Hesse, Holger
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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.
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.
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.
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.
Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage. Batteries provide a fast and high power capability, making them an ideal solution for this task. This work proposes a general framework for sizing of battery energy storage system (BESS) in peak shaving applications. A cost-optimal sizing of the battery and power electronics is derived using linear programming based on local demand and billing scheme. A case study conducted with real-world industrial profiles shows the applicability of the approach as well as the return on investment dependence on the load profile. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, peak-power tariff, and battery aging. This underlines the need for a general mathematical optimization approach to efficiently tackle the challenge of peak shaving using an energy storage system. The case study also compares the applicability of yearly and monthly billing schemes, where the highest load of the year/month is the base for the price per kW. The results demonstrate that batteries in peak shaving applications can shorten the payback period when used for large industrial loads. They also show the impacts of peak shaving variation on the return of investment and battery aging of the system.
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.
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.
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?
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.
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.