The Experts below are selected from a list of 63297 Experts worldwide ranked by ideXlab platform
Magnus Åberg - One of the best experts on this subject based on the ideXlab platform.
-
investigating the impact of Heat Demand reductions on swedish district Heating production using a set of typical system models
Applied Energy, 2014Co-Authors: Magnus ÅbergAbstract:Abstract The European Union (EU) aims at reducing its CO 2 emissions and use of primary energy. The EU also aims to improve the energy efficiency in buildings and promote the use of combined Heat and power (CHP) plants in district Heating (DH) systems. Due to significant differences among DH systems regarding fuel use and Heat production units, results for one individual DH systems are not generally valid for other DH systems. Therefore, there is a need to generally describe entire DH sectors in a way that considers the Heat production plant merit-orders of the individual DH systems. Here, four models of typical DH systems are defined to represent the Swedish DH sector. A scenario for stepwise Heat Demand reductions due to building energy efficiency improvements is studied. The results show that Heat Demand reductions in Swedish DH systems generally reduce global CO 2 emissions and mainly reduce the use of biomass and fossil fuels, while the use of waste and industrial waste Heat (IWH) is less influenced. The results further show that in order to maximise the reduction of CO 2 emissions by energy conservation in buildings, the Heat production technologies of the DH system should be considered. A large share of CHP production with a high electricity-to-Heat output ratio decreases the possibilities to reduce global CO 2 emissions through Heat Demand reductions.
-
Swedish District Heating Sensitivity : Future Heat Demand Change and Electricity Market Dynamics
2013Co-Authors: Magnus ÅbergAbstract:Swedish District Heating Sensitivity : Future Heat Demand Change and Electricity Market Dynamics
-
sensitivity of district Heating system operation to Heat Demand reductions and electricity price variations a swedish example
Energy, 2012Co-Authors: Magnus Åberg, Joakim Widen, Dag HenningAbstract:In the future, district Heating companies in Sweden must adapt to energy efficiency measures in buildings and variable fuel and electricity prices. Swedish district Heating Demands are expected to decrease by 1–2% per year and electricity price variations seem to be more unpredictable in the future. A cost-optimisation model of a Swedish local district Heating system is constructed using the optimisation modelling tool MODEST. A scenario for Heat Demand changes due to increased energy efficiency in buildings, combined with the addition of new buildings, is studied along with a sensitivity analysis for electricity price variations. Despite fears that Heat Demand reductions will decrease co-generation of clean electricity and cause increased global emissions, the results show that anticipated Heat Demand changes do not increase the studied system's primary energy use or global CO2 emissions. The results further indicate that the Heat production plants and the fuels used within the system have crucial importance for the environmental impact of district Heat use. Results also show that low seasonal variations in electricity price levels with relatively low winter prices promote the use of electric Heat pumps. High winter prices on the other hand promote co-generation of Heat and electricity in CHP plants.
-
District Heating Sensitivity to Heat Demand Reductions and Electricity Market Dynamics
2012Co-Authors: Magnus ÅbergAbstract:In the future, district Heating companies in Sweden must adapt to energy efficiency measures in buildings and variable fuel and electricity prices. Swedish district Heating Demands are expected to decrease by 1-2% per year and electricity price variations seem to be more unpredictable in the future. A cost-optimisation model of a Swedish local district Heating system is constructed using the optimisation modelling tool MODEST. A scenario for Heat Demand changes due to increased energy efficiency in buildings, combined with the addition of new buildings, is studied along with a sensitivity analysis for electricity price variations. Despite fears that Heat Demand reductions will decrease co-generation of clean electricity and cause increased global emissions, the results show that anticipated Heat Demand changes do not increase the studied system's primary energy use or global CO2 emissions. The results further indicate that the Heat production plants and the fuels used within the system have crucial importance for the environmental impact of district Heat use. Results also show that low seasonal variations in electricity price levels with relatively low winter prices promote the use of electric Heat pumps. High winter prices on the other hand promote co-generation of Heat and electricity in CHP plants.
-
Optimisation of a Swedish district Heating system with reduced Heat Demand due to energy efficiency measures in residential buildings
Energy Policy, 2011Co-Authors: Magnus Åberg, Dag HenningAbstract:The development towards more energy efficient buildings, as well as the expansion of district Heating (DH) networks, is generally considered to reduce environmental impact. But the combined effect of these two progressions is more controversial. A reduced Heat Demand (HD) due to higher energy efficiency in buildings might hamper co-production of electricity and DH. In Sweden, co-produced electricity is normally considered to displace electricity from less efficient European condensing power plants. In this study, a potential HD reduction due to energy efficiency measures in the existing building stock in the Swedish city Linkoping is calculated. The impact of HD reduction on Heat and electricity production in the Linkoping DH system is investigated by using the energy system optimisation model MODEST. Energy efficiency measures in buildings reduce seasonal HD variations. Model results show that HD reductions primarily decrease Heat-only production. The electricity-to-Heat output ratio for the system is increased for HD reductions up to 30%. Local and global CO2 emissions are reduced. If co-produced electricity replaces electricity from coal-fired condensing power plants, a 20% HD reduction is optimal for decreasing global CO2 emissions in the analysed DH system.
