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AbstractFor a commercial Carbon Capture and Storage (CCS) chain to be successful, it must satisfy a whole range of requirements: technical, economic, environmental, safety, and societal. A comprehensive, understandable and reproducible assessment of CCS projects is a complex task due to several reasons: wide range of actors and factors involved, substantial differences in the type and nature of both actors and factors, and numerous associated uncertainties. In this paper, a standardised methodology is described and illustrated on a few examples of relatively simple case studies. The proposed methodology provides means and tools for evaluation of several economic, environmental, and in the future also risk associated criteria and thereby enables selection of the most promising options for CCS. The methodology will also help to reduce the uncertainty by improving understanding of the most important dependencies and trends for the investigated key performance indicators as enlightened by the case studies examples. It could also help to design efficient incentives and measures to stimulate realization of CCS by identifying and evaluating the most important non-technical factors affecting the CCS chain viability.

Abstract In geothermal energy, a huge energy potential lies in hydrothermal reservoirs close to magma where ultra-high temperature fluids (>450 ° C) can be harnessed. Well casing system needs to be properly designed to ensure its integrity during its service lifetime. There currently exist neither any commonly accepted design tools nor standards regulating geothermal well design under such conditions. In this study a novel tool, Casinteg, was developed for structural analyses of geothermal wells. The tool is intended to bridge the gap between simplified analytic solutions and complex FE-based commercial software. The reliability and efficiency of the constitutive models implemented in Casinteg were verified in comparison with Abaqus. Casinteg's capability for structural analyses of full geothermal wells was preliminarily investigated, using IDDP-1 well as a case study. The calculated stress in the production casing was in a good agreement between Casinteg and Ansys models, while the computational time of Casinteg simulations was within minutes. Further developments are still needed. However, preliminary results were encouraging and have demonstrated the benefit of Casinteg for efficient structural analyses of full geothermal wells.

This paper studies the impact that different approaches of modeling the real-time use of the secondary regulation reserves have in the joint energy and reserve hourly scheduling of a price-taker pumped-storage hydropower plant. The unexpected imbalance costs due to the error between the forecasted real-time use of the reserves and the actual value are also studied and evaluated for the different approaches. The proposed methodology is applied to a daily-cycle and closed-loop pumped-storage hydropower plant. Preliminary results show that the deviations in the water volume at the end of the day are important when the percentage of the real-time use of reserves is unknown in advance, and also that the total income in all approaches after correcting these deviations is significantly lower than the maximum theoretical income.

The stability of vapour deposited Pt and Pd as electrocatalysts on fluorine doped, tin oxide coated glass for the use as counter electrodes in dye senzitized electrochemical solar cells has been investigated. The electrocatalytically active layers did not seem to be chemically stable in an electrolyte consisting of LiI and I2 dissolved in methoxy propionitrile. Thermodynamical calculations suggest the dissolution of Pt and Pd from the electrode surface to be caused by the formation of PtI4 and PdI6. Formation of complex species as PtI2−4 and PdI2−4 is less thermodynamically favoured, but PtI2−4 may also be present in the solution.

This paper presents a numerical analysis of the temperature profiles within the semiconductor devices of modular multilevel converters (MMCs). These temperature profiles are essential for assessing the power-cycling lifetime of IGBT modules, which influences the converter reliability. An electro-thermal simulation model has been established, combining a dynamic thermal model of the semiconductor devices with a detailed model of the MMC topology and its associated control loops. Conduction losses and switching losses in the semiconductors are calculated online in the simulation, using the instantaneous voltages and currents in the devices. The presented simulation results reveal low losses and limited thermal stresses in terms of peak-to-peak temperature variations in the devices, for both a 200-level MMC operating under staircase modulation, as well as for a 20-level MMC operating with pulsewidth modulation (PWM). This indicates that the application of conventional IGBT modules in high power MMCs is mainly constrained by the current switching capability and not by the power-cycling lifetime.

AbstractOffshore power systems will be characterized by a large penetration of power electronics converters and power cables, resulting in complex behaviors that can be quite different from traditional AC systems. Analyzing such systems is challenging due to their complexity and the relative lack of experience. In this context, performing laboratory tests in scaled down model network have significant relevance to verify simulation models and to test equipment and control methods in a controlled environment. The design of the laboratory needs to be a compromise between the capability to accurately reproduce the real system behavior and the practical constraints on ease of use, equipment size, cost and safety. This paper summarizes the main design considerations for a laboratory infrastructure dedicated to research of future offshore grids and the possible challenges. The laboratory facility developed jointly by NTNU (Norwegian University of Science and Technology) and SINTEF Energy Research is described with examples of the experimental possibilities.

Abstract The extension from Heat Integration (HI) and design of Heat Exchanger Networks (HENs) to including heating and cooling effects from pressure changing equipment has been referred to as Work and Heat Integration and design of Work and Heat Exchange Networks (WHENs). This is an emerging research area of Process Synthesis, however, WHENs is a considerably more complex design task than HENs. A key challenge is the fact that temperature changes (related to heat) and pressure changes (related to work) of process streams are interacting. Changes in inlet temperatures to compressors and expanders resulting from heat integration will influence work consumption and production. Likewise, pressure changes by compression and expansion will change the temperatures of process streams, thus affecting heat integration. As a result, Composite and Grand Composite Curves will change shape due to pressure changes in the process. The thermodynamic path of process streams from supply (pressure, temperature) to target state is not known and depends on the sequence of heating, cooling, compression and expansion. This paper introduces a definition and describes the development of WHENs. Future research challenges related to methodology development and industrial applications will be addressed. The potential of WHENs will be indicated through examples in literature.

Abstract A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a building on a certain time horizon with a good confidence are essential for the implementation of effective control systems. This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings, aiming at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Recent advancements in deep learning have enabled the development of more sophisticated forecasting models compared to traditional feedback control systems. The proposed global Transformer architecture can be trained on the entire dataset encompassing all rooms, eliminating the need for multiple room-specific models, significantly improving predictive performance, and simplifying deployment and maintenance. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings. The proposed approach provides a novel solution to enhance the accuracy and efficiency of temperature forecasting, serving as a valuable tool to optimize energy consumption and decrease greenhouse gas emissions in the building sector.