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This paper provides a performance comparison ejector technology to be installed in a CO2 supermarket refrigeration system. Namely, the fixed ejectors with and without swirl flow at the inlet of the motive and suction nozzles and the fixed and controllable ejectors equipped with convergent and convergent-divergent nozzles and were analysed. The Coefficient of Performance (COP) of the ejector-based refrigeration system is dependent on the design and control of the mass flow rate through this device. The literature suggests that one of the possible ejector efficiency improvements can be performed using a swirl generator at the inlet of the motive nozzle. Therefore, a swirl generator was simulated for different size CO2 ejectors under typical operating conditions in a supermarket and for a range of rotational speeds. In addition, the performance of a group of fixed ejectors working in parallel was compared with two controllable ejectors with a needle. To analyse the potential of the considered approaches, the device performance was numerically simulated using the validated Homogeneous Equilibrium Model (HEM) model for the same typical transcritical parameters. For all the fixed ejectors with and without the swirl generator, devices of various sizes were simulated and compared with the corresponding controllable ejector performance at a few needle positions. For both ejector types, a global efficiency as a function of the mass flow rate was presented and discussed.

In refrigeration and power cycles processes the use of air as heatsink for condensing, when large quantities of water are not available, is a common practice and even more frequent. Although ambient air has the great advantage of being available everywhere and in unlimited quantity, it also has numerous disadvantages: highly variable temperatures, low exchange coefficients, need to use of large heat exchange surfaces and large air quantities. To limit these drawbacks, LU-VE has developed an innovative solution named EMERITUS. This technology has a fan-cooled exchanger on which two additional water cooling systems are used in sequence: treated water is sprayed onto the heat exchanger coil and the remaining non-evaporated is collected and used on to the adiabatic panel. This combination of the two techniques has positive effects on both the thermal capacity exchanged and the quantity of water consumed. The article describes the functional principle of the new EMERITUS and analyses a case study in which the running costs of a water chiller combined with EMERITUS are compared with other traditional solutions.

By using a liquid desiccant ventilation system for dehumidification and an air-handling unit for cooling, the liquid desiccant cooling system (LDCS) system became a promising alternative for traditional technology. Solar thermal energy is suitable to deal with the heat requirement of LDCS in buildings, especially in the areas with abundant solar radiation. The energy saving of solar-assisted liquid desiccant air-conditioning system is significantly affected by various operation conditions, and multi-parameter optimization was necessary to improve the system applicability. In this paper, we investigated the impact of five main parameters on the system performance via self-developed system modelling, including the solution mass flow rate, concentration, cooling tower flow rate, and solar water flow rate and installation area of solar collector. A typical commercial building in Hong Kong was selected as a case study, which air-conditioning load was obtained by Energy-plus. The results indicated that the installation area of solar collector showed the greatest impact, and the effect of heating water flow rate was also important. The effect of desiccant flow rate was significant, but the influence of solution concentration was slight. Then, the multi-parameter optimization was conducted for obtaining a maximum annual electricity saving rate based on the Multi-Population Genetic Algorithm. The optimized installation area of solar collector was 72 m2, and the heating water flow rate was 0.66 kg/s. The optimized solution flow rate was 0.17 kg/s. The required cooling water flow rate was around 0.8 kg/s.

The dataset includes the electrical properties mesurements of SOFC with CeO2-s layer. Samples were produced using aqueous soft chemistry methods (microemulsion method) and applied in form of a layer onto the anode of the commercial SOFC. The SOFC was working under biogas feeding at 750oC. The layers were sintered at 1100oC. 'A fuel cell mounted in a measuring rig was put into the tubular furnace and heated up to 800 oC in argon atmosphere and then the gas was switched to hydrogen to perform anode’s reduction for 30 min. After initial reduction, the temperature was decreased to 750 oC and electrical measurements were taken at this temperature. After 18 h of operation in hydrogen the fuel was switched to synthetic biogas and the analysis was continued for 90 h. Two types of data from electrical measurements were collected, a current density versus voltage and a current density versus time at static load of 0.65 V.' (10.1016/j.ijhydene.2020.02.144Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie))

