## 3 Results  
### Overall System Design  
As stated in the previous section, it is very important to properly select the zones that will be used for the PV systems. Several factors must be used in determining the optimal combination of zones. The zone score should reflect the priority of implementation. The main parameter to focus on is the required energy (E\({}_{\text{R}}\)), where the optimum value is that the generated energy exactly matches the projected load demand. Higher or lower energy generation will be penalized equally, since the system is not paid back for any additional energy generated as per the net metering regulations. Minimizing the losses (L) is very important to optimizing the infrastructure utilization. Specific yield (SC) quantifies the ratio of the energy generated to the installed capacity, and it is affected by various design parameters, such as the height of the installation, inclination angle, and projected dust accumulation. But it is confined with the constraints of the site nature, and thus lower weight is assigned but is considered valuable in the score equation. System capacity (SC) is considered as the lowest weighted factor as it influences the initial investment only. Changing the factors might change the priorities of implementation and changing the weights might influence the zone score as well, but that would not change the order of zones significantly. Regardless of the order, it is a guide for the designer to focus on certain zones to meet the projected load demand. A factor-weighted equation is used to score the different combinations in Equation (1). \[\text{Score}=10\%\text{SC}+40\%\text{E}_{\text{R}}+30\%\text{L}+20\%\text{ SY}, \tag{1}\]  
SC: system capacity factor, the value will be 1 if it meets or exceeds the required capacity. RE: required energy, the value will be 1 if it exactly meets the required Energy, while it will be less for scenarios which provide more or less energy than the required energy. OL: overall average loses due to shading and direction. The value will be 1 for the lowest losses scenario, while it will be less for scenarios with higher losses. SY: specific yield which is the energy to power ratio. The value will be 1 for the highest ratio scenario, while it will be less for scenarios with smaller ratio values. The weights for the different factors represent site and project specific priorities. The weight may be changed based on the priorities that best serve implementation. As quite a large number of combinations may be used, six scenarios are defined as summarized in Table 6. The scenarios follow two main themes: utilizing the lowest shading-induced losses zones or utilizing main areas close to the major load demand. The first scenario considers all zones along the routes. Hence, it is a very big area and possibly able to contain large PV systems with more energy generation capacity than needed. Although a huge amount of energy can be generated in this scenario, losses due to direction and shading are tremendous. In scenario two, efficiency is increased by eliminating PV systems in zones with shading losses higher than 20%. In defining scenarios, shading losses were more important than losses due to the direction of the installed PV system. It is noticed that in this scenario, zones 2, 5, 6, 8, and 12 were excluded from the calculation. Therefore, the energy produced overall is less than in scenario one, but still covers more than the required energy for the project. In the third scenario, only zones that have 10% shading losses or lower are considered. In this \begin{table}
\begin{tabular}{c c} \hline
**Item** & **Emission** \\ \hline Emission Factor of Diesel & 74,100 kg/TJ \\ Quantity of Consumed Diesel & 5,818,447 L \\ Diesel Calorific Value & 43 MJ/kg = 0.000043 TJ/kg \\ Direct CO\({}_{2}\) Emission & 15,A24,713 kg CO\({}_{2}\) \\ WTT (Well-to-Tank)–Indirect Emissions & 23\% including black carbon \\ Total Carbon Emissions Due to Diesel (Well-to-Wheel) & 18,972,398 kg CO\({}_{2}\) \\ \hline \end{tabular}
\end{table}