the circulation pump for heating (4), the central room thermostat (6) with .... As a result, there is a considerable difference in comfort level. ..... The 6 kW limit is.
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The impact of the heat emission system on the grid-interaction of building integrated photovoltaics in low-energy dwellings R. Baetens1*, R. De Coninck2,3, L. Helsen2 & D. Saelens1 (1) (2)
Division of Building Physics, Department of Civil Engineering, K.U.Leuven, BE-3000 Leuven, Belgium
Division of Applied Mechanics and Energy Conversion, Department of Mechanical Engineering, K.U.Leuven, BE-3000 Leuven, Belgium (3)
1.
3E, Brussels, Belgium
ABSTRACT
Considering the recasting of the EPB Directive forseen for 2010, developing net zero energy buildings will become an important goal for the building industry. Building-integrated photovoltaics (BIPV) that produce the same amount of energy as consumed in the building on a yearly base are one of the options to achieve this goal. This paper focuses on the simultaneity of production and consumption for a dwelling equipped with different heat emission systems. A multi-zone TRNSYS model for a dwelling with compression heat pump for both space heating and domestic hot water (DHW), domestic consumers and on-site photovoltaic generation is set-up. The model is used to quantify the impact of the type of heat emission system on the auto-consumption and grid-interaction of BIPV systems in a Belgian climate. Low-temperature radiators, low-temperature floor heating and combination of both systems as heat emission system are modeled and evaluated. Herefore, the peak load and the cover factor, i.e. the ratio of domestic demand that is instantaneously covered by the BIPV, are evaluated. If one aims to drastically decrease the impact of domestic electricity demands on the main distribution grid, the installation of a BIPV might not be sufficient due to the mismatch of the electricity demand profile and the production profile by the BIPV system. An integrated approach is necessary. It is shown that the choice of heat emission system and its resulting operating times - in combination with the size of storage tank - directly influences the gridinteraction of a dwelling. The heat emission characteristics affect both the tank and the condenser temperatures, and as such the power demand and operating time for the heat pump. However, no direct conclusions are drawn on the choice of heat emission system as the control strategy will play an important role. Keywords: Low-temperature heating, photovoltaic, BIPV, cover factor, smart grid 2.
INTRODUCTION
Due to a favourable subsidy system in the Flemish region, the market of photovoltaic (PV) installations grew exponentially. By the end of year 2009, the total installed capacity grew to a cumulated capacity of 312 MWp in Belgium (Neyens 2009, VREG 2009). From the currently total installed capacity, nearly eighteen thousand installations with a size between 2 and 4 kWp are positioned on roofs of private houses in Flanders, practically all privately financed by the owners of the dwellings and the government subsidies (Neyens 2009), and all grid-connected (EurObserv'ER 2009). 8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
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For a business-as-usual model where no further attempt is made towards optimizing the energetic advantage of the BIPV installation - as in most of the current dwellings where the PV installation is installed on existing buildings - the reduction of the grid electricity demand of the dwelling is expected to remain rather low as the PV power output peaks around 12h whereas an average domestic load profile show two peaks: one around 8h and another after 18h. For quantifying the reduction of the grid electricity demand by a BIPV installation in a business-as-usual model and illustrating the auto-consumption and grid injection of such a PV installation, a multi-zone TRNSYS model for a dwelling with an electricity-driven, air-towater compression heat pump for both space heating and domestic hot water (DHW) production, domestic consumers and on-site photovoltaic generation is set-up (see figure 1 for a schematic presentation of the system modelled and figure 2 for an example model output of the electricity demand). In order to simulate correctly the dynamics of the system, a small time-step of 1 minute is chosen. The model is used to visualize grid-interactions, both the influence of user behaviour and the influence of the heating system on the grid-injection of BIPV, and to visualize bottle-necks for possible large-scale implementation of BIPV generation. 3.
BOTTOM-UP POWER BALANCE
Within the considered residential environment, 4 major groups of domestic electricity consumption can be distinguished of which 3 are considered in the model: 1. 2. 3. 4.
