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Abstract: This paper presents the results of a recent research effort to collect and analyze data pertaining to traditional bath buildings (“hammams”) in Egypt, Turkey, Morocco, Syria, and Algeria. Thereby, the energy performance of and the thermal comfort conditions in five such buildings were studied. Moreover, empirically calibrated building performance simulation models of a number of objects were generated in order to predict the consequences of alternative thermal retrofit measures. The results provide the opportunity for an objective assessment of the actual energy and indoor environmental performance of these buildings. [Copyright &y& Elsevier]

Energy systems need to decarbonize rapidly whilst satisfying heating and cooling needs. In Switzerland, residential cooling has so far only a small impact on the national energy demand, but climate change and a larger uptake of cooling devices are expected to lead to future increases. This requires novel approaches for sustainable cooling solutions suitable for implementation at a national scale. Here, we explore the potential of night ventilation and window shading to reduce the buildings cooling demand in a changing climate. A physical bottom-up approach is used to simulate residential space cooling demand and to identify the passive cooling potential whilst considering a detailed representation of the Swiss building stock, featuring building age, construction properties, regional climate, urban layout and occupant behaviour. A supervised building type classification approach is applied to enable up-scaling to the national level. Results show that in 2050, the residential Swiss building stock will require a national cooling demand of around 10.2 TWh for a Representative Concentration Pathway scenario 4.5. Under such future climatic conditions, we simulated a potential to reduce the total cooling demand by 84%, with both passive cooling solutions combined. Individually, window shading could reduce cooling demands by 71% and night ventilation by 38%. We found that newer buildings (built after 2000) already account for about 50% of the total current cooling energy demand. Results demonstrate that night ventilation and window shading have the potential to mitigate the impact of climate change in Switzerland and to improve the sustainability and resilience of residential cooling. [Display omitted] • 10.2 TWh cooling demand is simulated for Swiss residential sector by mid-century. • Passive cooling measures have the potential to reduce future cooling demand by 84%. • An extensive and comprehensive bottom-up methodology is applied for Switzerland. • A morphological building classification for cooling energy is applied for upscaling. • Peak cooling demand can roughly be halved by night ventilation and window shading. [ABSTRACT FROM AUTHOR]

Efforts to reduce carbon intensity of electricity involve increasing shares of variable renewable energy (VRE), including a trend towards decentralized generation. Ensuring their high utilization necessitates addressing generation variability at different spatial and temporal scales, likely involving a coordination of multiple systems (e.g. flexible loads, storage, dispatchable generation). This paper presents a methodology for estimating the energy flexibility required from such a synchronized system. The simple data-based approach uses local load and VRE production patterns, and provides a regional assessment considering (1) indicators to evaluate energy flexibility requirements based on VRE self-consumption (2) visualization methods to observe consumption and production patterns' impact on flexibility requirements across day/week/year and (3) simple algorithms to estimate the energy flexibility requirements at different timescales. The approach is demonstrated for three Swiss distribution system operators, for both current and increased shares of VRE generation. The largest benefits for optimal self-consumption are realized for the energy flexibility timescale of 6–12 hrs. Medium- and long-term (seasonal) storage appears beneficial at VRE penetration levels beyond 40%. The proposed framework can be readily utilized to assess energy flexibility requirements of different regions, and serve as a basis for identifying a suitable mix of strategies needed to address them. • Variable renewable energy raises flexibility needs at various spatiotemporal scales. • Novel metrics evaluate energy flexibility needs using aggregated time-series data. • They consider the effect of spatial and temporal mismatch on renewable energy use. • A simple algorithm estimates how they evolve with increased shares of renewables. • As proof-of-concept, the method is applied to multiple case studies in Switzerland. [ABSTRACT FROM AUTHOR]

