Domestic heating has a large share in the UK total energy consumption and significant contribution to the greenhouse gas emissions since it is mainly fulfilled by fossil fuels. Therefore, decarbonising the heating system is essential and an option to achieve this is by heating system electrification through heat pumps (HP) installation in combination with renewable power generation. Potential increase in performance and flexibility can be achieved by pairing HP with thermal energy storage (TES), which allows the shifting of heat demand to off peak periods or periods with surplus renewable electricity. We present a design and operational optimisation model which is able to assess the performance of HP-TES relative to conventional heating system. The optimisation is performed on a synthetic heat demand model which requires only the annual heat demand, temperature and occupancy profiles. The results show that the equipment and operational cost of a HP system without TES are significantly higher than for a conventional system. However, the integration of TES and time-of-use tariffs reduce the operational cost of the HP systems and in combination with the Renewable Heating Incentive make the HP systems cost competitive with conventional systems. The presented demand model and optimisation procedure will enable the design of low carbon district heating systems which integrate the heating system with the variable renewable electricity supply.

The unprecedented rise in cooling demand globally is a critical blind spot in sustainability debates. We examine cooling as a system comprised of active and passive measures, with key social and technical components, and explain its link to all 17 Sustainable Development Goals. We propose an analytical and solution-oriented framework to identify and shape interventions towards sustainable cooling. The framework comprehends demand drivers; cradle-to-cradle stages; and system change levers. By intersecting cooling stages and levers, we discuss four specific, exemplary interventions to deliver sustainable cooling. We propose an agenda for research and practice to transition towards sustainable cooling for all.

Purpose – This paper aims to present parametric models to estimate the environmental footprint of the selective laser sintering (SLS)’ production phase, covering energy and resource consumption as well as process emissions. Additive manufacturing processes such as (SLS) are often considered to be more sustainable then conventional manufacturing methods. However, quantitative analyses of the environmental impact of these processes are still limited and mainly focus on energy consumption. Design/methodology/approach – The required Life Cycle Inventory data are collected using the CO2PE! – Methodology, including time, power, consumables and emission studies. Multiple linear regression analyses have been applied to investigate the interrelationships between product design features on the one hand and production time (energy and resource consumption) on the other hand. Findings – The proposed parametric process models provide accurate estimations of the environmental footprint of SLS processes based on two design features, build height and volume, and help to identify and quantify measures for significant impact reduction of both involved products and the supporting machine tools. Practical implications – The gained environmental insight can be used as input for ecodesign activities, as well as environmental comparison of alternative manufacturing process plans. Originality/value – This article aims to overcome the current lack of environmental impact models, covering energy and resource consumption as well as process emissions for SLS processes.

One of the greatest challenges for long-term emissions reduction is the decarbonisation of heating and cooling due to the large scale, seasonal variation and distributed nature. Energy flexible buildings with electric heating, smart demand-side management and efficient thermal energy storage are one of the most promising strategies to deploy low-carbon technologies which can benefit the electricity system by reducing the need of reinforcing existing networks and their ability to use electricity in times of low demand and high supply. Combined with spot price contracts, in which the electricity tariff changes every half-hour depending on supply and demand, they can effectively reduce on-peak demand periods, achieve economic profits for end-users and retailers, and reduce the environmental impact of the electricity grid by operating in periods with lower CO2 emissions rate. To achieve these benefits, it is crucial to develop accurate models for energy flexible buildings as well as control strategies to optimise the complex system operation. This paper proposes a novel flexible energy building concept, based on smart control, high density latent heat storage and smart grids, able to predict the best operational strategy according to the environmental conditions, economic rates and expected occupancy patterns. The smart integration model, carried out in TRNSYS for a Scottish case study, solves a multi-criteria assessment based on future energy demand prediction (learning machine model supported by end-user's predefined occupancy by Internet of Things, present and forecast weather data, and building load monitoring), electricity tariff evolution and building performance. The results show that end-user's electricity bill savings of 20% are obtained and retailer's electricity cost is reduced by 25%. In addition, despite an increase in final energy consumption of up to 8%, the environmental impact remains constant due to operation at times with lower CO2 emissions rate of the electricity generation. The developed tools enable the design of smart energy systems for energy flexible buildings which can have a large, positive impact on the building sector decarbonisation.