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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.
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.
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.
The battery pack lifetime is severely affected by the State-of-Charge (SOC) and operating temperature. This paper proposes a real-time SoC-Temperature balance power-sharing algorithm for the battery racks to optimize the battery life. The control scheme takes into account the SoC disparity and temperature deviation simultaneously to calculate the active power set-points for battery energy storage system (BESS) units. The proposed algorithm can serve as an alternative to the state of the health (SoH) based power sharing algorithms, which require complex SoH estimation procedures and extensive data for battery age prediction. This method can be particularly useful for second-life batteries, which often show significant heterogeneity in age, internal resistance, and capacity, necessitating a generic yet robust control strategy for their optimal utilization and to minimize degradation. Simulation results demonstrate (i) the suitability of the proposed control scheme for real-time implementation, (ii) the controller efficacy to limit high battery temperatures, which can help to slow down the ageing process. Overall, this research enables the easy integration of second-life batteries for grid ancillary services, obviating the need for complex SoH estimation procedures by considering battery temperature and SoC.
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.
Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems
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.
Stationäre Batteriespeicher gewinnen sowohl im privaten als auch im gewerblichen Bereich weiterhin an Relevanz:
Fortschritte in Zell- und Systemtechnologie erlauben innovative und kostengünstigere Lösungen, neue Geschäftsfelder werden erschlossen und rechtliche Rahmenbedingungen sind im stetigen Wandel. Dieser Artikel fasst die wichtigsten Entwicklungen und Branchen-Trends für 2021 in Deutschland zusammen und untermauert diese anhand von Umfragen des Experten-Fachforums „BVES Fachforum Batteriespeicher“. Zusätzlich zu einer Markt- und Kostenprognose erlaubt die Auswertung einen Blick auf mögliche neue Anwendungsfelder sowie eine Branchenempfehlung zu Handlungsfeldern in den Bereichen Recht und Regulatorik.
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