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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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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 rapidly falling capital expenditures and rising energy and power densities of battery energy storage systems are leading to more widespread use of these systems in both mobile and stationary applications. In stationary applications, it is expected that the economic potential of a battery energy storage system will improve when serving multiple applications simultaneously. In this work, the newly developed tool, Sim-SES Mutti-Use, is introduced, which is capable of simulating an energy storage system that serves multiple applications simultaneously. Furthermore, the techno-economic impact of multi-use operation strategies on the energy storage system is explored. In its present state, the tool allows the modelling of several applications, depending on the scenario explored. Available applications include: frequency containment reserve, peak shaving, self-consumption, and compensation of reactive power. In line with other publications, simulations conducted in this work confirm, that the stacking of applications is more lucrative than single-use operation. Both economic and technical characteristics of the battery are considered in this work.

Summary 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.

BACKGROUND: A thrombolysis in cerebral infarction (TICI) score of 2b is defined as a good recanalization result although the reperfusion may only cover 50% of the affected territory. An additional mTICI2c category was introduced to further differentiate between mTICI scores. Despite the new mTICI2c category, mTICI2b still covers a range of 50-90% reperfusion which might be too imprecise to predict neurological improvement after therapy. AIM: To compare the 7-point "expanded TICI" (eTICI) scale with the traditional mTICI in regard to predict functional independence at 90 days. METHODS: Retrospective review of 225 patients with large artery occlusion. Angiograms were graded by 2 readers according the 7-point eTICI score (0% = eTICI0; reduced clot = eTICI1; 1-49% = eTICI2a, 50-66% = eTICI2b50; 67-89% = eTICI2b67, 90-99% = eTICI2c and complete reperfusion = eTICI3) and the conventional mTICI score. The ability of e- and mTICI to predict favorable outcome at 90days was compared. RESULTS: Given the ROC analysis eTICI was the better predictor of favorable outcome (p-value 0.047). Additionally, eTICI scores 2b50, 2b67 and 2c (former mTICI2b) were significantly superior at predicting the probability of a favorable outcome at 90 days after endovascular therapy with a p-value of 0.033 (probabilities of 17% for mTICI2b50, 24% for mTICI2b67 and 54% for mTICI2c vs. 36% for mTICI2b). CONCLUSIONS: The 7-point eTICI allows for a more accurate outcome prediction compared to the mTICI score because it refines the broad range of former mTICI2b results. Open-Access-Publikationsfonds 2019 peerReviewed

Optimal control of a battery energy storage system for energy arbitrage strongly depends on the marginal costs of operation. A cost function considering energy conversion losses and cycle-induced capacity losses is defined to calculate the marginal costs as a function of system power and power flow direction. The results are evaluated and reveal increased costs due to energy losses at low power operation, as well as increased costs due to capacity loss costs during charge operation at high power. The marginal cost function is evaluated for an optimal system control in an energy arbitrage scenario with variable electricity prices. Here, results show that for low price variation the optimized power control aims for minimized marginal costs. At high price variation, that strongly exceeds marginal costs, the optimum operation according to the best prices is the most profitable control. Finally, a discussion on the inclusion of the cycle-induced capacity losses is presented.

Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage systems. Among other studies, there have been several reports examining optimal sizing of such storage systems. Most such works make significant assumptions about the key factors that affect battery degradation. This work examines these assumptions using a linear aging model that considers a state of charge (SOC) dependent calendric aging. The linear model reveals the potential of an SOC-aware charge control strategy for peak shaving applications: with adequate forecasting, the battery storage system lifetime could be significantly prolonged. The results also show that while batteries used in peak shaving applications are sensitive to calendric aging, the depth of discharge cycling is much less relevant. This is an important observation that will simplify relevant optimization studies and thus contribute to more widespread application of industrial peak shaving systems.

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.

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.

Energy storage systems are acknowledged as key components for transforming the power system towards low-carbon technology. The performance and resulting benefit of energy storage systems are determined by their operation strategies. We present a generic and adaptive control algorithm, based on fuzzy-logic and optimized by a meta heuristic search method. The performance of the algorithm is demonstrated in a solar home context, with the aim to alleviate the voltage rise in the low-voltage distribution grid caused by renewable energy generation. This is achieved by reducing the household's peak feed-in of each day. The obtained results show that the proposed algorithm performs similarly well as a rule-based reference algorithm, specifically designed to reduce the daily feed-in peak. At the same time, the generic structure of the proposed controller is by far more versatile, as it allows its application in a variety of scenarios just by modifying the objective function for the search algorithm.