Heat production for space heating and DHW by means of an electricity-driven, air-towater compression heat pump, Standby power and domestic cooling appliances, Non-shiftable occupancy and behaviour based power demand, i.e. lighting, ventilation, cooking and the use of media, Shiftable power demand, i.e. washing machines, tumble dryers and dishwashers. This last group is not modelled due to a lack of information on their usage and consumption profiles.
The photovoltaic power output PPV (W) is modelled with the available TRNSYS Type 194 implementing the 5-parameter model developed by the research group of Beckman (Duffie & Beckman 1991). The position of the PV panels has been chosen to maximize the total output over an entire year for the climate data of Uccle (Belgium), i.e. an inclination of 34° and oriented directly to the South (Huld & Suri 2010). For retrieving a high-resolution power output of the photovoltaic system, minute values for the sky diffuse and beam radiation on the tilted surface of the PV cells and the solar zenith angle are generated from Meteonorm 6.1 (Meteotest 2008) for Uccle, Belgium.
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Figure 1. Schematic representation of the simulated dwelling including the domestic load profile. Within the dwelling, a building integrated photovoltaic system (1), an electricitydriven air-to-water heat pump (2) for space heating and DHW production, a storage tank (3), the circulation pump for heating (4), the central room thermostat (6) with outdoor sensor (5), mechanical ventilation (7), thermostatic valves (8), low-temperature radiators (9) and a floor heating system (10) are modelled and simulated.
3.1 Building and installation model A low-energy detached house is modelled in detail for simulating the profile of electricity demand. The dwelling is modelled as a multi-parameter multi-zone building in TRNSYS (Solar Energy Laboratory 2009) where each single room is a zone. It has a usable area of 210 m², a roof area of 270 m² and U-values of 0.17, 0.20 and 1.17 W/(m²K) respectively for the façade, roof and window panes. The dwelling has a balanced mechanical ventilation system with a total extract air flow rate of 175 m³/h. The ventilation system has a heat recovery unit with an efficiency of 0.70, both the supply and exhaust fan have a power of 95 W. The air tightness n50 is 4 h-1. Furthermore, internal gains of 8 W/m² are taken into account with a simplified day & night profile for the dayzone and nightzones. Because of the limited heat demand and the resulting possibilities for low-temperature heat emission systems in low energy dwellings, a heat pump is often used for space heating. Within this work, it is assumed that the same heat pump is used for the production of DHW. The resulting total electricity demand for the dwelling is shown in figure 4. The modelled low energy dwelling is equipped with three different low-temperature heat emission systems: 1.
Hydronic underfloor heating (FH) sized according to EN 12831:2003 and prEN 153773:2005 for a temperature difference ΔT of 8°C. The floor heating system is controlled by a room thermostat and a floor sensor present in every room. The heating curve for the supply temperature varies linearly between 43°C and 20°C at an outdoor temperature of 8°C and 20°C respectively.
2.
Classic hydronic low-temperature (LT) radiators (R) sized according to EN 12831:2003 and EN 1442:2003 for a temperature drop of 10°C. The radiators in the living room are controlled by a central room thermostat, the others are controlled by thermostatic valves. The heating curve for the supply temperature varies linearly between 55°C and 20°C at an outdoor temperature of -8°C and 20°C respectively.
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3.
A combination (C) of both floor heating and LT radiators (Hasan et al. 2009) as shown in figure 1. Each room is equipped with a room thermostat and a floor sensor is placed in the middle of the floor heating circuit. During absence, the room thermostat will control the floor heating to keep the temperature of each room above or equal to 16°C by using the floor temperature at the end of a heating period as set-point temperature. When comfort is required, the room thermostat will control both the floor heating and the radiators to achieve 21°C. The floor heating is sized according to EN 12831:2003 and prEN 15377-3:2005 to provide a basic heating at 16°C, whereas the radiators are sized according to EN 12831:2003 and EN 1442:2003 to raise the room temperature from 16°C to the comfort level of 21°C. The heating curve for the supply temperature of the FH system varies linearly between 43°C and 20°C, whereas the heating curve for the supply temperature for the radiators varies linearly between 55°C and 20°C at an outdoor temperature of -8°C and 20°C respectively. The set-point temperature for the FH system is achieved with three-way valves, mixing the retour water of the FH system and supply water at the set-point temperature of the LT radiators (which is used as set-point for the heat production).
