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Die Rolle von Batteriespeichern im Kontext eines künftigen Energiesystems: Smart Energy Systems
(2023)
Welche Bedeutung haben Batteriespeicher für künftige Energiesysteme? Können wir mit Batteriespeichern eine sichere, stabile und bezahlbare Energieversorgung erreichen? Wie flexibel muss unser Stromsystem sein?
In seiner Präsentation im erläutert Prof. Dr. Holger Hesse anhand anschaulicher Beispiele und seiner jahrelangen Erfahrung, ob und wie Elektrofahrzeuge künftig auch durch "Vehicle-to-Grid" einen Beitrag leisten können, wann "Second-life" eine wirkliche Option sein kann und in welchen Bereichen es schon heute wirtschaftlich sein kann, einen Batteriespeicher zu betreiben.
AI in Battery Storage
(2023)
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.
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.
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.
Grid-connected multi-string battery energy storage systems (BESS) operating under sustained high-power conditions face heat accumulation, power derating, and cooling-related efficiency losses, leading to trade-offs among thermal management, efficiency, and system availability. To manage these competing objectives, this work proposes a mixed-integer nonlinear programming (MINLP)-based multi-objective optimization (MOO) framework for optimal power allocation across parallel strings under air cooling. The optimization incorporates an equivalent circuit model (ECM) with SOC-, temperature-, and C-rate-dependent internal resistance, temperature-driven derating, and an experimentally derived inverter loss model. High-fidelity control is achieved by co-simulating the optimizer with an electro-thermal BESS simulation that captures spatial thermal dynamics of the battery pack. The simulation provides online state of charge (SOC) and temperature feedback and is validated against industry-measured battery pack temperatures, revealing core temperatures up to 4.5 °C higher than those predicted by conventional 0D average thermal models calibrated to pack surface measurements. Simulation results show that the baseline MINLP controller with evenly weighted objectives achieves approximately 3% higher round-trip efficiency (RTE) and system availability compared to the industrial benchmark droop control while minimizing thermal derating. Linearized optimal control without thermal optimization results in higher maximum string temperatures up to 40 °C and 4% higher derating losses. A Pareto-based objective prioritization improves energy performance by up to 5% while keeping temperatures below the 35 °C derating threshold. System scalability analysis shows that operating an optimal subset of strings outperforms conventional full-string operation, achieving 5.5% higher RTE with comparable availability and thermal performance. The framework is released open-source for adaptation to custom BESS applications.
The performance of battery energy storage systems (BESS) along their lifetime is significantly influenced by their degradation. The rate of degradation depends on stress factors such as the state of charge, charge-discharge rate, and depth of cycle, but evolves differently for every individual battery. Batteries with differences in capacity and or state of health may diverge further apart when cycled evenly causing further inhomogeneities. BESS can also consist of heterogeneous sub-units if second life batteries with reduced capacity and or state of health are integrated in the system. In order to minimize the overall system aging effects those variances can be balanced with an according operation strategy. This contribution presents a model predictive control (MPC) framework that helps to solve optimization problems regarding the power split within heterogeneous BESS-models. An exemplary 2-string battery model is used to represent batteries with different characteristics and it contains cycle and calendar aging mechanisms. In our case one of the strings is preaged. The MPC framework combines aging estimation and operation optimization by using a commercial solver. It iterates over a given profile and predicts the optimal power split between the heterogeneous strings. By defining the system’s total minimal capacity loss as an objective function, the optimizer achieves to minimize the BESS-model’s overall capacity loss during operation. The simulation reveals that while the new string ages by 1.23 %, the preaged string will lose 0.258 % state of health within the 21-day timeframe investigated.
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?
