5.1. Weather Conditions
Riyadh has a dry desert climate, with an average annual temperature of 28 °C and a relative humidity of 25%, according to the King Abdullah City for Atomic and Renewable Energy climate classification [
33]. The hourly ambient dry bulb temperature and relative humidity are shown in
Figure 7.
As anticipated, the dry bulb temperature increases during the summertime when it ranges between 30 °C to 42 °C. The lowest values for the dry bulb temperature were between 7 °C and 20 °C during winter. The relative humidity presents an inverse behaviour as shown in
Figure 7 when compared to the dry-bulb temperature. During the winter season, the relative humidity ranges between 40 and 70%. However, dry conditions with relative humidity between 10 and 30% prevail during the summer season (April to October).
Figure 7 shows that there is an urgent need for the cooling system to retain internal space in the comfort condition zone.
On the other hand, Riyadh has high solar radiation, as shown in
Figure 8. The high solar radiation increases the reliability of solar driven systems. The maximum irradiance (
IDN) exceeds 1100 W/m
2 while even the minimum average values are around 500 and 650 W/m
2 which is extremely viable for any solar thermal system. The diffuse solar radiation (
IdiffH) displays a varying range between 100 and 250 W/m
2. As mentioned before, the proposed solar adsorption system will serve the building for the whole day (24 h) and throughout the year. However, during the winter season, the system is mostly switched off.
5.3. Building Energy Analysis
A dynamic simulation of the building thermal behaviour was conducted on an hourly basis over a 1-year period with the model described in
Section 3.3.
Figure 11 shows both the monthly cooling requirement as well as the instantaneous cooling load.
The fluctuation depicts the hourly cooling demand, while the bars shows the seasonal change in cooling energy load. The hourly cooling load depicts not just seasonal fluctuations but also daily differences between day and night.
As for the validation of the simulation results [
19], this was modelled on a villa in Riyadh following the same envelope characteristics as the Saudi Arabia thermal standard. The cooling load per unit area is 82.5 W/m
2, whereas it is 85 W/m
2 as per the present model. As is clear from
Figure 11, the monthly demand is between 6000 kWh and 8000 kWh (30.4–40.4 kWh/m
2/month) during summertime. The minimum monthly demand loads were recorded during wintertime between 1500 kWh and 2000 kWh.
The main reason for this high cooling load is because a 24 h operation is in place compared to shorter operating times in the other works which follow the ASHRAE standards 90.2 [
36] and 62.1 [
37]. This shows that the required cooling power is in compliance with previously published studies while the cooling energy is higher due to the longer operational hours used in this study.
The high cooling demand in Saudi Arabia is due to several reasons: high dry bulb temperature and high solar radiation throughout the year. While Riyadh has reasonably low relative humidity, as seen in
Figure 7, Saudi cities located closer to the sea have significantly higher relative humidity. This difference across the different regions of Saudi Arabia will be discussed in a future publication. The high cooling demand shows that Riyadh and the other cities in Saudi Arabia require efficient cooling systems. It is essential to evaluate the effect of different design aspects to achieve beneficial economic and environmental performance.
5.4. System Performance Analysis
In this section, a parametric study on the system has been performed over the volume of the storage tank, the solar collector area, and the auxiliary heater set point.
Figure 12 shows the effect of the storage volume on the system solar fraction (
SF) for various solar collector areas. Increasing the solar collector field area could increase the
SF up to 97% for an optimal storage volume of 300–400 m
3.
As expected, the annual energy of the auxiliary heat was recorded at its lowest at a solar field size of 5500 m
2 and highest at a solar field size of 4000 m
2. It is interesting to note that for a fixed solar field size, the auxiliary energy demand increases for larger storage tank sizes. This is due to the increased thermal losses from the hot water storage tank as well as the nonoptimal temperature profile in the larger storage tanks. The annual auxiliary energy indicates that using a solar field of 4000 m
2 would need more thermal power to compensate the system demand, which would increase the cost and the CO
2 emissions. However, at 5500 m
2, the annual auxiliary energy would be supported by minimum power, as shown in
Figure 12. As mentioned in this regard, the optimum storage volume should be kept between 350 and 500 m
3 for each case.
Figure 13 illustrates the variation of the SF against the auxiliary heater setpoint temperature.
