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Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability.\ud \ud This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

The optimal selection, sizing, and location of small-scale technologies within a grid-connected distributed energy system (DES) can contribute to reducing carbon emissions, consumer costs, and network imbalances. This is the first study to present an optimisation framework for obtaining discrete technology sizing and selection for grid-connected DES design, while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution networks. An algorithm is developed to solve the resulting Mixed-Integer Nonlinear Programming (MINLP) formulation. It employs a decomposition based on Mixed-Integer Linear Programming (MILP) and Nonlinear Programming (NLP), and utilises integer cuts and complementarity reformulations to obtain discrete designs that are also feasible with respect to the network constraints. A heuristic modification to the original algorithm is also proposed to improve computational speed. Improved formulations for selecting feasible combinations of air source heat pumps (ASHPs) and hot water storage tanks are also presented. The algorithms outperform the existing state-of-the-art commercial MINLP solver, which fails to find any solutions in two instances. While feasible solutions were obtained for all cases, convergence was not achieved for all, especially for those involving the larger network. Where converged, the algorithm with the heuristic modification has achieved results up to 70% faster than the original algorithm. Results for case studies suggest that including ASHPs can support up to 16% higher renewable generation capacity compared to gas boilers, albeit with higher ASHP investment costs. The optimisation framework and results can be used to inform stakeholders such as policy-makers and network operators, to increase renewable energy capacity and aid the decarbonisation of domestic heating systems. 47 pages, 10 figures, 14 Tables

Feature-based machine learning models for capacity and internal resistance (IR) curve prediction have been researched extensively in literature due to their high accuracy and generalization power. Most such models work within the high frequency of data availability regime, e.g., voltage response recorded every 1–4 s. Outside premium fee cloud monitoring solutions, data may be recorded once every 3, 5 or 10 min. In this low-data regime, there are little to no models available. This literature gap is addressed here via a novel methodology, underpinned by strong mathematical guarantees, called ‘path signature’. This work presents a feature-based predictive model for capacity fade and IR rise curves from only constant-current (CC) discharge voltage corresponding to the first 100 cycles. Included is a comprehensive feature analysis for the model via a relevance, redundancy, and complementarity feature trade-off mechanism. The ability to predict from subsampled ‘CC voltage at discharge’ data is investigated using different time steps ranging from 4 s to 4 min. It was discovered that voltage measurements taken at the end of every 4 min are enough to generate features for curve prediction with End of Life (EOL) and its corresponding IR values predicted with a mean absolute percentage error (MAPE) of approximately 13.2% and 2.1%, respectively. Our model under higher frequency (4 s) produces an improved accuracy with EOL predicted with an MAPE of 10%. Full implementation code publicly available.

Abstract Most of the current Stirling-type pulse tube refrigerators (PTRs) adopt inertance tubes with large reservoirs for phase shifting. Recovering the acoustic power dissipated in the inertance tube provides a great potential for improving the efficiency of a PTR. In this study, an inertance tube PTR is modified by replacing the dissipative inertance tube and reservoir with a mass-spring displacer directly coupled to a compression space. Numerical simulations are conducted on both the PTRs based on a validated one-dimensional computational fluid dynamics model. Optimization of the inertance tube PTR shows that the coefficient of performance (COP) is limited within 0.103 at the cooling temperature of 77 K. The simulation of the PTR with the feedback mechanism indicates that COP can be significantly improved due to the extra power recovered by the mass-spring displacer. The parametric analyses of the moving mass, spring stiffness, mechanical resistance, piston diameter, and working frequency of the mass-spring displacer are finally performed. The phase relations at both ends of the regenerator are significantly influenced by the geometric and operating parameters, which further affect the performance. The designing parameters have been optimized, COP reaches about 0.13–0.14 with the relative Carnot COP of around 0.4. It demonstrates that adopting the mass-spring displacer to feed the expansion power back into the compression space is an effective way of improving the performance of PTRs. This work provides comprehensive understanding of the mechanisms and characteristics of the PTRs with the mass-spring displacer. It would be helpful for future designs of such systems.