The energy strategies used to plan future emissions reductions typically have separate strategies for decarbonisation of the transport and building sectors. However, the advent and diffusion of alternative fuel vehicle technologies could result in vehicle transport becoming a major energy load in urban energy systems in addition to heating and electricity. The building and personal transport sectors both have a large potential for CO 2 emissions reductions, however the energy must come from mainly renewable sources. In this work, a multi-objective optimisation model is developed that considers a multi-energy system and minimises both the total cost and life-cycle emissions of both the buildings and vehicles. The model selects both the conversion and storage technologies that the community uses to produce electricity and heat, as well as vehicle powertrain types. The resulting Pareto solutions rely on transitions away from internal combustion engine vehicles to battery electric vehicles, and to a much lesser extent plug-in hybrid vehicles. The heating energy is decarbonised with a transition to heat pumps and away from gas boilers. The optimisation model is tested with 77 vehicles and 50 buildings, and results shows that the emission reductions could be as high as 79% by 2035 and 85% by 2050. • Multi-energy system optimisation with integrated building and vehicle charging systems. • Optimal selection and operation of conversion, storage, and vehicle powertrain technologies. • Constraints developed for vehicle driving profiles and vehicle WLTP driving cycle performance. • Battery, thermal storage, and bi-directional EV charging all essential for reaching targets. • Multi-objective optimisation minimising both costs and life-cycle emissions utilised. [ABSTRACT FROM AUTHOR]

[Display omitted] • The techno-economic potential of lake-source district systems is assessed for Europe. • The European potential is 1.9 TWh/y supplying only cooling, 11.3 TWh/y adding heating. • Lake-source thermal networks could save up to 0.8 TWh of electricity per year. • The impact of lake-source systems on temperature in most European lakes is minor. Lake-source thermal district networks can efficiently supply heating and cooling to buildings and thus save energy and CO 2 emissions. However, it remains unclear to which degree they are a sustainable alternative at a larger geographical scale. An evaluation of the potential of developing technically and economically feasible lake-source district systems in Europe was performed in this study, with an integrated spatial explicit techno-economic assessment that accounts for different boundary conditions, such as electricity price, CO 2 price and climate change. The feasibility of covering building energy demand near lakes was found to be particularly sensitive to the relationship between capital costs from network design and operational costs from heat pumps, associated electricity consumption and CO 2 emissions. Results suggest a European techno-economic potential of 1.9 TWh/y considering only direct cooling and 11.3 TWh/y if thermal networks supply both direct heating and cooling by heat pumps. Respective electricity savings are 0.36 TWh/y and 0.78 TWh/y. An estimated 17% of the cooling demand near European lakes can thus be covered by viable cooling-only lake-source systems. For combined systems, the viable potential is estimated to be 7% of the total combined heating and cooling demand. Lake-source district systems are found to be particularly promising for Italy, Germany, Turkey and Switzerland. The integration of lake-source thermal networks should rarely lead to severe lake water temperature alteration and therefore not limit the techno-economic potential. The introduced methodology allows for a combined evaluation of technological, ecological and economic boundary conditions for using lakes as a source for district heating and cooling. Thereby, a more realistic estimation of their potential implementation becomes possible, enabling informed energy planning for central or decentral system configurations. [ABSTRACT FROM AUTHOR]

[Display omitted] Energy demand of buildings forms a key component of energy system analyses. To integrate building energy demand into energy system models, detailed simulation of every single building is impractical and often computationally infeasible. Simulations are therefore typically performed on a sample of buildings for bottom-up analysis. For a simplified representation of the building stock, grouping and clustering are widely applied. However, a spatiotemporal quantification of the resulting upscaling errors is however lacking. Here, a grouping and clustering approach is performed for all Swiss buildings for bottom-up energy demand analysis. The spatiotemporal simulation error is quantified at the national, regional and neighbourhood (300×300 m) scales. Whereas national energy demands are well represented, the error induced by the grouping and clustering is significant at the neighbourhood scale: simulated demand is easily over or underestimated by double-digit percentages. Obtained regional full load hours and daily load factor values by a grouping and clustering approach are comparable to a full simulation of all buildings. However, careful consideration must be given to the used climate data if relying on clustering approaches. Our analysis reveals the challenge of representing peak demands well by aggregation of building demand profiles, which is exacerbated by grouping and clustering. Bottom-up studies thus need to consider errors arising from the grouping and clustering approach and distinguish between errors resulting from the upscaling process and other sources of uncertainty such as data inputs or modelling concepts. Finally, calculated energy signatures are shown to perform well as a fast and viable alternative to grouping and clustering. [ABSTRACT FROM AUTHOR]