• Energy Research
• 2016-2025close
• GB close
• PL close
• Applied Energy close

Abstract Two office layouts with high and low levels of thermal control were compared, respectively traditional cellular and contemporary open plan offices. The traditional Norwegian practice provided every user with control over a window, blinds, door, and the ability to adjust heating and cooling. Occupants were expected to control their thermal environment to find their own comfort, while air conditioning was operating in the background to ensure the indoor air quality. In contrast, in the British open plan office, limited thermal control was provided through openable windows and blinds only for occupants seated around the perimeter of the building. Centrally operated displacement ventilation was the main thermal control system. Users’ perception of thermal environment was recorded through survey questionnaires, empirical building performance through environmental measurements and thermal control through semi-structured interviews. The Norwegian office had 35% higher user satisfaction and 20% higher user comfort compared to the British open plan office. However, the energy consumption in the British practice was within the benchmark and much lower than the Norwegian office. Overall, a balance between thermal comfort and energy efficiency is required, as either extreme poses difficulties for the other.

Abstract Two office layouts with high and low levels of thermal control were compared, respectively traditional cellular and contemporary open plan offices. The traditional Norwegian practice provided every user with control over a window, blinds, door, and the ability to adjust heating and cooling. Occupants were expected to control their thermal environment to find their own comfort, while air conditioning was operating in the background to ensure the indoor air quality. In contrast, in the British open plan office, limited thermal control was provided through openable windows and blinds only for occupants seated around the perimeter of the building. Centrally operated displacement ventilation was the main thermal control system. Users’ perception of thermal environment was recorded through survey questionnaires, empirical building performance through environmental measurements and thermal control through semi-structured interviews. The Norwegian office had 35% higher user satisfaction and 20% higher user comfort compared to the British open plan office. However, the energy consumption in the British practice was within the benchmark and much lower than the Norwegian office. Overall, a balance between thermal comfort and energy efficiency is required, as either extreme poses difficulties for the other.

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

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.

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.

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.