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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.
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.
The Efficiency and Profitability of the Modular Multilevel Battery for Frequency Containment Reserve
(2021)
The modular multilevel battery (M2B) is a novel approach to integrate battery storage into the electricity grid. This paper obtains the efficiency and financial benefits of a working prototype system, compared to conventional systems for frequency containment reserve (FCR). The efficiency is determined with a low-level simulation that models the conduction losses on the circuit, the MOSFETs’ switching and conduction losses, and the system consumption. The simulation model shows, that the efficiency is superior to conventional inverters over the entire operating range. The operation for FCR shows a cost-reduction of 50% for transactions on the intraday market, resulting in a 72.9% higher net-profit after 10 years.
The connection to the electrical grid is a key component of stationary battery energy storage systems. Utility-scale systems comprise of several power electronics units. Various grid connection topologies may be used, depending on the conversion stages within each unit, the load distribution between the power electronics and additionally the grid level to which the system is connected. The energy efficiency, which is a key performance indicator for storage systems, is compared between various scenarios. Detailed models are developed for the key components: The inverter/rectifier, the DC-DC converter, and the transformer. The respective model parameterization is based on state-of-art industry components and compared against experimental data. Two grid application scenarios, namely Primary Control Reserve and Secondary Control Reserve, are simulated for a comparison in reference application scenarios often discussed for utility-scale battery energy storage systems. Results show that grid connection setups without an intermediate DC link conversion stage are more efficient than those with. The optimum number of inverters in dependence on the actual load is determined. The connection to the low-voltage grid is more efficient due to the absence of the transformer which introduces significant additional losses. The topology models developed herein can be integrated into system models that include the overall systems or used for the design of novel battery systems grid connection topologies with detailed evaluation down to the component level.
Utility-scale battery storage systems typically consist of multiple smaller units contributing to the overall power dispatch of the system. Herein, the power distribution among these units is analyzed and optimized to operate the system with increased energy efficiency. To improve the real-life storage operation, a holistic system model for battery storage systems has been developed that enables a calculation of the energy efficiency. A utility-scale Second-Life battery storage system with a capacity of 3.3 MWh/3 MW is operated and evaluated in this work. The system is in operation for the provision of primary control reserve in combination with intraday trading for controlling the battery state of charge. The simulation model is parameterized with the system data. Results show that losses in power electronics dominate. An operational strategy improving the energy efficiency through an optimized power flow distribution within the storage system is developed. The power flow distribution strategy is based on the reduction of the power electronics losses at no-load/partial-load by minimizing their in-operation time. The simulation derived power flow distribution strategy is implemented in the real-life storage system. Field-test measurements and analysis prove the functionality of the power flow distribution strategy and reveal the reduction of the energy throughput of the units by 7%, as well as a significant reduction of energy losses in the units by 24%. The cost savings for electricity over the system’s lifetime are approximated to 4.4% of its investment cost
For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.
Electric vehicles (EVs) can participate in various markets through a vehicle-to-grid (V2G) interface. Aggregators can combine the individual contributions of EVs to offer them, for example, on the frequency containment reserve (FCR) market or to use them for arbitrage trading. A simple approach is combining EVs in random fashion until the pool is able to reach the threshold for a service of choice. Alternatively, aggregators can compose their pools in smart fashion and include only EVs that contribute significantly to the pool's performance. In a previous publication, we have shown that optimizing the aggregated pools of commercial vehicles for the provision of FCR or arbitrage trading can increase revenues by up to 7-fold. In this work, we analyze the optimally composed pools and show that large vehicle batteries in the order of 80 kWh are particularly useful for arbitrage trading, while FCR provision is also possible with medium-sized EV batteries in the range of 30 kWh due to the small cycle depths. The inclusion of EVs with very small vehicle batteries around 20 kWh in aggregated pools is neither economically optimal for arbitrage trading nor for FCR provision. An analysis of the economic sectors of the commercial EVs selected for the optimal EV pools shows that some economic sectors are more suitable for V2G than others: In particular EVs of the sector “human health and social work activities” are unsuitable for V2G provision due to regular and long travel times during the day. In contrast, EVs from the "manufacturing" sector are particularly well represented in all applications and the "transportation and storage" sector in the arbitrage application. In addition to these analyses of the optimized pools, we reveal that a reduction in the required minimum power and increments would make the FCR market even more attractive to EV pools by increasing revenues by 50% to 66%. It would also better exploit the potential of EVs, as increments could be better utilized than they are in the current 1 MW minimum power requirement in central Europe.
