certification:passive_house_categories:per
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certification:passive_house_categories:per [2015/03/11 17:43] – bwuensch | certification:passive_house_categories:per [2024/04/18 19:11] (current) – jgrovesmith | ||
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- | ====== | + | ====== PER-factors for electricity use: Location & application specific decarbonisation |
- | Which parameter can be used as a suitable indicator for the sustainability of buildings from an energy standpoint? | + | // |
+ | This article is an based on the paper "The PER sustainability assessment" | ||
- | ==== PER factors: The methodology | + | ===== Introduction ===== |
- | The methodology used to derive | + | The so-called |
- | {{:certification:passive_house_categories:per_formel_en.jpg? | + | This article describes the general methodology used to derive the PER-factors for electricity. |
+ | |||
+ | ===== PER factors for electricity: The methodology ===== | ||
+ | |||
+ | The methodology used to derive PER factors is based on the ideas that have previously been published in [Feist 2013] and [Feist 2014] (see [[basics:passive_house_-_assuring_a_sustainable_energy_supply:passive_house_the_next_decade|Passive House – the next decade]]). The approach described in these original publications were then further developed, applied and analysed internationally. With an hourly resolution load profiles of the energy demand are simulated in the context of a future scenario - where the energy is supplied solely from renewable energy sources, including all necessary storage facilities (Figure 1). The individual calculations are based on climate data from various sources, the resulting PER factors describe how much more renewable energy must be supplied in order to cover the final energy consumed at the building, including all losses incurred along the way. For example, a PER-factor of 1.5 indicates that a surplus of 50% renewable primary energy has to be generated in order to be able to meet the final energy demand at the building. | ||
+ | |||
+ | PER-factor | ||
The PER factor is determined by the simultaneity of available energy resources and the energy demand, as this dictates how much energy needs to be temporarily stored before it is used. Short-term storage can technically be achieved fairly efficiently, | The PER factor is determined by the simultaneity of available energy resources and the energy demand, as this dictates how much energy needs to be temporarily stored before it is used. Short-term storage can technically be achieved fairly efficiently, | ||
- | [{{:certification: | + | [{{:picopen: |
- | ===== Load profiles – Supply and Consumption | + | ==== Load profiles – Supply and Consumption ==== |
- | ==== Renewable electricity supply | + | === Renewable electricity supply === |
The modelled future energy supply network is based purely on electricity from renewable sources. Three different sources are taken into account: Photovoltaics, | The modelled future energy supply network is based purely on electricity from renewable sources. Three different sources are taken into account: Photovoltaics, | ||
- | The hourly electricity production through solar power is calculated based on a photovoltaic system oriented toward the equator. The power output is calculated based on the solar radiation information from the respective climate data set, taking into account a tempe-rature-sensitive efficiency of the photovoltaic module. The model’s inclination is determined such that the highest possible annual energy yield is reached at the considered location. | + | The hourly electricity production through solar power is calculated based on a photovoltaic system oriented toward the equator. The power output is calculated based on the solar radiation information from the respective climate data set, taking into account a temperature-sensitive efficiency of the photovoltaic module. The model’s inclination is determined such that the highest possible annual energy yield is reached at the considered location. |
The hourly electricity produced through wind energy is calculated based on a smoothed profile of hourly wind velocities. The same climate data set is used, in order to take account of local climatic correlations between wind and radiation or temperature. However, such localised wind data is rarely representative of the potential of wind energy in the surrounding region. The original wind velocity data is therefore calibrated based on long-term measured averages of the region [SSE] and extrapolated to a hub height of 150 m. The actual power output then depends on the turbine’s power curve, which is modelled with a specific power output of 380 W/m² and 200 W/m² for regions with strong and weak wind, respectively (based on [Mono et.al. 2014]). If the average wind speed at hub height is lower than 4 m/s, a significant development of wind energy in the area is unlikely for economic reasons. The contribution of wind energy to the total mix in the calculations is therefore limited to 0.5 % of the annual yield. Offshore wind energy is not considered at this stage – the calculations are slightly on the safe side in this regard. | The hourly electricity produced through wind energy is calculated based on a smoothed profile of hourly wind velocities. The same climate data set is used, in order to take account of local climatic correlations between wind and radiation or temperature. However, such localised wind data is rarely representative of the potential of wind energy in the surrounding region. The original wind velocity data is therefore calibrated based on long-term measured averages of the region [SSE] and extrapolated to a hub height of 150 m. The actual power output then depends on the turbine’s power curve, which is modelled with a specific power output of 380 W/m² and 200 W/m² for regions with strong and weak wind, respectively (based on [Mono et.al. 2014]). If the average wind speed at hub height is lower than 4 m/s, a significant development of wind energy in the area is unlikely for economic reasons. The contribution of wind energy to the total mix in the calculations is therefore limited to 0.5 % of the annual yield. Offshore wind energy is not considered at this stage – the calculations are slightly on the safe side in this regard. | ||
- | Last but not least, the electricity produced from hydropower is taken into account based on the predicted contribution of this energy source to each country’s future total energy demand. The prediction for future hydropower generation | + | Last but not least, the electricity produced from hydropower is taken into account based on the predicted contribution of this energy source to each country’s future total energy demand. The prediction for future hydropower generation |
- | ==== Electricity demand at the building | + | |
+ | === Electricity demand at the building === | ||
At the receiving end we differentiate between five different load profiles for different types of energy consumption: | At the receiving end we differentiate between five different load profiles for different types of energy consumption: | ||