For all three heat emission systems, the heat production is provided by an electricity-driven, air-to-water compression heat pump. The installation consists of a storage tank for heating with an integrated DHW heat exchanger coupled to the heat pump with a nominal power of 10.0 kW and a COP of 4.26 at nominal conditions (i.e. at 2/35°C). DHW profiles are generated based on (i) the occupancy profiles and (ii) the daily probability distributions in (Spur et al. 2006, Jordan & Vajen 2001) for both small and medium loads, bathing and showering. Two set-point temperatures are included in the design of the installation. A first set-point temperature is set to 50°C at the top of the storage tank, in order to always allow take-off of DHW at high temperatures. A second set-point temperature is set at the middle of the storage tank, equalling the set-point temperature for the supply temperature of the heat emission system and depending on the outdoor temperature. The heat-pump will be switched on if one of both set-point temperatures is not achieved. Different sizes for the storage tank, ranging from 0.5 up to 4.0 m³, are considered in the simulations. Within a conventional approach for installation design in dwellings, such large tank volumes are rarely used. However, the possibility of large thermal buffers in dwellings may play an important role from a grid-point of view and in the development of strategies towards smart grids. The large buffers may allow a time-shift in power demand in order to use and store excessive electric PV power as thermal energy. For the control of the heating system and the required comfort, no day or week profile is used. The comfort demand is directly coupled to the stochastic generated occupancy profile (see section 3.2) denoting that the occupants will switch on manually when arriving home or waking up and switch off again when leaving home or going to sleep. The manual control is considered as benchmark and allows to calculate improvements achieved by adapted control algorithms towards later optimisation criteria. In the model, an operative temperature of 21°C is used as set-point temperature for the central room-thermostat during occupation periods at which the space heating system is allowed to be switched of, while the room temperature is not allowed to drop below 16°C throughout the whole year.
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3.2 Domestic load profile In order to analyse the impact of the heating system on the grid-interaction of the dwelling equipped with a BIPV installation, it is necessary to model the domestic load profile of the dwelling excluding the demand for space heating and DHW production. A more extensive description of the demand profile can be found in Baetens et al. (2010).
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Within the developed building model, a stochastically generated occupancy profile from Richardson et al. (2008) and stochastically generated domestic load profile are included. The modeled domestic electricity consumption profile takes into account standby power (de Almeida et al. 2008) and domestic cooling appliances (Firth et al. 2008, Liu et al. 2004), lighting (Stokes et al. 2004, Richardson et al. 2009), balanced mechanical ventilation, cooking (Glorieux & Vandeweyer 2002, Wood & Newborough 2003) and the use of media television and computer (Glorieux & Vandeweyer 2002, TPDCB 2010). For the purpose of detailed electricity load profile prediction and simulation, the original tool for lighting has been coupled to minute global irradiance data derived from Meteonorm 6.1 (Meteotest 2008) for Uccle, Belgium. The use of washing machine and tumble dryer are not modelled as their current use is determined by the day and night regime of electricity tariff and due to a lack of information on their usage and consumption profiles.
time, h
Figure 2. Example week profile for a typical mid-season week of the modelled electricity grid load for a dwelling equipped with LT radiators and the use of a heat pump for heat generation. Positive values denote grid demand, negative values grid supply.
3.3 Limits of the model The average yearly electricity consumption for the modelled situation is 2.4 MWh/a excluding heat production. For Belgium, the average domestic electricity consumption for a similar dwelling is 3.5 MWh/a. The difference may be explained as follows: (i) Shiftable load profiles such as washing machine and tumble dryer are not modelled. Based on the number of cycles and the average electricity consumption per cycle (Danish Energy Agency 1995), these appliances count for an average annual electricity consumption of 0.7 MWh/a. The remaining difference might be explained by (ii) small electrical appliances which might show a higher power demand but are used for a very short period and (iii) the influence of electrically generated DHW in the statistics.
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4.