This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

The increased global demand for energy will require additional tools to help guide policy and management actions to conserve wildlife. Grouse (Tetraoninae) are adversely affected by infrastructure associated with energy development, but the magnitude of effects are difficult to quantify in a singular management prescription. Advancement in monitoring and analysis techniques have allowed researchers to evaluate complex questions surrounding the effects of infrastructure on grouse populations, rapidly increasing our knowledge. To better inform management decisions, especially with the emergence of renewable energy, a quantitative synthesis of previous research evaluating the effects of infrastructure on grouse populations is needed. We reviewed studies evaluating the effect of energy infrastructure on grouse, with the main objective to determine the magnitude of effect on grouse lek attendance, resource selection, and survival to help inform future conservation actions. We modeled slope coefficients for distance to energy infrastructure, standardized by scale, on various behaviors to determine overall effect sizes in a meta-analysis. We used 93 study-result combinations from 21 studies that directly evaluated resource selection, survival, or lek attendance relative to energy infrastructure. Trends in overall effect sizes suggest an adverse effect of distance to energy infrastructure on grouse behavior; however, the combination of non-significant pooled regression slopes and high among-study heterogeneity suggest the effect of distance to energy infrastructure is context dependent. While distance to infrastructure is a common metric used in many grouse management plans, our results suggest distance to infrastructure may not be a reliable predictor of grouse behavior and the effect is context dependent making management prescriptions based solely on distance to infrastructure in a one size fits all approach difficult. Our analysis points to numerous aspects that scientists can improve upon by evaluating density in conjunction with distance to energy infrastructure as well as reporting the necessary statistics for future meta-analyses. Data was obtained from peer-reviewed literature. We extracted regression coefficients, standard erros or confidence intervals, and sample sizes from statistical models that evaluated resource selection, survival, and lek attendance relative to distance to energy infrastructure. We contacted authors that assessed the effects of distance to energy infrastructure on grouse if they did not report relevant statistics. 

This dataset contains data and codes required to replicate the results in the article "Joint assessment of generation adequacy with intermittent renewables and hydro storage: A case study in Finland" to be published in Electric Power Systems Research. See the enclosed Readme for further instructions.

QTDIAN - Quantification of Technological DIffusion and sociAl constraiNts - is a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition that influence the overall potential, the rate of energy-related technology and service diffusion and the design of the future energy system. The output of QTIDIAN is empirically founded datasets of social and political drivers and barriers of the transition, both in the form of raw data describing past and current developments and manipulated to constitute consistent quantifications of the storylines. Here you can download the data for six QTDIAN themes: Socially feasible scaling of energy technologies Policy preferences & dynamics Barriers to infrastructural development (wind energy, grid development) Citizen energy Private energy demand Further information on the QTDIAN modelling toolbox and the data can be found in the SENTINEL Deliverable 2.3 and Deliverable 2.4: S��sser, D., al Rakouki, H., & Lilliestam, J.(2021). The QTDIAN modelling toolbox���Quantification of social drivers and constraints of the diffusion of energy technologies. Deliverable 2.3. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS). S��sser, D., Pickering, B., Chatterjee, S., Oreggioni, G., Stavrakas, V., & Lilliestam, J.(2021). Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project. Potsdam: Institute for Advanced Sustainability Studies (IASS).

Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

Hourly steam use data for all significant buildings from the fiscal year (FY) 2017 (1 July 2016 - 30 June 2017) are provided. The total annual campus heating load in FY 2017 was about 0.81 trillion Btu (283,000 MWth-hrs). The data are presented as a spreadsheet, detailing on an hourly basis the total steam utilized for all buildings in each of three categories of buildings. Also available are conversions of that steam mass to Megawatts-thermal (MWth). For the same hour, the outdoor temperature, relative humidity, and enthalpy values are listed.