Energy efficiency is a key performance indicator for battery storage systems. A detailed electro-thermal model of a stationary lithium-ion battery system is developed and an evaluation of its energy efficiency is conducted. The model offers a holistic approach to calculating conversion losses and auxiliary power consumption. Sub-models for battery rack, power electronics, thermal management as well as the control and monitoring components are developed and coupled to a comprehensive model. The simulation is parametrized based on a prototype 192 kWh system using lithium iron phosphate batteries connected to the low voltage grid. The key loss mechanisms are identified, thoroughly analyzed and modeled. Generic profiles featuring various system operation modes are evaluated to show the characteristics of stationary battery systems. Typically the losses in the power electronics outweigh the losses in the battery at low power operating points. The auxiliary power consumption dominates for low system utilization rates. For estimation of real-world performance, the grid applications Primary Control Reserve, Secondary Control Reserve and the storage of surplus photovoltaic power are evaluated. Conversion round-trip efficiency is in the range of 70–80%. Overall system efficiency, which also considers system power consumption, is 8–13 percentage points lower for Primary Control Reserve and the photovoltaic-battery application. However, for Secondary Control Reserve, the total round-trip efficiency is found to be extremely low at 23% due to the low energy throughput of this application type.
The rapid increase of renewable energy sources made coordinated control of the distributed and intermittent generation units a more demanded task. Matching demand and supply is particularly challenging in islanded microgrids. In this study, we have demonstrated a mixed-integer quadratic programming (MIQP) method to achieve efficient use of sources within an islanded microgrid. A unique objective function involving fuel consumption of diesel generator, degradation in a lithium-ion battery energy storage system, carbon emissions, load shifting, and curtailment of the renewable sources is constructed, and an optimal operating point is pursued using the MIQP approach. A systematic and extensive methodology for building the objective function is given in a sequential and explicit manner with an emphasis on a novel model-based battery aging formulation. Performance of the designed system and a sensitivity analysis of resulting battery dispatch, diesel generator usage, and storage aging against a range of optimization parameters are presented by considering real-world specifications of the Semakau Island, an island in the vicinity of Singapore.
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 smart charging strategies or the installation of grid integrated storage systems represent an alternative to conventional grid reinforcement. This paper examines the influence of various charging strategies at electric vehicle charging parks to the peak grid load. Furthermore, the battery energy storage systems with various capacities located at these charging parks are simulated with a control strategy aiming to reduce the impact to the grid. Results show that with controlled charging strategies the capacity of the storage systems at the charging parks can be reduced from 2MWh to 600kWh while achieving the same reduction of peak load at the point of common coupling
Electrical energy storage systems are key to the integration of intermittent renewable energy technologies such as photovoltaic solar systems and wind turbines. As installed battery energy storage system capacities rise, it is crucial that the environmental impacts of these systems are also positive. In this work, a methodology to ascertain the effect and effectiveness of integration of energy storage on the carbon footprint of isolated island grid energy systems and its reduction is presented. Two metrics are introduced — the Levelized Emissions of Energy Supply (LEES), and the reduction in emissions per additional unit of energy storage (R). The proposed methodology is applied to an island grid scenario to ascertain the variation in the LEES value with the peak power and energy storage capacity of the BESS. A simplified LCA of a utility-scale Lithium-ion BESS is also carried out for this purpose. It is found that for the considered scenarios, incorporation of battery systems is always effective in reducing emissions, with a maximum possible reduction of nearly 50% compared to no storage. With the help of the metric R, the proposed methodology is also useful in identifying isolated energy systems which should be prioritized for incorporation of additional energy storage capacity.
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.