RESULTS AND DISCUSSION
4.1 Comfort and energy demand As different heat emission systems and different storage tank sizes are compared towards their impact on the grid-interaction of a dwelling with a BIPV installation, it is important to evaluate the achieved comfort in the dwelling for the configured heating systems and the total energy demand and performance factor of the systems. The resulting indoor operative temperature distribution curves of the achieved comfort levels during presence for the different heat emission systems and storage tank sizes are shown in figure 3. It is shown that the achieved comfort levels are different due to the characteristics of the heating system even though the same operative set-point temperature of 21°C is used. It may be noticed that short-term comfort problems occur for the combination C of floor heating and LT radiators and a small storage tank, due to the limited convective re-heat capacity of the radiators. The control strategy and switch-on moments for the different emission systems are the same. As a result, there is a considerable difference in comfort level. The average comfort is much higher for the combined system with a strong peak at 21°C, whereas floor heating FH peaks at 19.5°C and radiators R at 20°C. Finally, higher comfort may be found for larger storage tank sizes as more short-term heating power is available. The strong dip in figure 3 after 21°C is caused by the set-point temperature for heating of 21°C. Heating is switched off at this operative temperature. When over-heating occurs due to the thermal inertia of the system, it occurs directly at higher temperatures. 0.14 0.10
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Figure 3. Indoor operative temperature distribution curves at occupancy for the different heat emission systems with a small (i.e. 0.5 m³) [left] and large (i.e. 4.0 m³) storage tank [right]. The strong dip after 21°C is caused by the set-point temperature. The total electricity consumption and the resulting seasonal performance factors SPF for space heating and DHW production are shown in figure 4 for the different heat emission systems and storage tank sizes. Here, the SPF is defined as the ratio of the total produced heat to the total electricity consumption of the heat pump (according to the European Standard EN 15316). The total electricity consumption for heating (i.e. both space heating and DHW production) varies between 40 and 75 kWh/m². The SPF decreases with an increasing storage tank volume and lies around 2.8 for heating with radiators, between 3.2 and 3.0 for heating with floor heating and between 3.5 and 3.0 for heating with a combination of both floor heating and radiators. 8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
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The combination of floor heating and radiators has shown to have a higher SPF compared to floor heating or radiators. An explanation may be found in the occurrence of lower condenser temperatures for the combination as shown in figure 5 due to a better stratification in the storage tank. The longer necessary operating time for the FH system to reach comfort results in a higher storage temperature as the tank will always be filled with water at return temperature of the FH system. As the condenser temperature increases, the heat pump will operate at a lower COP and consequently delivers a lower thermal power. The resulting lower thermal power causes the heat pump to fail reaching the required set-point for DHW of 50°C at the top of the storage tank, as water at lower temperature is added at the top of the tank. 80
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Figure 4. Total electricity demand (kWh/m²) for space heating and DHW production [left] and the resulting seasonal performance factor SPF (-) [right] of heat production for the different heat emission systems as a function of the storage tank volume.
Figure 5. Distribution curve of the condenser temperature for the different heat emission systems.
4.2 Cover factor From a macroeconomic point of view, the effectiveness of the building integrated photovoltaic system for reducing the electricity demand from the main distribution grid may be expressed by quantifying the cover factor. In general, a cover factor γ (-) is a number indicating to what extent a set of threads is covered by another set of threads. Within this context, the cover factors γD (where D denotes demand) and γS (where S denotes supply, i.e. γPV in this case) provide efficiency-like information and are defined as ‘the ratio to which the power demand is covered by the BIPV supply’ and ‘the ratio to which the BIPV supply is covered by the power demand’ respectively t2
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In a business-as-usual model where no attempt is made towards power load matching and thus increasing γD and γPV, a maximum value γD,max for the cover factor γD on daily basis can be defined based on the length of daytime and the resulting maximum daily period during which power output of the PV array is possible as a function of the locations latitude and orientation of the PV array. γD,max expresses the ratio of the electricity demand during sunshine to the total electricity demand, independently of the PV installation size. (An example year profile op the cover factors can be found in figure 6.)
