## 5 Conclusions  
This paper proposed an LCOE model to assess the feasibility of five emerging PV technologies: N-type, TOPCon, bifacial TOPCon, PERC and HJT, considering three installed powers for each one: 3 KW, 5 kW and 7 kW. The PV systems are evaluated based on their technical performance and economic parameters. The proposed approach considers initial capital investment, government incentives, end-of-life residual value of the system, operation and maintenance costs and divides them to the total energy out of the PV system. The energy produced by each system during its lifetime is determined by calculation, depending on the location of the system and its performance.  
The results show that the LCOE values depend on the capacity of the system, the higher the capacity is, the lower the LCOE values are. Moreover, government incentives cover the capital investment costs in most cases, the values ranging between 2.39-3.21 c\(\ell\)/kWh for TOPCon, 2.41-3.16 c\(\ell\)/kWh for PERC, 2.46-3.19 c\(\ell\)/kWh for N-type, 2.50-3.16 c\(\ell\)/kWh for bifacial TOPCon and 2.92-3.24 c\(\ell\)/kWh for HJT. Without subsidies, the LCOE values are higher from 6.05-7.38 c\(\ell\)/kWh for TOPCon, 6.17-7.33 c\(\ell\)/kWh for bifacial TOPCon, 6.18-7.40 c\(\ell\)/kWh for N-type, 6.32-7.52 c\(\ell\)/kWh for PERC to 6.51-7.94 c\(\ell\)/kWh for HJT. Nevertheless, based on these values the most feasible PV technology is TOPCon, while the most expensive one is HJT.  
The sensitivity analysis concluded that the LCOE values depend on the capital investment and operation and maintenance costs. When incentives are granted, the 3 kW and the majority of 5 kW systems are not affected when the capital investment costs vary. The LCOE values of the 7 kW systems, however, increase and decrease alongside the percentage change in capital investment costs. Similar situations are employed when the operation and maintenance costs present variations, with the smaller systems being more affected than the larger ones.  
The analysis performed in this study is valuable to the decision makers and can assist the household consumers to select the optimal solution for their PV system, indicating the installed power and the PV module technology. Moreover, to the authors knowledge, there are few studies that assess emerging PV technologies, the results showcasing the correlation  
Figure 9: \(LCOE_{2}\) values obtained based on change in _OM_ costs.  
between PV technology improvements and capital investments costs. Our findings are valuable for further investigations that analyze the same PV module technologies worldwide.  
Due to new emerging technologies and continuous PV efficiency improvements, the PV technologies must be assessed from an economic point of view for an objective and current analysis. Thus, the learning curve concept might be implemented in future work to analyze the behavior of performance improvements in the studied PV module technologies. Moreover, future work may also include the assessment of commercial and utility scale PV systems with the proposed LCOE model, to highlight significant economic differences between the five PV module technologies.  
Conceptualization, M.C. and C.C.; methodology, M.C.; validation, C.C., R.-A.T. and F.M.S.; formal analysis, M.C.; investigation, C.C.; resources, R.-A.T.; data curation, M.C. and F.M.S.; writing--original draft preparation, M.C.; writing--review and editing, C.C. and R.-A.T.; visualization, M.C. and C.C.; supervision, C.C. and R.-A.T.; project administration, M.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This work was supported by a grant from the National Program for Research of the National Association of Technical Universities--GNAC ARUT 2023, Grant No. 20/01.07.2024.  
The authors declare no conflicts of interest.