Battery-Assistance vs. Grid Reinforcement for High-Power EV Charging: An Emissions Perspective
(2021)
An increasingly large number of battery electric vehicles (BEVs) and electric buses are hitting the roads globally each year. These numbers are expected to grow further in light of ever-more favourable costs, and improved battery technology that addresses concerns about range anxiety and charging times. This upward trend in electrification of the automobile sector essentially shifts the mobility energy demand from the oil & gas sector to the electricity sector. Power transmission bottlenecks in the grid, caused on the supply-side by periods of strong renewable generation, may get further aggravated by adding demand-side bottlenecks to the mix as well. The grid, at several locations, will need to be overhauled to be able to supply huge bursts of power intermittently to cover large power demands to support fast-charging simultaneously at multiple locations. This can entail huge monetary investments for the upgradation of grid infrastructure. Battery-assisted high power charging (BA-HPC) is thought of as a convenient solution to this problem. This solution enables demand-side peak- load shaving, and draws energy more uniformly over extended periods of time. While this solution may well be economically profitable in some cases, an investigation of the environmental impact of this solution can yield interesting insights, and aid decision-makers by identifying scenarios in which one solution is more favorable over the other. This is crucial, since the rationale for shifting to electromobility is the curbing of greenhouse gas emissions. In this work, we employ in-house python-based time-series analysis simulation tools to simulate the grid reinforcement and the battery energy storage systems, and present comparative lifetime emissions analyses for both solutions in a variety of configurations.
For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately.For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1 % of the original cell capacity.
This paper presents a comprehensive calendar aging study on a lithium-ion battery with a test duration of 29 months. This aging study was realized with a widely used commercial LiFePO4/graphite cell from Sony/Murata, which promises both long calendar and cycle lifetime, which is especially required for stationary battery applications. The development of the cells’ capacity, as well as the resistances, are shown in a static calendar aging study for 17 test points, each with 3 cells, having constant storage conditions of temperature and state of charge. Based on the measurement data, a semi-empirical aging model is presented for the capacity loss and resistance increase, consisting of only 5 parameters which are valid for all storage conditions. An additional dynamic calendar aging study is performed with 9 months test duration for model validation, consisting of 15 test points with varying conditions of temperature and state of charge. The absolute model errors against the validation data points remain below 2.2% for the capacity loss and below 6.9% for the resistance increase for all dynamic validation tests. In conclusion, this calendar aging model allows the prognosis of the calendar lifetime of LiFePO4/graphite batteries in different applications with varying storage conditions over time.
The imminent coupling of the transport sector with the electricity sector and the possibilities of grid integrated energy storages are creating new potentials and challenges.
With open_BEA (open battery models for electrical grid applications), the following open questions are addressed:
• Which storage technology is costoptimal for a given application?
• How must stationary storage systems optimally positioned, dimensioned and operated in order to provide gridrelated services?
• To which extend are battery electric vehicles capable to serve as a flexibility option in a future power grid?
• Which potential has an energy storage system performing active peak-shaving for future grid planning?
Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.
To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.
Lithium-ion batteries are used for both stationary and mobile applications. While in the automotive industry standard profiles are used to compare the performance and efficiency of competing vehicles, a similar comparative metric has not been proposed for stationary battery energy storage systems. Because standard profiles are missing, the comparable evaluation of different applications with respect to efficiency, long-term behavior and profitability is very difficult or not possible at all. This work presents a method to create these standard profiles and the results are available as open data for download. Input profiles including frequency data, industry load profiles and household load profiles are transformed into storage profiles including storage power and state of charge using a holistic simulation framework. Various degrees of freedom for the energy management system as well as for the storage design are implemented and the results are post-processed with a profile analyzer tool in order to identify six key characteristics, these being: full-equivalent cycles, efficiency, depth of cycles, resting periods, number of changes of sign and energy throughput between changes of sign. All applications examined in this paper show unique characteristics which are essential for the design of the storage system. E.g., the numbers for annual full-equivalent cycles vary from 19 to 282 and the efficiency lies between 83% and 93%. With aid of this work in conjunction with the open data results, users can test and compare their own cell types, operation strategies and system topologies with those of the paper. Furthermore, the storage power profiles and state of charge data can be used as a reference for lifetime and profitability studies for stationary storage systems.
Battery energy storage systems (BESS) coupled with rooftop-mounted residential photovoltaic (PV) generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on solar energy availability and local demand. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, feed-in remuneration, and battery aging. Using up to date technology-specific aging information and the investment cost of battery and inverter systems, three mature battery chemistries are compared; a lead-acid (PbA) system and two lithium-ion systems, one with lithium-iron-phosphate (LFP) and another with lithium-nickel-manganese-cobalt (NMC) cathode. The results show that different storage technology and component sizing provide the best economic performances, depending on the scenario of load demand and PV generation.