1.0 0.8 0.6 0.4 0.2 0.0
γDmax,FH γDmax,R γDmax,C
Figure 6. Comparison between the year profile for the cover factors γD, γD,max and γS (-) for a dwelling equipped with floor heating (FH), LT radiators (R) and a combination (C) of both floor heating and LT radiators. 4.2.1
Varying BIPV size
Obviously, the size of the BIPV installation has a major influence on the overall cover factor γD and γS of the installation as its size determines the maximum power that could be covered instantaneously by the BIPV system. The influence of the BIPV size on the cover factors γyear are shown in figure 7 for the different heat emission systems. The intersection between γD(PPV) and γS(PPV) (indicated with an arrow in figure 7) denotes a zero-energy building, i.e. within the definition of a building wherefore the total yearly electricity production equals the total electricity consumption. Each design on the right side of this intersection denotes a net electricity producing dwelling. The necessary PV size only depends on the total yearly electricity demand as shown in figure 4, whereas the resulting cover factor will also depend strongly on the demand profile. Within this study, the combination C of both floor heating and radiators shows the lowest electricity demand and on the same time also shows a higher cover factor of 0.24 at zero-energy compared to 0.21 for 8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
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the use of radiators. The cover factor for a zero-energy building with floor heating is shown to be 0.30, but a much larger PV size is necessary here to reach a zero-energy level. Even though yr∫PD (see Eq.1) is found a factor 1.3 to 1.5 higher for the use of floor heating compared to radiators or a combination of both, as shown in figure 4, the use of floor heating results in a higher γD(PPV) with a factor up to 1.1. As a result, the total demand covered by the BIPV installation lies 1.3 to 1.6 times higher compared to the other heat emission systems (see Eq.1), which is confirmed by the higher γS(PPV) with the same factor. This observation reveals that, however the floor heating system shows a higher total electricity consumption for space heating, the load profile of the heating system matches better with the load profile of the PV system resulting in a lower dependency of the grid for virtual storage of the produced power. Two reasons may be found for the higher cover factor for the FH system. Firstly, the same reason that causes the low SPF for the FH system, as explained in section 4.1, may result in a higher cover factor. The higher condenser temperatures result in lower heat pump thermal power and longer - and more constant - operating times for the heat pump. Secondly, both setpoint temperatures for the storage tank are kept through the complete year. As a result, lower temperatures are allowed in the middle of the storage tank (i.e. the set-point for the supply temperature) outside the heating season which results in higher demands during summer when DHW is taken from the storage tank as may be concluded from figure 6. As may be already concluded from the definition of γD,max, the BIPV size has no influence on γD,max as it may be considered as ‘the maximum achievable cover factor γD with an infinite PV system’. 1.0
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Figure 7. Cover factors γD, γD,max and γS (-) for a dwelling equipped with floor heating [left], LT radiators [mid] and a combination of both floor heating and LT radiators [right] as a function of the BIPV system size (kWp) when a heat pump is used for heat production and for a storage tank of 1.0 m³. The arrow indicates a zero-energy building where the BIPV produces as much energy as the yearly consumption. 4.2.2
Varying storage tank size
The influence of the storage tank size on the cover factors γyear for the different heat emission systems is shown in figure 8. The size of the storage tank may have an influence on the cover factors γD and γS of the installation as it allows storage of heat and as such shifting of the power demand for space 8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
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heating and DHW production. However, for the system with radiators R and the combination C of both floor heating and radiators little to no influence is noticed of the storage tank size on the cover factors. Nevertheless, a slowly decreasing tendency may be noticed for γS, showing that the total covered demand decreases even though the total demand increases as shown in figure 4. Different results are retrieved for the system with floor heating. Here, both a maximum at 1.0 m³ and minimum at 2.5 m³ for γS may be noticed and to a lower extent for γD due to the increasing total electricity consumption as shown in figure 4. The resulting graph for floor heating shows that optimisation of the cover factor can be achieved, but that other factors play an important role too: providing the possibility of heat storage in order to influence or improve the cover factor of the BIPV system shows to have little to no impact if no attempt is made for adapting the control strategy of the combination heat pump - storage towards power load matching and if the required power of the heat pump is not reduced. However, it must be stated that the total yearly consumption of the system remains important, as increasing energy demands are noticed for higher storage tank sizes. 1.0
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Figure 8. Cover factors γD, γD,max and γS (-) for a dwelling equipped with floor heating [left], LT radiators [mid] and a combination of both floor heating and LT radiators [right] as a function of the storage tank size (m³) when a heat pump is used for heat production and for a BIPV system of 4 kWp.
