• Energy Research
• 2021-2025close
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Abstract Fundamental studies on Flame-Wall Interaction (FWI) are of the utmost importance to unravel the intricate coupling between chemistry and transport in the near-wall region, and to characterize the quenching dynamics. For this purpose, an accurate description of reaction kinetics is especially needed. In this work, the role of chemistry during the wall quenching of premixed dimethyl ether (DME) flames is numerically investigated, leveraging recently-obtained experimental data in a Side-Wall Quenching configuration (SWQ) in a variety of conditions. A detailed kinetic model describing the low-to-high temperature oxidation of DME is used as starting point. In order to accommodate it into 2D simulations, it is first reduced to a skeletal level, both accounting for (31 species and 140 reactions) and neglecting (20 species and 93 reactions) low-temperature chemistry, and successfully validated against the original model in predicting flame propagation and Head-On Quenching (HOQ). Using an established CFD framework, the SWQ features of DME are investigated in terms of flame structure, heat fluxes and thermochemical CO-T states. A good agreement with the available experimental data is observed in the operating space. In the near-wall region, the prediction of the thermochemical state is most critical, and it is found that diffusion is here the prevailing contribution in defining such state. In the same region, kinetic analysis shows that low-temperature chemistry affects the speciation of the quenching region to a good extent, which is modified with respect to a freely-propagating flame. Yet, due to the lower reactivity, SWQ thermal features are not significantly influenced. As a result, for the purposes of the wall quenching analysis, only high-temperature chemistry needs to be accounted for, and the 20-species mechanism can be safely considered as a consolidated milestone for future studies.

New energy generation and storage systems are continuously being developed due to climate change, resource scarcity, and environmental laws. Some systems are incremental innovations of existing systems while others are radical innovations. Radical innovation systems are risky investments due to their relevant technical and economic uncertainties. Prototyping can hedge these risks by spending a fraction of the cost of a full-scale system and in return receiving economic and technical information regarding the system. In economic terms, prototyping is an option to hedge risk coming at a cost that needs to be properly assessed. Real options analysis is the project appraisal approach for these assessments. This paper aims to introduce and test an algorithm based on real options analysis to quantitatively assess the “option to prototype” in the energy sector. First, the interrelated research areas of prototyping, energy systems, and real options analysis are reviewed. Then, a novel algorithm is presented and applied to an innovative Generation Integrated Energy Storage system: Wind-driven Thermal Pumping to demonstrate the effectiveness of option to prototype and the main parameters influencing this decision. Results show that the cost of the prototype and the market size (number of identical systems to build) are key parameters.

Within the Best Estimate Plus Uncertainty framework for the safety analysis of Nuclear Power Plants, the quantification of the uncertainties affecting the Thermal-Hydraulics (T-H) codes used is crucial. For this, Inverse Uncertainty Quantification (IUQ) methodologies are being developed for determining the probability density functions of relevant T-H codes input parameters, based on experimental data from Separate Effect Tests (SETs) experimental facilities. In practice, IUQ is challenged by the large range of variability of the experimental data in terms of Initial and Boundary Conditions (ICs & BCs), because the experimental campaigns are designed to cover the widest possible domain of conditions with the smallest number of experiments, so that same or similar ICs and BCs are seldomly repeated. To address this issue, we propose to use global sensitivity analysis, to tailor the IUQ on specific sub-regions described by segmented ICs & BCs domains. The methodology proposed is exemplified on two SETs, namely Sozzi-Sutherland and Super Moby Dick, whose experimental databases have been made available in the ATRIUM (Application Tests for Realization of Inverse Uncertainty quantification and validation Methodologies in thermal hydraulics) project promoted by the OECD/NEA/CSNI. The results obtained are superior to those of traditional IUQ methodologies for models highly sensitive to ICs & BCs.

Liquid metals are promising heat transfer fluids since they remain liquid in a wide temperature range and can transfer heat efficiently due to their high thermal conductivity. A first-of-its-kind lab-scale thermal energy storage system with filler material and with lead-bismuth as heat transfer fluid is currently tested at the Karlsruhe Institute of Technology, while a 100-kWh storage system is under construction. This numerical study aims to analyse the influence of the filler parameters on the system's efficiency when the fluid used is a liquid metal. The filler should store part of the thermal energy, be efficiently discharged during the cyclic process and buffer the degradation of the thermocline during standby. For each of these purposes different particle diameters and values of some thermophysical properties of the filler, such as thermal conductivity, specific heat capacity and density, may be advantageous. Their influence on the thermocline extension is numerically investigated using a one-dimensional concentric dispersion model. The results of the parameter study show that for an efficient discharge process in a liquid metal dual-media storage, a small filler particle size is beneficial (d< 10 mm for the reference case chosen in this work). In contrast, the standby phase is favoured by larger diameters, here an order of 10–20 mm. Furthermore, a high thermal conductivity of the filler material improves the discharge performance, due to the enhanced heat transfer, but leads to an accelerated growth of the thermocline during standby. For this case, the optimum value is 5–10W/mK. Moreover, using a filler material with a high volumetric heat capacity leads to the best overall performance. A full factorial analysis shows that the filler diameter has the strongest effect on the discharge behaviour, while, during standby, the volumetric heat capacity has the largest influence.

The Social Sciences and Humanities (SSH) have a key role to play in understanding which factors and policies would motivate, encourage and enable different actors to adopt a wide range of sustainable energy behaviours and support the required system changes and policies. The SSH can provide critical insights into how consumers could be empowered to consistently engage in sustainable energy behaviour, support and adopt new technologies, and support policies and changes in energy systems. Furthermore, they can increase our understanding of how organisations such as private and public institutions, and groups and associations of people can play a key role in the sustainable energy transition. We identify key questions to be addressed that have been identified by the Platform for Energy Research in the Socio-economic Nexus (PERSON, see person.eu), including SSH scholars who have been studying energy issues for many years. We identify three main research themes. The first research theme involves understanding which factors encourage different actors to engage in sustainable energy behaviour. The second research theme focuses on understanding which interventions can be effective in encouraging sustainable energy behaviour of different actors, and which factors enhance their effects. The third research theme concerns understanding which factors affect public and policy support for energy policy and changes in energy systems, and how important public concerns can best be addressed as to reduce or prevent resistance.

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

The reliability analysis of IESs (Integrated Energy Systems) is a complicated task because of the complex characteristics of different subsystems and the multi-scale dynamics that develop therein. To effectively address such problems, this paper proposes a systematic framework to analyse the reliability of energy supply in IESs, considering the dynamics of IESs and the inter-relationships among uncertainties. First, based on the linepack-based performance analysis model of IES, a quasi-steady-state model is established to model the dynamic behaviours in IESs, properly accounting for practical engineering and operational strategies. Then, considering the inter-correlations among different uncertainty sources and time-dependent relationships of each variable, a model that combines the statistical structure of copula with the machine learning method of stacked autoencoder (CSML) is adopted to establish the timely multivariate joint distributions for variables. Monte Carlo simulation combined with Order Statistics is used for assessing supply reliability. Case studies are performed on a realistic IES that combines an IEEE-15 power system with an 18-node natural gas pipeline network. The efficiency and accuracy of the quasi-steady-state model are validated. The reliability evaluation results show that the inter-correlations among variables and time-dependent relationships of each variable have great effects on the system reliability assessment. The consideration of linepack can significantly improve the supply reliability of IES whereas the management strategy of linepack may lead to some risky points. © 2022