Aaron Praktiknjo - One of the best experts on this subject based on the ideXlab platform.
-
Time series of Heat Demand and Heat pump efficiency for energy system modeling
Scientific Data, 2019Co-Authors: Oliver Ruhnau, Lion Hirth, Aaron PraktiknjoAbstract:With electric Heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the Heat Demand and the coefficient of performance (COP) of Heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water Heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different Heat sources – air, ground, and groundwater – and different Heat sinks – floor Heating, radiators, and water Heating – are calculated based on COP and Heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform. Measurement(s) time sampled measurement data set • Heating Technology Type(s) computational modeling technique • digital curation Factor Type(s) year Sample Characteristic - Environment anthropogenic environment Sample Characteristic - Location Austria • Belgium • Bulgaria • Czech Republic • Germany • French Republic • Great Britain • Croatia • Hungary • Republic of Ireland • Kingdom of the Netherlands • Poland • Romania • Slovenia • Slovak Republic Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9878849
-
Time series of Heat Demand and Heat pump efficiency for energy system modeling.
Scientific data, 2019Co-Authors: Oliver Ruhnau, Lion Hirth, Aaron PraktiknjoAbstract:With electric Heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the Heat Demand and the coefficient of performance (COP) of Heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water Heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different Heat sources – air, ground, and groundwater – and different Heat sinks – floor Heating, radiators, and water Heating – are calculated based on COP and Heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9878849
Oliver Ruhnau - One of the best experts on this subject based on the ideXlab platform.
-
Time series of Heat Demand and Heat pump efficiency for energy system modeling
Scientific Data, 2019Co-Authors: Oliver Ruhnau, Lion Hirth, Aaron PraktiknjoAbstract:With electric Heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the Heat Demand and the coefficient of performance (COP) of Heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water Heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different Heat sources – air, ground, and groundwater – and different Heat sinks – floor Heating, radiators, and water Heating – are calculated based on COP and Heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform. Measurement(s) time sampled measurement data set • Heating Technology Type(s) computational modeling technique • digital curation Factor Type(s) year Sample Characteristic - Environment anthropogenic environment Sample Characteristic - Location Austria • Belgium • Bulgaria • Czech Republic • Germany • French Republic • Great Britain • Croatia • Hungary • Republic of Ireland • Kingdom of the Netherlands • Poland • Romania • Slovenia • Slovak Republic Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9878849
-
Time series of Heat Demand and Heat pump efficiency for energy system modeling.
Scientific data, 2019Co-Authors: Oliver Ruhnau, Lion Hirth, Aaron PraktiknjoAbstract:With electric Heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the Heat Demand and the coefficient of performance (COP) of Heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water Heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different Heat sources – air, ground, and groundwater – and different Heat sinks – floor Heating, radiators, and water Heating – are calculated based on COP and Heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9878849
Krzysztof Grygierek - One of the best experts on this subject based on the ideXlab platform.
-
Analysis of Accuracy Determination of the Seasonal Heat Demand in Buildings Based on Short Measurement Periods
Energies, 2018Co-Authors: Joanna Ferdyn-grygierek, Dorota Bartosz, Aleksandra Specjał, Krzysztof GrygierekAbstract:In this paper, we present a multi-variant analysis of the determination of the accuracy of the seasonal Heat Demand in buildings. The research was based on the linear regression method for data obtained during short periods of measurement. The analyses were carried out using computer simulation, and the numerical models of the multifamily building and school building were used for the simulation. The simulations were performed using the TRNSYS, ESP-r, and CONTAM programs. The multi-zone models of the buildings were validated based on the measurement data. The impact of the building’s parameters (airtightness, insulation, and occupancy schedule) on the determination of the accuracy of the seasonal Heat Demand was analyzed. The analyses allowed guidelines to be developed for determining the seasonal energy consumption for Heating and ventilation based on short periods of Heat Demand measurements and to determine the optimal duration of the measurement period.
Stefan W Schneider - One of the best experts on this subject based on the ideXlab platform.