4.3 Grid peak loads The grid peak load is an important factor from a grid-point of view as the ratio of the highest peak load to the total energy demand plays an important role in the design of power supply towards the dwelling. As the set point temperature for the storage tank and the supply temperature for heating - and as a result also the heat emission system - strongly determine the power demand of the heat pump, the choice of heat emission system may affect the peak power demand of the dwelling. The influences of the heat emission system and the storage tank size on the highest peak power demands of the dwelling are shown in figure 9. Additionally, the error bars indicate the average peak load above 6 kW and the standard deviations within the range of 6 kW and the highest peak. The limit of 6 kW is chosen arbitrarily. The retrieved highest peak power demands are found to lie in the range between 10.8 and 13.2 kW and show that reduction of the peak power demand is possible. However, no clear relation is found between the peak power demand and the heat emission system nor the storage tank 8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
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size as the heat pump is not the only factor determining the peak power demand. The highest peak powers (i.e. above approximately 9 kW) are found to be caused by a stochastic combination of peak loads such as the use of media, cooking and power demand for space heating and DHW production.
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Figure 9. Grid peak demands (kW) as a function of the storage tank volume (m³) [left] and as a function of the BIPV installation size (kWp) [right]. The bars show the highest peak occurring through the year, whereas the error bars denote the average peak load above 6 kW and the standard deviations within the range of 6 kW and the highest peak. The 6 kW limit is chosen arbitrary.
4.4 Discussion It was shown that the choice of heat emission system and providing the possibility of heat storage has little to no impact on the cover factor of the BIPV system and on the reduction of peak loads if no attempt is made for adapting the control strategy towards the grid-interaction. As the applied control strategy for heating assumes that the inhabitant of the dwelling controls space heating by switching on space heating when being at home and comfort is not met, it may be used as benchmark for possible optimization with an adapted control strategy taking into account the storage tank, the heat pump and required comfort. The application of an appropriate control strategy for the heat pump (and heat emission system) may result in better results (De Coninck et al. 2010) and a higher impact of the heat emission system on the grid-interactions of the dwelling equipped with a BIPV installation. By means of adjusting the control strategy of the heat pump, the SPF of the system may be increased by adapted set-point temperatures and control of the operating times based on outdoor temperatures or the cover factor may be influenced by allowing control based on the instantaneous power balance of the dwelling. Furthermore, the application of large storage tanks may allow the application of smaller heat pumps (with lower capacity), which is not taken into account within this paper. 5.
CONCLUSION
A multi-zone TRNSYS model for a dwelling with compression heat pump for both space heating and domestic hot water production, domestic consumers and on-site photovoltaic generation was set up to quantify the impact of the type of heat emission system on the autoconsumption and grid-interaction of a BIPV system in a Belgian climate. It was shown that the heat emission system influences the grid-interaction of a dwelling when a BIPV system is applied in combination with an electricity-driven heat pump for space heating and DHW production in low-energy dwellings. A slower system with lower supply8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
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temperatures such as floor heating shows to fit better with the local power supply of the PV system as less and lower peak loads occur, but may result in a lower SPF and a higher overall electricity consumption. The comparison of different PV sizes shows that the PV size to achieve a net zero-energy dwelling only depends on the overall yearly electricity consumption, whereas the resulting cover factor strongly depends on the demand profile governed by the heat emission system. Providing the possibility of heat storage has shown to have little to no impact in order to influence or improve the cover factor of the BIPV system and reduce peak loads if no attempt is made for adapting the control strategy