Initially, the solar fraction increased with the auxiliary setpoint temperature up to an optimum point and decreased with further increases of the setpoint temperature. The value of the optimum setpoint depends on the size of the solar field area and storage tank size, which here is fixed at 350 m
2, and should be in the range of 60–75 °C. Concurrently, the setpoint temperature of the auxiliary heater influences the energy provided by the auxiliary heater. This demonstrates the complex interaction between the supply and return temperatures in the operation of solar-driven cooling systems. For example, if the setpoint temperature is too high, the hot water storage tank will be unable to absorb all the energy from the solar field, which will reduce the solar fraction.
Figure 12 and
Figure 13 indicate the system viability throughout the year under different operating conditions.
Figure 14 represents contrasting monthly energy loads such as the cooling load, useful energy, and energy requirement for specific units such as adsorption chiller and cooling tower.
As shown in
Figure 14, the energy load of the cooling tower is higher than the rest. The thermal power of the cooling tower is high because of the large amounts of heat that need to be rejected from the adsorption chiller. This would affect the system performance throughout the year besides the amount of CO
2 emission.
In the same regard,
Figure 15 shows the monthly energy loads for the vapour compression cycle for 1 year.
The figure shows the differences between load variations based on compressor power, chiller load, and heat rejection. It is demonstrated in
Figure 15 that the chiller heat rejection is the highest load among the other units. Increasing the chiller load would be followed by an increase in the chiller heat rejection.
The monthly energy consumption of the pumps was calculated for the cases shown in
Figure 14 and
Figure 15. The energy requirement for continuously operating pumps for the
ADC system is around 20 MWh (depending on the exact pipe layout) and around half for the
VCC system. The values for the
ADC system are the worst case because the solar collector loop is not operating during the night time. While the
ADC system has a higher pump energy requirement, these are significantly lower than the energy requirements to run the adsorption chiller or
VCC.
Figure 16 shows the percentage of primary energy saving (
PES) based on the solar collector area and the storage tank volume.
Increasing the storage tank volume would increase the
PES up to 70% for 5500 m
2 of solar area. After this optimum point,
PES does not increase further, and it is not beneficial to increase the storage tank volume further. For the test range of solar areas, a storage volume between 300–400 m
3 would be optimal.
Figure 17 shows the percentage of savings in the annual operating costs of the
ADC system compared to the
VCC system for different solar fields and storage sizes.
By optimizing the storage tank size, the savings of the reported
ADC would increase from 48% to 62% for a 4000 m
2 solar field size and from 64% to 75% for a 5500 m
2 solar field size. Increasing the storage volume will increase the percentage of saving to a certain limit. For the best evaluated case, the
ADC system has a 69% lower primary energy consumption compared to the
VCC system. The percentage of savings has been increased due to the indirect effect of the increased solar fraction. Although increasing the solar field area would increase the investment cost; a future study will optimise the levelised cost as well as the energy savings. For instance, the behaviour in
Figure 17 shows an increasing trend from 150 m
2 up to 300 m
2. After that, the behaviour shows a nearly constant trend related to the volume increase. Accordingly, a value range of 300–400 m
3 should be adopted in this work.
As anticipated and based on the effect of the solar fraction coefficient, the percentage of saving at 5500 m
2 is the highest at 75%. On the other hand, at 4000 m
2 the percentage of saving is the lowest at about 45%.
Figure 18 shows the effect of storage tank volume on the CO
2 saving percentage. These savings follow the same trends as the operational cost savings.
Figure 19 shows the percentage of energy provided by the solar field to achieve the required cooling energy. As mentioned earlier, a storage volume of 300–400 m
3 is optimal in that regard. For a solar field area of 5500 m
2, the percentage of energy from the solar field is about 85%. However, for a solar field area of 4000 m
2, the percentage of energy will drop to around 75%. Similarly,
Figure 20 shows the percentage of energy provided by the solar field against the auxiliary setpoint temperature. As anticipated, increasing the auxiliary setpoint temperature from 50 °C to 85 °C will decrease the percentage of the energy provided by the solar field by around 15%.
Table 6 shows that both cooling systems share some mechanical components, such as a pump and a cooling tower. The
VCC has approximately three times lower capital costs compared to
ADC because the
ADC system also needs a solar field, a evaporator, a condenser, and adsorbent beds, leading to its high capital cost, which consequently leads to a large increase on the investment cost. Because the larger investment cost is balanced by lower operational costs and environmental benefits, the profitability of solar
ADC systems will depend, among other factors, on the cost of finance and carbon costs. This is beyond the scope of this study but will be handled in a future optimisation study.