-
a Heat Demand load curve model of the swiss national territory
IOP Conference Series: Earth and Environmental Science, 2019Co-Authors: Stefan W Schneider, Pierre Hollmuller, J E Chambers, Martin Kumar PatelAbstract:This paper presents a bottom up model simulating the hourly Heat Demand load curve for space Heating and domestic hot water production for Swiss buildings listed in the national building and dwelling register. The model was calibrated on the actual Heat Demand load curves of several building types and predicts the Demand as function of external temperature and solar irradiation. In addition, it includes stochastic deviations to accurately reproduce the aggregated load of large building groups. Using a climatic database covering the whole Swiss territory, the model takes account of the diverse weather conditions and climate types. The aggregated simulated load curve is compared with the measurements from a large district Heating network, demonstrating that key indicators such as peak load and ranked loads are very well reproduced. To disseminate the results, a GIS database was setup that estimates the aggregated Heat Demand load curve for any portion of the Swiss national territory. The proposed approach addresses the challenge of large territorial scale simulation using only limited information available on its building stock. The model can be easily adapted to generate load curves for other EU regions provided the required information is available for the building stock.
-
Central Europe Towards Sustainable Building (CESB19) - A Heat Demand Load Curve Model of the Swiss National Territory
IOP Conference Series: Earth and Environmental Science, 2019Co-Authors: Stefan W Schneider, Pierre Hollmuller, Jonathan Chambers, Martin Kumar PatelAbstract:This paper presents a bottom up model simulating the hourly Heat Demand load curve for space Heating and domestic hot water production for Swiss buildings listed in the national building and dwelling register. The model was calibrated on the actual Heat Demand load curves of several building types and predicts the Demand as function of external temperature and solar irradiation. In addition, it includes stochastic deviations to accurately reproduce the aggregated load of large building groups. Using a climatic database covering the whole Swiss territory, the model takes account of the diverse weather conditions and climate types. The aggregated simulated load curve is compared with the measurements from a large district Heating network, demonstrating that key indicators such as peak load and ranked loads are very well reproduced. To disseminate the results, a GIS database was setup that estimates the aggregated Heat Demand load curve for any portion of the Swiss national territory. The proposed approach addresses the challenge of large territorial scale simulation using only limited information available on its building stock. The model can be easily adapted to generate load curves for other EU regions provided the required information is available for the building stock.
-
geo dependent Heat Demand model of the swiss building stock
2016Co-Authors: Stefan W Schneider, Jad Khoury, Bernard Marie Lachal, Pierre HollmullerAbstract:One of the strategies for decarbonizing the energy mix is to increase the use of district Heat networks, for distribution of waste Heat or Heat produced by renewable energy sources. However, planning such networks needs a geo-dependent energy database concerning Demand and supply, incorporating their dependency on space and time. The present paper concerns the development of a bottom up statistical extrapolation model for estimating the Heat Demand of the Swiss building stock. The database is constructed on top of the Swiss building register, which contains basic data on building category, age, ground area and number of floors, as well as area devoted to dwellings. The model itself concerns: (i) estimation of the Heated area of each building; (ii) estimation of the Heat Demand of each building, with disaggregation in terms of space Heating (SH) and domestic hot water (DHW). The model is calibrated by way of recorded data concerning actual yearly energy consumption of around 27'000 buildings, with a classification by building category and age. The disaggregation in terms of SH and DHW allows for climatic correction of the observed data within a common climate basis. In a second step this calibration data is used for extrapolation of the Demand on the entire Swiss building stock, for which the SH component is corrected by taking into account local climate characteristics. Finally, statistical methods tend to quantify the uncertainty inherent to the use of average Demand values by age and category, in relation to the level of spatial aggregation.
-
World Fustainable Built Environment Conference 2017, Conference Proceedings (WSBE17) - Geo-dependent Heat Demand model of the Swiss building stock
2016Co-Authors: Stefan W Schneider, Jad Khoury, Bernard Marie Lachal, Pierre HollmullerAbstract:One of the strategies for decarbonizing the energy mix is to increase the use of district Heat networks, for distribution of waste Heat or Heat produced by renewable energy sources. However, planning such networks needs a geo-dependent energy database concerning Demand and supply, incorporating their dependency on space and time. The present paper concerns the development of a bottom up statistical extrapolation model for estimating the Heat Demand of the Swiss building stock. The database is constructed on top of the Swiss building register, which contains basic data on building category, age, ground area and number of floors, as well as area devoted to dwellings. The model itself concerns: (i) estimation of the Heated area of each building; (ii) estimation of the Heat Demand of each building, with disaggregation in terms of space Heating (SH) and domestic hot water (DHW). The model is calibrated by way of recorded data concerning actual yearly energy consumption of around 27'000 buildings, with a classification by building category and age. The disaggregation in terms of SH and DHW allows for climatic correction of the observed data within a common climate basis. In a second step this calibration data is used for extrapolation of the Demand on the entire Swiss building stock, for which the SH component is corrected by taking into account local climate characteristics. Finally, statistical methods tend to quantify the uncertainty inherent to the use of average Demand values by age and category, in relation to the level of spatial aggregation.