of the combination heat pump - storage towards power load matching. The retrieved results show clearly that future optimisation of buildings with on-site generation towards its grid impact consists of a multi-parameter question. A dwelling may rely to a lower extent on the grid for virtual storage of the produced electricity, but may show a higher overall electricity consumption whereas both factors play a role in the assessment of future net-zero energy buildings. REFERENCES de Almeida, A., Fonseca, P., Bandairinha, R., Fernandes, T., Araújo, R., Nunes, U., Dupret, M., Zimmermann, J.P., Schlomann, B., Gruber, E., Kofod, C., Feildberg, N., Grinden, B., Simeonov, K., Vorizek, T., Markogianis, G., Giakoymi, A., Lazar, I., Ticuta, C., Lima, P., Angioletti, R., Larsonneur, P., Dukhan, S., de Groote, S., de Smet, J., Vorsatz, D., Kiss, B., Loftus, A.-C., Pagliano, L., Roscetti, A. & Valery, D. (2008). REMODECE Residential monitoring to decrease energy use and carbon emissions in Europe. Final report, p.96. Baetens, R., De Coninck, R., Helsen, L. & Saelens, D. (2010). The impact of domestic load profiles on the grid-interaction of building integrated photvoltaic (BIPV) systems in extremely low-energy dwellings. In : Zero Emission Buildings, proceedings of Renewable Energy Research Conference 2010, Trondheim, 7-8 June 2010, 121-131. De Coninck, R., Baetens, R., Verbruggen, B., Driesen, J., Saelens, D. & Helsen, L. (2010). Modelling and simulation of a grid connected photovoltaic heat pump system with thermal energy storage using Modelica. To be published in : proceedings of 8th International Conference on System Simulation in Buildings, December 13-15, 2010, Liege. Duffie, J.A. & Beckman, W.A. (1991). Solar Engineering of Thermal Processes (2nd ed.). Wiley. EN 442:2003 Specification of radiators and convectors. Part 1 - Technical specifications and requirements. Part 2 - Test methods and rating. Part 3 - Evaluation of conformity. EN 12831:2003 (E) Heating systems in buildings - Method for calculation of the design heat load EN 15316-4-2 Heating systems in buildings - Method for calculation of system energy requirements and system efficiencies - Part 4-2: Space heating generation systems, heat pump systems EurObserv'ER (2009). Baromètre photovoltaïque / Photovoltaic barometer : 9 533,3 MWc dansl'UE / in the EU. Systèmes solaires - Le journal du photovoltaïque 1, 72-103. Firth, S., Lomas, K., Wright, A. & Wall, R. (2008). Identifying trends in the use of domestic appliances from household electricity consumption measurements. Energy and Buildings 40, 926-936. 8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
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Flemish Region (2006). Bijlage I - Bepalingsmethode van het peil van primair energieverbruik van woongebouwen. Glorieux, I. & Vandeweyer, J. (2002). Statistische studie nr110 - 24 uur ... Belgische tijd, een onderzoek naar de tijdsbesteding van de Belgen. Hasan, A., Kurnitski, J. and Jokiranta K. (2009). A combined low temperature water heating system consisting of radiators and floor heating, Energy and Buildings 41, 470-479. Huld, T. & Suri, M. (2010). PVGIS, PV Estimation Utility. Available at: http://re.jrc.ec.europa.eu/pvgis/apps/pvest.php?lang=en&map=europe . Liu, D., Chang, W.R. & Lin, J.Y. (2004). Performance comparison with effect of door opening on variable and fixed frequency refrigerators/freezers. Applied Thermal Engineering 24, 2281-2292. Meteotest (2008). METEONORM Version 6.1 - Edition 2009. Neyens, J. (2009). The PV market in the Flemish region - Status May 2009. prEN 15377:2005 (E) Heating systems in buildings - Design of embedded water based surface heating and cooling systems. Part 1 - Determination of the design heating and cooling capacity. Part 2 - Design, dimensioning and installation. Part 3 - Optimizing for use of renewable energy sources. Richardson, I. & Thomson, M. (2008). Domestic lighting demand model 1.0. Loughboury University. Richardson, I., Thomson, M. & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings 40, 1560-1566. Richardson, I., Thomson, M., Infield, D. & Delahunty, A. (2009). Domestic lighting: A highresolution energy demand model. Energy and Buildings 41, 781-789. Solar Energy Laboratory (2009). TRNSYS 16 - A transient system simulation program. Spur, R. Fiala, D., Nevrala, D. & Probert, D. (2006). Influence of the domestic hot-water daily draw-off profile on the performance of a hot-water store. Applied Energy 83(7), 749-773. TPDCB (2010). The power consumption database. Available at: www.tpcdb.com. VREG (2009). Marktmonitor 2009, Vlaamse reguleringsinstantie voor de elektriciteits- en gasmarkt, 82p.
8th International Conference on System Simulation in Buildings, Liege, December 13-15, 2010
