FSP1: Energie
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This paper presents a unified framework for the optimal scheduling of battery dispatch and internal power allocation in Battery Energy Storage Systems (BESS). This novel approach integrates both market-based (price-aware) signals and physical system constraints to simultaneously optimize (1) external energy dispatch and (2) internal heterogeneity management of BESS, enhancing its operational economic value and performance. This work compares both model-based Linear Programming (LP) and model-free Reinforcement Learning (RL) approaches for optimization under varying forecast assumptions, using a custom Gym-based simulation environment. The evaluation considers both long-term and short-term performance, focusing on economic savings, State of Charge (SOC) and temperature balancing, and overall system efficiency. In summary, the long-term results show that the RL approach achieved 10% higher system efficiency compared to LP, whereas the latter yielded 33% greater cumulative savings. In terms of internal heterogeneity, the LP approach resulted in lower mean SOC imbalance, while the RL approach achieved better temperature balance between strings. In the short-term evaluation, LP delivers strong optimization under known conditions, whereas RL demonstrates higher adaptability in dynamic environments for real-time BESS control.
This paper presents a degradation-cost-aware optimization framework for multi-string battery energy storage systems, emphasizing the impact of inhomogeneous subsystem-level aging in operational decision-making. We evaluate four scenarios for an energy arbitrage scenario, that vary in model precision and treatment of aging costs. Key performance metrics include operational revenue, power schedule mismatch, missed revenues, capacity losses, and revenue generated per unit of capacity loss. Our analysis reveals that ignoring heterogeneity of subunits may lead to infeasible dispatch plans and reduced revenues. In contrast, combining accurate representation of degraded subsystems and the consideration of aging costs in the objective function improves operational accuracy and economic efficiency of BESS with heterogeneous aged subunits. This fully informed scenario achieves 21% higher revenue per unit of SOH loss compared to the baseline scenario. These findings highlight that modeling aging heterogeneity is not just a technical refinement but may become a crucial enabler for maximizing both short-term profitability and long-term asset value in particular for long BESS usage scenarios.
This study investigates two models of varying complexity for optimizing intraday arbitrage energy trading of a battery energy storage system using a model predictive control approach. Scenarios reflecting different stages of the system’s life-time are analyzed. The findings demonstrate that the equivalent-circuit-model-based non-linear optimization model outperforms the simpler linear model by delivering more accurate predictions of energy losses and system capabilities. This enhanced accuracy enables improved operational strategies, resulting in increased roundtrip efficiency and revenue, particularly in systems with batteries exhibiting high internal resistance, such as second-life batteries. However, to fully leverage the model’s benefits, it is essential to identify the correct parameters.
Der Vortrag thematisiert das Thema "klimapositives" Müllheizkraftwerk, d.h. die Erzeugung negativer CO2 Emissionen durch die CO2-Abtrennung an Müllheizkraftwerken oder Biomasseheizkraftwerken mit hohem biogenem Brennstoffanteil. Er präsentiert den aktuellen Stans sowie mögliche Synergien mit dem klassischen Emissionsmanagement bzw. bei Modernisierungen oder Ertüchtigungen.
This paper presents a novel integration concept for mid-voltage power semiconductor modules. The concept is based on a dielectric liquid that is used for both, cooling and insulation. The semiconductor chips are attached to Cu inlays in a PCB substrate. Dual side cooling is achieved jet by impingement on the bottom side and immersion in the dielectric liquid on the top side. A first laboratory demonstrator was designed and build-up with 2.0 kV rated SiC MOSFETs in a 140×100 mm² module package. Shell immersion cooling fluid S5 LV was applied as coolant. Test results indicate a junction to fluid thermal resistance of 0.54 K/W at a flow rate of 5 l/min and with pressure drop of 12 kPa. The results obtained are on the same level compared with a state- -the-art automotive power of modules with a pin-fin array integrated in the baseplate and water-glycol as coolant.
Energy Management Systems (EMS) for controlling Distributed Energy Resources (DER) like batteries in buildings play an increasingly important role in integrating variable renewable energy. To cope with their increasing complexity, Deep Reinforcement Learning (DRL) has been suggested as data-driven and highly flexible control method. However, DRL applied to EMS still suffers from relatively poor performance compared to Model Predictive Control (MPC) and requires long training time on large datasets to become competitive. This paper presents a novel approach to improve DRL controllers for solar battery systems optimizing time of energy use based on dynamic prices by initializing Proximal Policy Optimization (PPO) parameters using optimal solutions from Linear Programming (LP). We adapt Behavior Cloning (BC), a form of Imitation Learning that traditionally transfers human expertise to robots, by using mathematically optimized behaviors rather than human demonstrations, resulting in reduced training steps and increased performance. Experimental results using actual data collected in industrial microgrids show that our approach achieves up to 40% higher cost savings compared to standard DRL models, while reducing training steps by 80% requiring just 4 weeks of LP solutions as demonstrations. Using an extensive sensitivity analysis across various load profiles and system sizing parameters, we validate the robustness of these improvements. Our findings highlight the potential of LP-based parameter initialization to accelerate the adoption of DRL in Energy Management Systems.
