This section concerns elaboration of a base case for the forthcoming sensitivity analysis (section 3.6), for which the previously simulated case study will be normalized to (i) standard weather conditions and (ii) standard heat stor-age/distribution. The diverse normalization scenarios and associated border conditions are summarized in Table 3:1.
Name Year Demand Layout5 Collectors
Validation 2012 monitored decentralized non-insulated Norm 2012 2012 modelled decentralized non-insulated Norm Std standard modelled decentralized non-insulated Base standard modelled centralized non-insulated Base, ins. standard modelled centralized insulated
Table 3:1 Normalization scenarios with respective border conditions.
3.5.1 Normalization to standard weather
The upcoming simulations require different demand profiles than the monitored one, which was specific to the 2012 weather conditions, system layout and building type. Such profiles will be generated by the simplified model de-scribed in section 3.3.3. Before using this model on different system layouts and building types, we will now simulate the analysed case study once again, this time with the modelled instead of the monitored heat demand. The objective of this exercise is twofold: (i) understand the effect of the simplified SH heat demand model on the simulation results;
(ii) normalize the simulation results to standard weather conditions, which will be used for the subsequent sensitivity analysis, in order to avoid the abnormally low temperatures of February 2012.
In a first step (Norm 2012), characterization of the demand concerns the monitored 2012 weather data, for which the annual DHW and SH demands remain the same as for the monitoring and validation (47.7 kWh/m2 and 20.6 kWh/m2).
For SH, the hourly profile is recalculated by the linear dependency on outdoor temperature described in section 3.3.3.
The outdoor set point to start SH is fixed to 15°C, in concordance with observation (Fraga et al., 2015), and the nomi-nal load at 0°C is fixed to 9 W/m2, so that the annual integral corresponds to 20.6 kWh/m2. For DHW, which is inde-pendent of weather conditions, the monitored hourly profile is used as is.
In a second step (Norm Std), characterization of the demand concerns standard hourly weather data for Geneva (SIA2028, 2010). Comparison between standard and 2012 weather data can be summarized as follows (see detailed monthly and annual values in Annex D). Despite far less extreme temperatures in February (2.9°C average, as com-pared to -1.8°C in 2012), the winter months are in average 0.1 K colder than in 2012. As a result, the total heating degree days (2385 K.day, Oct-Apr, on a 18/12 °C basis) are only 1% more important than in 2012, which is also the case of the global horizontal solar radiation (1277 kWh/m2). Finally, the modelled SH demand (same linear function as above) amounts to 20.8 kWh/m2, also 1% more than in 2012.
Results of both these scenarios are summarized in Figure 3:7.
5 decentralized: decentralized DHW storage with single loop distribution;
centralized: centralized DHW storage with standard distribution.
Towards a SPF of 5? Numerical sensitivity analysis for various building demands
Figure 3:7 System performance with normalized weather and heat demand:
monitored demand, 2012 (Validation); modelled heat demand, 2012 (Norm 2012); modelled heat demand, standard weather data (Norm Std).
For 2012, simulation results with the modelled heat demand (Norm 2012) are very similar to simulation results with the monitored heat demand (Validation), at least at aggregated system level. At the level of direct solar heat produc-tion Qsol,dir , the slight discrepancy can be explained by a finer analysis, in terms of the monthly dynamic of Qsol,dem . When simulating with the monitored heat demand, Qsol,dem fairly well corresponds to monitoring (Figure 3:6); when simulating with the modelled heat demand, it remains below monitoring during the months of March to May, but catches up during summer (see Annex E). This discrepancy is related to the simplified modelling of the SH demand by way of a linear function in terms of outdoor temperature, not taking into account the capacitive effects of the building nor the dependency of SH demand to solar gains, which both turn out relevant during the months between winter and summer. However, the annual difference of 2.3 kWh/m2 between the two scenarios remains very low as compared to the energy flows of the overall system balance, which indicates that the simplified heat demand model is good enough for overall system analysis.
At overall level, simulation with the standard weather year (Norm Std) yields quite similar results as for 2012. The main differences concern i) a drop of direct electric heating (1.9 instead of 3.6 kWh/m2), due to less extreme tempera-tures in February; ii) a slight decrease of direct solar heat production (Qsol,dir of 7.9 instead of 11.2 kWh/m2), due to higher wind speeds that have an important impact in the solar collectors heat loss (see section 3.3.2).
0
Input energy flows kWh/m2/yr Edir
Ehp
Input energy flows kWh/m2/yr Edir
Ehp
Towards a SPF of 5? Numerical sensitivity analysis for various building demands
3.5.2 Normalization to standard heat storage/distribution
As mentioned previously, the above monitored system layout suffers from a peculiar heat distribution (single loop for alternate distribution of SH or DHW, with individual DHW tanks in the flats), which does not allow for solar preheating of DHW. As an alternative, we will now consider a standard system layout consisting of a centralized SH and DHW storage in the technical room, with separate distribution for SH and DHW (Figure 3:5 bottom). This scenario (Base) will serve as the base case for the forthcoming sensitivity analysis (section 3.6.1). The components are identical to the layout of the case study: solar collectors (116 m2, i.e. 0.125 m2 per m2 heated area); HP (35 kW, i.e. 37.6 W per m2 heated area, corresponding to the maximal hourly heat demand) and storage (8400 L, i.e. 9.1 L per m2 heated area).
Hourly input is given by the standard weather year and by the corresponding heat demand (DHW: 47.7 kWh/m2, SH:
20.8 kWh/m2).
Simulation results (Figure 3:8) show an important gain of direct solar heat production (19.4 kWh/m2), as compared to the decentralized DHW storage (7.9 kWh/m2). Unlike in latter case, direct solar production now covers a major part of the demand from May to September (see Annex F). As a consequence the electricity for the HP drops to 15.0 kWh/m2. Furthermore, the installed HP capacity (37.6 W per m2 heated area) almost matches the maximal hourly heat demand (38.7 W per m2 heated area), which was not the case for the single distribution loop and the related batch charge of the individual DHW storage tanks, where the maximal hourly heat demand reached up to 65.2 W per m2 heated area (see load curves in Annex G). As a consequence, direct electric heating now drops to 0.7 kWh/m2 (1% of the total heat demand). As a result of both these improvements, the SPFsys reaches a value of 4.4.
Figure 3:8 System performance with normalization of system layout:
decentralized DHW storage (Norm Std); centralized DHW storage (Base); centralized DHW storage + insulated solar collectors (Base / insul.).
0
Input energy flows kWh/m2/an Edir
Ehp
Towards a SPF of 5? Numerical sensitivity analysis for various building demands
Finally, as an alternative to preceding base case scenario, we analyse the possible added value of insulating the rear face of the unglazed solar collectors (Base / insul.), for which the loss coefficients of the solar collector field are set to:
hsol.0 = 9.7 W/K.m2; hsol.v = 1.8 W/K.m2 per m/s (Da Costa Louro Branco, 2005). The insulation should improve the solar collectors efficiency in summer (increased solar gains), but lower their efficiency in winter (reduced heat exchange with ambient air, leading to lower evaporator temperature). However, as can be seen in Annex B and Annex F, we only observe a small gain of direct solar heat production (1.9 kWh/m2), as well as a small increase of direct electric heating (0.6 kWh/m2). Finally, both SPFhp and SPFsys suffer a slight drop of 0.1.