charging station are used to demonstrate the functionality of the anonymization. The indicators of the anonymized load profiles are compared with the original ones in both time and frequency domains, and the effects of load profile anonymization on the operation of battery storage systems in two scenarios are analyzed. While the anonymized load profiles retain the time-invariant indicators from the original profile, the permutation causes a loss of regularity in the load profiles. As a result, relevant indicators of battery storage systems subjected to these anonymized profiles deviate to a greater extent in time-dependent applications such as self-consumption increase. This is reflected in the overestimation of equivalent full cycles by up to 6% and underestimation of self-sufficiency by up to 9 percentage points. In time-independent applications such as peak shaving, however, the indicators can be well reproduced with deviations of up to 3% despite the lost regularity. In order to make the anonymization methodology usable for everyone, we present the open-source tool LoadPAT, in which users can anonymize their load profiles and choose their desired level of anonymization. This work is intended to further encourage the dissemination of open-source data.
The electrification of the transportation sector leads to an increased deployment of lithium-ion batteries in vehicles. Today, traction batteries are installed, for example, in electric cars, electric buses, and electric boats. These use-cases place different demands on the battery. In this work, simulated data from 60 electric cars and field data from 82 electric buses and six electric boats from Germany are used to quantify a set of stress factors relevant to battery operation and life expectancy depending on the mode of transportation. For this purpose, the open-source tool SimSES designed initially to simulate battery operation in stationary applications is extended toward analyzing mobile applications. It now allows users to simulate electric vehicles while driving and charging. The analyses of the three means of transportation show that electric buses, for example, consume between 1 and 1.5 kWh/km and that consumption is lowest at ambient temperatures around 20 °C. Electric buses are confronted with 0.4–1 equivalent full cycle per day, whereas the analyzed set of car batteries experience less than 0.18 and electric boats between 0.026 and 0.3 equivalent full cycles per day. Other parameters analyzed include mean state-of-charges, mean charging rates, and mean trip cycle depths. Beyond these evaluations, the battery parameters of the transportation means are compared with those of three stationary applications. We reveal that stationary storage systems in home storage and balancing power applications generate similar numbers of equivalent full cycles as electric buses, which indicates that similar batteries could be used in these applications. Furthermore, we simulate the influence of different charging strategies and show their severe impact on battery degradation stress factors in e-transportation. To facilitate widespread and diverse usage, all profile and analysis data relevant to this work is provided as open data as part of this work.
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.
Both global climate change and the decreasing cost of lithium-ion batteries are enablers of electric vehicles as an alternative form of transportation in the private sector. However, a high electric vehicle penetration in urban distribution grids leads to challenges, such as line over loading for the grid operator. In such a case installation of grid integrated storage systems represent an alternative to conventional grid reinforcement. This paper proposes a method of coordinated control for multiple battery energy storage systems located at electrical vehicle charging parks in a distribution grid using linear optimization in conjunction with time series modeling. The objective is to reduce the peak power at the point of common coupling in existing distribution grids with a high share of electric vehicles. An open source simulation tool has been developed that aims to couple a stand alone power flow model with a model of a stand alone battery energy storage system. This combination of previously disjointed tools enables more realistic simulation of the effects of storage systems in different operating modes on the distribution grid. Further information is derived from a detailed analysis of the storage system based on six key characteristics. The case study involves three charging parks with various sizes of coupled storage systems in a test grid in order to apply the developed method. By operating these storage systems using the coordinated control strategy, the maximum peak load can be reduced by 44.9%. The rise in peak load reduction increases linearly with small storage capacities, whereas saturation behavior can be observed above 800 kWh.