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Göttinger Forum für Altertumswissenschaft, Bd. 24 Nr. 1 (2021): Göttinger Forum für Altertumswissenschaft

Solar field developers include innovative solutions to optimize the energy production of their plants. Simulation tools play a significant role in the design and testing phases as they provide estimations of this yield in different conditions. Transient processes, like passing clouds and solar field start-up, are specifically challenging to optimize and estimate using such simulation tools. Solar fields are subject to high degree of both temporal and spatial variability in the energy input and a detailed estimation can be achieved by simulating subsystems within acceptable time and computational power. Hence, such simulation tools cannot be utilized for tests under realistic operation conditions. The Virtual Solar Field is a computationally efficient simulation tool that allows a detailed transient simulation of parabolic trough solar fields based on single-phase fluids. Using this tool, developers could reproduce a transient test case with exactly the same disturbances to provide fair comparisons between different configurations. In this paper, an evaluation process based on numerical simulations using the Virtual Solar Field is presented. The economic benefit of novel innovative control concepts can be assessed according to the presented scheme. This is demonstrated by evaluating the potential benefit of availability of spatial DNI nowcasts on the control of parabolic trough solar fields. Results show that nowcasting can increase the economic revenue of commercial power plants by up to 2.5% per day. This proves the feasibility of installing such systems.

Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method (up to 4% of improvement in comparison to an approach that does not consider uncertainties), emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way.

Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in market conditions prediction and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in whole-sale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine learning approach, the proposed method develops a financial optimum schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method, emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

\(\bf Background\) Chronic rejection remains the Achilles heel in vascularized composite allotransplantation. Animal models to specifically study chronic rejection in vascularized composite allotransplantation do not exist so far. However, there are established rat models to study chronic rejection in solid organ transplantation such as allogeneic transplantation between the rat strains Lewis and Fischer344. Thus, we initiated this study to investigate the applicability of hindlimb transplantation between these strains to imitate chronic rejection in vascularized composite allotransplantation and identify potential markers. \(\bf Methods\) Allogeneic hindlimb transplantation were performed between Lewis (recipient) and Fischer344 (donor) rats with either constant immunosuppression or a high dose immunosuppressive bolus only in case of acute skin rejections. Histology, immunohistochemistry, microarray and qPCR analysis were used to detect changes in skin and muscle at postoperative day 100. \(\bf Results\) We were able to demonstrate significant intimal proliferation, infiltration of CD68 and CD4 positive cells, up-regulation of inflammatory cytokines and initiation of muscular fibrosis in the chronic rejection group. Microarray analysis and subsequent qPCR identified CXC ligands 9–11 as potential markers of chronic rejection. \(\bf Conclusions\) The Fischer344 to Lewis hindlimb transplantation model may represent a new option to study chronic rejection in vascularized composite allotransplantation in an experimental setting. CXC ligands 9–11 deserve further research to investigate their role as chronic rejection markers.