The rapid decentralization of energy generation and storage facilitates an opportunity to redesign existing energy systems. Here, peer-to-peer energy trading in local markets offers advantages for demand response and flexibility of energy delivery, yet it still faces problems of customer acceptance, namely, concerns over sharing control of batteries and the degradation impacts of increased cycles. To help overcome these hurdles, this research develops a techno-economic model that optimizes the interplay between peer-to-peer trading and energy management systems in a community. The model distinguishes between two decision making approaches in a local electricity market: decentral, where the household retains full control over its storages, and central, where the flexibilities are fully leveraged to maximize the community benefit. Both approaches demonstrate the significant monetary benefit of peer-to-peer trading, with the central approach reaching the greatest profitability potential. Negative effects on the battery lifetime only occur in the central case with bidirectional vehicles, and the degradation is comparatively slight.
This article proposes a basic concept for the multi-use of stationary battery storage systems with multiple stakeholders to improve the economic value of battery storage systems. An auction market is suggested, where segments of the energy storage system and rights of use are auctioned. The blockchain technology is incorporated to develop a generic, low-cost concept that enables distinct obligations between the stakeholders caused by the technical operation of the battery storage system. Smart contracts allow flexible sharing of the battery storage system and increase the system’s utilization ratio in the presence of prediction uncertainties.
Due to their decreasing cost, lithium-ion batteries (LiB) are becoming increasingly attractive for grid-scale applications. In this paper, we investigate the use of LiB for providing secondary reserve and show how the achieved cost savings could be increased by using model-based optimization techniques. In particular, we compare a maximum use dispatch strategy with two different cost-minimizing strategies. For the estimation of state-dependent battery usage cost, we combine an existing electro-thermal LiB model of a mature lithium-iron-phosphate battery cell with corresponding semiempirical calendar and cycle aging models. We estimate the benefit of storage operation from the system operator's point of view by gauging the avoided cost of activated reserve. Our evaluation is based on two years worth of data from the German reserve market. The proposed cost minimizing dispatch strategies yield significantly better results than a dispatch strategy that maximizes battery utilization.
Assessment of residential battery storage systems and operation strategies considering battery aging
(2020)
With the increasing popularity of combining residential photovoltaic systems with battery storages, research, industry, and customers look for ways to determine if such an investment is economically profitable. Simulation programs may serve to predict the profitability and lifetime of the system. In this paper, we use techno-economic analysis with a specific account of battery degradation to determine profitability and lifetime of a residential photovoltaic (PV) battery system under different energy management and tariff regimes. This work presents two case studies: the first being a techno-economic comparison for a residential PV-battery system in New South Wales, Australia and Germany, and the second analyzing the profitability and degradation impact of three different operation strategies for a battery storage in Australia. The results reveal that site-specific conditions (i.e., geographical and energy-economic constraints) may have a significant impact on the ideal system configuration and ultimately the anticipated battery lifetime. Furthermore, statistical analysis of different storage operation strategies applied to various prosumer load and generation profiles reveals the effects of storage dispatch strategies on battery aging.
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.
Self-consumption of household photovoltaic (PV) storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, ideally without relying on error-prone forecasts for both generation and consumption. Considering the large variation in power profiles of different houses in a neighborhood, the strategy is also supposed to be beneficial and applicable for the entire community. In this study, an adaptable battery charging control strategy is designed in order to obtain minimum costs for houses without any meteorological or load forecasts. Based on fuzzy logic control (FLC), battery state-of-charge (SOC) and the variation of SOC (∆SOC) are taken as input variables to dynamically determine output charging power with minimum costs. The proposed FLC-based algorithm benefits from the charging battery as much as possible during the daytime, and meanwhile properly preserves the capacity at midday when there is high possibility of curtailment loss. In addition, due to distinct power profiles in each individual house, input membership functions of FLC are improved by particle swarm optimization (PSO) to achieve better overall performance. A neighborhood with 74 houses in Germany is set up as a scenario for comparison to prior studies. Without forecasts of generation and consumption power, the proposed method leads to minimum costs in 98.6% of houses in the community, and attains the lowest average expenses for a single house each year.
