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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%.
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.
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.
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.
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.