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- | ===== Site-specific PER factors | + | ==== Site-specific PER factors ==== |
In order to determine the PER factor, the respective load profiles of electricity production and electricity consumption are each scaled to 100 kWh/a and compared on an hourly basis. Direct electricity consumption is assumed to the extent of supply and demand matching up. If the supply exceeds the demand, any excess electricity is fed into the storage facilities. Accordingly, | In order to determine the PER factor, the respective load profiles of electricity production and electricity consumption are each scaled to 100 kWh/a and compared on an hourly basis. Direct electricity consumption is assumed to the extent of supply and demand matching up. If the supply exceeds the demand, any excess electricity is fed into the storage facilities. Accordingly, | ||
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It is further assumed that the seasonal storage has the required capacity to store exactly the amount of energy storage required over the course of the year (supply = demand + losses). One possibility of a working seasonal storage system is the conversion of RE electricity into methane, for which a conversion efficiency of 57 % is assumed. The re-conversion from gas into electricity in a CCTG plant is modelled with an efficiency of 50 %. Electricity consumed via the seasonal storage, therefore has an overall efficiency of only approx. 30 %. Finally, 5 % distribution losses are added for all electricity transmission via the electrical grid. | It is further assumed that the seasonal storage has the required capacity to store exactly the amount of energy storage required over the course of the year (supply = demand + losses). One possibility of a working seasonal storage system is the conversion of RE electricity into methane, for which a conversion efficiency of 57 % is assumed. The re-conversion from gas into electricity in a CCTG plant is modelled with an efficiency of 50 %. Electricity consumed via the seasonal storage, therefore has an overall efficiency of only approx. 30 %. Finally, 5 % distribution losses are added for all electricity transmission via the electrical grid. | ||
- | [{{:certification: | + | [{{:picopen: |
---- | ---- | ||
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For each of the load profiles the PER routine calculates the required RE supply to cover the total energy demand, plus all storage losses. The PER factor for the energy application in question then equates to the slope of the changing RE supply over the increase in energy demand (examples are shown in Figure 4). In some cases this can lead to factors below one, which would mean that less energy needs to be additionally generated than will be consumed. This is the case only if the additional energy demand balances out seasonal disparities and thus reduces the need for seasonal storage, e.g. additional cooling in a heating dominated climate. | For each of the load profiles the PER routine calculates the required RE supply to cover the total energy demand, plus all storage losses. The PER factor for the energy application in question then equates to the slope of the changing RE supply over the increase in energy demand (examples are shown in Figure 4). In some cases this can lead to factors below one, which would mean that less energy needs to be additionally generated than will be consumed. This is the case only if the additional energy demand balances out seasonal disparities and thus reduces the need for seasonal storage, e.g. additional cooling in a heating dominated climate. | ||
- | [{{:certification: | + | [{{:picopen: |
- | [{{:certification: | + | [{{:picopen: |
---- | ---- | ||
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Figure 5 shows example PER factors for selected climates under very different climatic conditions (all with comparatively small hydropower contribution). The results for household electricity and domestic hot water don’t vary much, with typical values around 1.3 – i.e. 30 % more RE electricity needs to be supplied than can actually be used at the building. The factors for heating, cooling and dehumidification are more strongly influenced by the given local climatic conditions. Unfortunately, | Figure 5 shows example PER factors for selected climates under very different climatic conditions (all with comparatively small hydropower contribution). The results for household electricity and domestic hot water don’t vary much, with typical values around 1.3 – i.e. 30 % more RE electricity needs to be supplied than can actually be used at the building. The factors for heating, cooling and dehumidification are more strongly influenced by the given local climatic conditions. Unfortunately, | ||
- | ===== Regional PER factors worldwide | + | ==== Regional PER factors worldwide ==== |
The methodology to derive PER factors, as described in this article, to begin with are only valid for the specific climate data set used. Calculations for the very same location but a different climate data set (e.g. different time period, different source) will lead to slightly different results. Furthermore, | The methodology to derive PER factors, as described in this article, to begin with are only valid for the specific climate data set used. Calculations for the very same location but a different climate data set (e.g. different time period, different source) will lead to slightly different results. Furthermore, | ||
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The PER factors to be used in the PHPP are thus not based on individual local calculations but rather on a combination via a global Fourier approximation of the results calculated for over 700 locations worldwide. In addition, the minimum value used in the PHPP is 1 (supply = demand). Figure 6 shows the average value and variation of the PER factor for space heating of all locations currently integrated into the PHPP. | The PER factors to be used in the PHPP are thus not based on individual local calculations but rather on a combination via a global Fourier approximation of the results calculated for over 700 locations worldwide. In addition, the minimum value used in the PHPP is 1 (supply = demand). Figure 6 shows the average value and variation of the PER factor for space heating of all locations currently integrated into the PHPP. | ||
- | [{{:certification: | + | [{{:picopen: |
===== Summary and Outlook ===== | ===== Summary and Outlook ===== | ||
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- | ==== Literature ==== | + | ===== Literature |
[Feist 2013] Feist, | [Feist 2013] Feist, | ||
[Feist 2014] Feist, | [Feist 2014] Feist, | ||
+ | |||
+ | [Grove-Smith/ | ||
[intpow 2009] World Hydro Potential and Development. intpow, Norwegian Renewable Energy Parnters 2009 | [intpow 2009] World Hydro Potential and Development. intpow, Norwegian Renewable Energy Parnters 2009 |
certification/passive_house_categories/per.1426092211.txt.gz · Last modified: 2015/03/11 17:43 by bwuensch