Driven by the need for a sustainable energy transition and a paradigm shift in the energy and mobility sectors, the popularity of electric vehicles is on the rise. Learning curve effects and falling investment costs further accelerate the deployment of electric vehicles with lithium-ion batteries; and as a multi-purpose technology, they are predestined for serving multiple applications. In this work we present an electric vehicle multi-use approach for a German commercial electricity consumer with an electric vehicle fleet. We analyze which behind-the-meter and in front-of-the-meter applications are particularly suitable for electric vehicles from a techno-economic point of view. In addition to providing the mobility service, we investigate the applications self-consumption increase, peak shaving, frequency regulation, and spot market trading. For the implementation of the approach, we introduce a model predictive control framework in which a mixed-integer linear programming algorithm is combined with a semi-empirical degradation model. The approach is analyzed with the investigation of fleet sizes from 1 to 150 vehicles, different application combinations, possible energy shift between the energy partitions, bidirectional charging schemes, and degradation awareness formulations. The results show that the deployment flexibility and application synergies increase with the number of stacked services, leading to additional annual cash flows of up to 2224 EUR per electric vehicle as well as battery lifetime improvements.
Unlocking the Potential of Battery Storage With the Dynamic Stacking of Multiple Applications
(2020)
The ability of a battery energy storage system (BESS) to serve multiple applications makes it a promising technology to enable the sustainable energy transition. However, high investment costs are a considerable barrier to BESS deployment, and few profitable application scenarios exist at present. Here, we show that by tapping into multiple revenue streams using the dynamic stacking of applications, profitable operation is viable under current regulatory conditions. We develop a multi-use optimization framework which distinguishes between behind-the-meter and in-front-of-the-meter applications and considers how power capacity is allotted in addition to energy capacity allocation. The algorithm uses a rolling horizon optimization with an integrated degradation model and is fed with real-world data from a stationary lithium-ion battery in Germany. When combining peak shaving with frequency containment reserve, a net present value per Euro invested of 1.00 is achieved, and 1.24 with the addition of arbitrage trading on the intraday continuous market.
In the context of the increased acceptance and usage of electric vehicles (EVs), vehicle-to-building (V2B) has proven to be a new and promising use case. Although this topic is already being discussed in literature, there is still a lack of experience on how such a system, of allowing bidirectional power flows between an EV and building, will work in a residential environment. The challenge is to optimize the interplay of electrical load, photovoltaic (PV) generation, EV, and optionally a home energy storage system (HES). In total, fourteen different scenarios are explored for a German household. A two-step approach is used, which combines a computationally efficient linear optimizer with a detailed modelling of the non-linear effects on the battery. The change in battery degradation, storage system efficiency, and operating expenses (OPEX) as a result of different, unidirectional and bidirectional, EV charging schemes is examined for both an EV battery and a HES. The simulations show that optimizing unidirectional charging can improve the OPEX by 15%. The addition of V2B leads to a further 11% cost reduction, however, this corresponds with a 12% decrease in EV battery lifetime. Techno-economic analysis reveals that the V2B charging solution with no HES leads to strong self-consumption improvements (EUR 1381 savings over ten years), whereas, this charging scheme would not be justified for a residential prosumer with a HES (only EUR 160 savings).
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.
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 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.
The electrification of transportation modes such as cars, buses, and boats offers the potential of providing vehicle-to-X services during idle times. Pools of vehicles can provide balancing power, trade on the electricity market, or be used for load peak shaving. In this work, the usage patterns of electric cars, electric buses, and electric boats are investigated, and the provision of vehicle-to-X with these vehicles is simulated using an open-source simulation tool. A data analysis and a vehicle usage pattern assessment show that especially private electric cars behave predictably at night. It also reveals that the vehicle-to-X availability varies over the week for all vehicle types and is highest at night for cars and buses. During the day on weekdays, private cars are available for vehicle-to-X 30 to 70% of the time, the analyzed buses 5 to 50% of the time, and the availability of the boats depends on their primary use as ferries or private boats. If the three transportation modes provide vehicle-to-X during idle times, the equivalent full cycles that the lithium-ion batteries complete increase at different rates depending on the vehicle pool size, while the mean charging rates decrease. Furthermore, an exemplary aging analysis shows that the additional load of vehicle-to-X provision slightly increases the capacity loss of the car batteries compared to a paused unidirectional charging strategy.
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.