With increasing air traffic, rising fuel costs and tighter environmental targets, efficient airport ground operations are one of the key aspects towards sustainable air transportation. This complex system includes elements such as ground movement, runway scheduling and ground services. Previously, these problems were treated in isolation since information, such as landing time, pushback time and aircraft ground position, are held by different stakeholders with sometimes conflicting interests and, normally, are not shared. However, as these problems are interconnected, solutions as a result of isolated optimisation may achieve the objective of one problem but fail in the objective of the other one, missing the global optimum eventually. Potentially more energy and economic costs are thus required. In order to apply a more systematic and holistic view, this paper introduces a multi-objective integrated optimisation problem incorporating the newly proposed Active Routing concept. Built with systematic perspectives, this new model combines several elements: scheduling and routing of aircraft, 4-Dimensional Trajectory (4DT) optimisation, runway scheduling and airport bus scheduling. A holistic economic optimisation framework is also included to support the decision maker to select the economically optimal solution from a Pareto front of technically optimal solutions. To solve this problem, a multi-objective genetic algorithm is adopted and tested on real data from an international hub airport. Preliminary results show that the proposed approach is able to provide a systematic framework so that airport efficiency, environmental assessment and economic analysis could all be explicitly optimised.

Abstract An enhanced TRNSYS simulation model, NEM, of the behaviour of a domestic hot-water (DHW) store, with an immersed heat-exchanger (HX), has been developed and validated. This model simulates the dynamic heat-depletion and recovery processes in the immersed HX and predicts the transient temperature-patterns for various DHW draw-off versus time profiles. Realistic daily profiles (RDPs), based on field studies, were developed to provide representative draw-off patterns for the testing of thermal stores and simulation studies. The effects of these RDPs and five other existing profiles on the store’s performance are analysed using the enhanced model. The simulation results indicate the importance of the HX’s recovery, as well as the number, type and time of occurrence of the draw-offs in the profile, on the thermal store’s performance. It is concluded that RDP profiles should be used in the performance testing of thermal stores to obtain results that reflect conditions experienced in the field.

Building-Integrated Concentrated Photovoltaics (BICPV) is based on Photovoltaic (PV) technology which experience a loss in their electrical efficiency with an increase in temperature that may also lead to their permanent degradation over time. With a global PV installed capacity of 303 GW, a nominal 10 °C decrease in their average temperature could theoretically lead to 15 GW increase in electricity production worldwide. Currently, there is a gap in the research knowledge concerning the effectiveness of the available passive thermal regulation techniques for BICPV, both individually and working in tandem. This paper presents a novel combined passive cooling solution for BICPV incorporating micro-fins, Phase Change Material (PCM) and Nanomaterial Enhanced PCM (n-PCM). This work was undertaken with the aim to assess the unreported to date benefits of introducing these solutions into BICPV systems and to quantify their individual as well as combined effectiveness. The thermal performance of an un-finned metallic plate was first compared to a micro-finned plate under naturally convective conditions and then compared with applied PCM and n-PCM. A designed and fabricated, scaled-down thermal system was attached to the electrical heaters to mimic the temperature profile of the BICPV. The results showed that the average temperature in the centre of the system was reduced by 10.7 °C using micro-fins with PCM and 12.5 °C using micro-fins with n-PCM as compared to using the micro-fins only. Similarly, the effect of using PCM and n-PCM with the un-finned surface demonstrated a temperature reduction of 9.6 °C and 11.2 °C respectively as compared to the case of natural convection. Further, the innovative 3-D printed PCM containment, with no joined or screwed parts, showed significant improvements in leakage control. The important thermophysical properties of the PCM and the n-PCM were analysed and compared using a Differential Scanning Calorimeter. This research can contribute to bridging the existing gaps in research and development of thermal regulation of BICPV and it is envisaged that the realised incremental improvement can be a potential solution to (a) their performance improvement and (b) longer life, thereby contributing to the environmental benefits.