Distribution systems’ vulnerability to power losses remains high, among other parts of the power system, due to the high currents and lower voltage ratio. Connecting distributed generation (DG) units can reduce power loss and improve the overall performance of the distribution networks if sized and located correctly. However, existing studies have usually assumed that DGs operate only at the unity power factor (i.e., type-I DGs) and ignored their dynamic capability to control reactive power, which is unrealistic when optimizing DG allocation in power distribution networks. In contrast, optimizing the allocation of DG units injecting reactive power (type-II), injecting both active and reactive powers (type-III), and injecting active power and dynamically adjusting (absorbing or injecting) reactive power (type-IV) is a more likely approach, which remains unexplored in the current literature. Additionally, various metaheuristic optimization techniques are employed in the literature to optimally allocate DGs in distribution networks. However, the no-free-lunch theorem emphasizes employing novel optimization approaches, as no method is best for all optimization problems. This study demonstrates the potential of optimally allocating different DG types simultaneously to improve power distribution network performance using a parameter-free Jaya optimization technique. The primary objective of optimally allocating DG units is minimizing the distribution network’s power losses. The simulation validation of this study is conducted using the IEEE 33-bus test system. The results revealed that optimally allocating a multiunit DG mix instead of a single DG type significantly reduces power losses. The highest reduction of 96.14% in active power loss was obtained by placing three type-II, two type-III, and three type-IV units simultaneously. In contrast, the minimum loss reduction of 87.26% was observed by jointly allocating one unit of the aforementioned three DG types.
The future increase in electric consumers in households, especially electric vehicles and heat pumps, as well as the rising solar power installation, allows for a certain degree of grid-independency and cost savings through self-generated electricity. Both the use of self-generated electricity and cost savings may be increased through the implementation of smart energy management systems at home, which may also have a beneficial impact on electric vehicle sales. In this paper, we combine two models that optimize the energy consumption at household level and a market diffusion model for alternative fuel vehicles to address this aspect for Germany in 2030. Results show that the use of smart energy management systems can result in savings of up to €900–€1200 per year for households with heat pumps, photovoltaics, and electric vehicles, depending on the daily and annual mileage. The results of our analyses indicate that additional battery storage systems are unlikely to be cost-effective with electric vehicles in place. Market shares of battery electric vehicles can be increased, especially for households with small vehicles, where price sensitivities are highest. However, smart charging without solar power only returns small savings and has no impact on electric vehicle market shares.
As the global energy landscape evolves toward greater sustainability, batteries are becoming an increasingly important component in the integration of volatile energy sources. Stationary battery storage solutions offer significant benefits in commercial and industrial settings, including peak load reduction, increased self-consumption and optimised procurement with respect to dynamic electricity tariffs. The same capabilities are attributed to bidirectional battery electric vehicles that can serve as mobile battery storage in the context of commerce and industry, also referred to as “Vehicle-to-Business”. However, a review of existing literature reveals a significant gap in research concerning the combination and interaction between bidirectional battery electric vehicle fleets and stationary battery storage in various economic sectors. To address this gap, a simulation environment is used to evaluate three different tertiary sector use cases: Manufacturing, Office, and Health and Social Services, taking into account economic and technical indicators. A variation of fleet size, stationary battery storage capacity and battery electric vehicle charging strategies shows that bidirectional fleets and stationary battery storage can lead to varying degrees of cost savings in different use cases. Further investigation into the amortisation time in different scenarios indicates that investment recommendations regarding bidirectional fleets and/or stationary battery storage are highly sensitive to factors such as use case, fleet size, stationary battery storage investment level and investment year, as well as starting conditions such as greenfield or brownfield scenarios. Our work provides new insights into long-term investment analysis and the relationship between bidirectional battery electric vehicle fleets and stationary battery storage for various economic sectors.
Unsere Welt steht vor tiefgreifenden ökologischen Herausforderungen. Die gegenwärtige Art des Wirtschaftens und Lebens hat zu einer besorgniserregenden Erwärmung des Klimas, einem drastischen Rückgang der Biodiversität und zunehmenden Umweltbelastungen geführt. Diese Entwicklungen bedrohen nicht nur die natürlichen Lebensgrundlagen kommender Generationen, sondern verschärfen auch soziale und wirtschaftliche Ungleichheiten weltweit.
Als akademische Institution tragen wir eine besondere Verantwortung, nachhaltige Lösungen zu entwickeln, wissenschaftliche Erkenntnisse in die Praxis zu überführen und als Vorbild für eine zukunftsfähige Gesellschaft zu agieren. An der Hochschule Kempten sind wir uns dieser Verantwortung bewusst. Der Klimawandel ist nicht nur ein Problem der Zukunft – er betrifft uns bereits heute und erfordert entschlossenes Handeln auf allen Ebenen. Deshalb setzen wir mit unserem Klimaschutzkonzept ein klares Zeichen für Veränderung.
Dieses Konzept verfolgt das Ziel, Klimaschutz und Nachhaltigkeit systematisch in unsere Strukturen und Abläufe zu integrieren. Wir wollen nicht nur bestehende Initiativen intensivieren, sondern auch neue nachhaltige Strategien entwickeln, um den ökologischen Fußabdruck unserer Hochschule zu minimieren. Dazu gehört die Reduzierung von Emissionen, der effiziente Einsatz von Ressourcen und die Förderung eines Bewusstseinswandels innerhalb unserer Hochschulgemeinschaft.
Doch Klimaschutz ist mehr als eine technologische oder organisatorische Herausforderung – es ist eine gesamtgesellschaftliche Aufgabe, die unser Denken und Handeln grundlegend verändern muss. Wissenschaftlicher Fortschritt und gesellschaftliche Entwicklung dürfen nicht auf Kosten der Umwelt erfolgen. Vielmehr müssen Forschung, Lehre und Praxis Hand in Hand gehen, um langfristig tragfähige Lösungen zu entwickeln.
Dieses Klimaschutzkonzept ist das Ergebnis engagierter Zusammenarbeit vieler Beteiligter, denen wir unseren herzlichen Dank aussprechen möchten. Es lebt vom Mitwirken der Studierenden, Lehrenden und Mitarbeitenden. Gemeinsam können wir die Hochschule Kempten zu einem Vorbild für den Bereich der Nachhaltigkeit machen.