# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 4 Discussion
Large-scale installations can be connected without any tariff, as the cost of energy is lower than the retail cost of energy at any case. The probabilistic method (Monte Carlo) has been evaluated precisely for all the variable inputs if grid parity is achieved and how profitable the installation can be as a business. In Table 15, it can be determined when the LCOE is achieved, considering a recovery factor value of 0.1102. The representative cases where the LCOE is lower than the retail cost of energy have been highlighted. Table 16 shows when the IRR is positive; this means the investment can be considered a business. The representative cases where the IRR is positive have been highlighted. Finally, the means applied to the variables can be taken into consideration. These means defining the likelihoods defined across this paper where the panel cost is key to determine the LCOE and the IRR. Table 17 presents a summary that determines the results for the full range of options that we have determined for all the variables, which have been represented across this paper. The success rate represented in Table 17 determines the percentage of how many cases of LCOE have resulted above the retail electricity cost. As can be seen in Table 17, the rate is quite high. However, the financial parameters, such the interest rate, loan duration and amount loaned, significantly impact the IRR. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**PV Module Cost (\(\ell\)/Wp)**} & \multicolumn{3}{c}{**IRR (\%)**} \\ \cline{2-4}  & **5 kW** & **5 kW** & **50 kW** \\ \hline
1 & \(-4.81\) & \(-2.05\) & \(-0.32\) \\
0.8 & \(-3.83\) & \(-0.53\) & **1.67** \\
0.6 & \(-2.68\) & **1.39** & **4.36** \\
0.4 & \(-1.31\) & **3.97** & **8.02** \\
0.2 & **0.40** & **7.46** & **14.52** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison of the IRR based on the Monte Carlo method for all the cases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Power (kW)**} & **Mean PV Module** & **Mean Yield** & **Mean Interest** & **Mean LCOE** & **Mean** & **Success** \\  & **Cost (\(\ell\)/Wp)** & **(kWh/kWp)** & **Rate (\%)** & **(\(\ell\)/kWh)** & **IRR (\%)** & **Rate (\%)** \\ \hline
5 & 0.8 & 1300 & 6 & 0.1494 & \(-0.97\) & 82.26 \\
50 & 0.6 & 1300 & 6 & 0.1019 & 5.92 & 99.59 \\
500 & 0.4 & 1300 & 6 & 0.0727 & 14.34 & 99.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Summary of the means of IRR and LCOE extracted from the Monte Carlo method for all the cases. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**PV Module Cost (\(\ell\)/Wp)**} & \multicolumn{3}{c}{**LCOE (\(\ell\)/kWh)**} \\ \cline{2-4}  & **5 kW** & **5 kW** & **50 kW** & **500 kW** \\ \hline
1 & 0.2756 & 0.2154 & **0.1585** \\
0.8 & 0.2514 & 0.1911 & **0.1385** \\
0.6 & 0.2271 & **0.1669** & **0.1184** \\
0.4 & 0.2029 & **0.1427** & **0.0984** \\
0.2 & 0.1787 & **0.1184** & **0.0783** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of the LCOE based on the Monte Carlo method for all the cases. **Author Contributions:** Conceptualization, R.D.-L. and J.L.B.-A. ; Formal Analysis, C.J.S.-M.; Methodology, C.J.S.-M., R.D.-L. and J.L.B.-A. ; Software, C.J.S.-M.; Validation, R.D.-L. and J.L.B.-A. ; Writing--Original Draft, C.J.S.-M.; Writing--Review & Editing, R.D.-L. and J.L.B.-A. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflicts of interest.