## 10 Conclusions  
The current study has focused on off-grid rural electrification in a remote rural area of the Indian state of Odisha. The location is still not electrified due to its remote location because grid extension to that area is not feasible for two reasons: first, extending the grid to that area is more expensive because it is more than 50 km away. The second reason is that grid extension is not a viable option for the location since it is located in a densely forested area surrounded by hilly mountains. As a result, electrification of the study area is planned, utilizing the study area's readily available RE resources, such as solar and biomass. However, due to the time dependence and metrological weather conditions dependence of these RE resources, the power supply is not continuous. As a consequence, three different types of battery technologies have been proposed for the current study. To identify a battery technology that is technologically and economically suitable for the study area.  
The current study used available RE resources and battery technologies to model three configurations: PVP/BIOMG/BATTERY(Ni-Fe), PVP/BIOMG/BATTERY(Li-Ion) and PVP/BIOMG/BATTERY(LA). The main goal here is to identify the configuration that is appropriate for electrifying the study area. The identification process incorporated with nine well-known and established algorithms, such as Differential Evolutionary Algorithm, Genetic Algorithm, Moth Flame Optimization, Dragonfly Algorithm, Particle Swarm Optimization, Grey Wolf Optimization, Ant Lion Optimization, Grasshopper Optimization Algorithm and Dynamic Differential Annealed Optimization (DDAO) Algorithm. When DDAO was compared to the remaining algorithms, it produced the global best optimal results. Based on the results, the PVP/BIOMG/BATTERY(Ni-Fe) configuration was identified as the feasible configuration to electrify the study area.  
When compared to the configurations made up of Li-Ion and LA battery, the configuration of Ni-Fe battery provided the lowest LCC value as 367,586 USD, which is 39% and 64% lower in percentage terms. The optimal configuration has been further analyzed with different loss of power supply probability values ranging from 1% to 5%. According to the findings, at a loss of power supply probability value of 1%, the optimal configuration may be recommendable for electrifying the study area.  
Conceptualization P.P.K.; methodology, P.P.K.; software, P.P.K.; validation, P.P.K. and M.J.; formal analysis, P.P.K.; investigation, P.P.K. and V.S.; resources, M.J. and V.S.; data curation, M.J. and P.P.K.; writing--original draft preparation, P.P.K.; writing--review and editing, M.J. and V.S.; visualization, P.P.K., V.S. and M.J.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L, V.S. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chair of Electrical Engineering Fundamentals, Wroclaw University of Science and Technology K38W05D02.  
Not Applicable.  
No conflict of Interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Configuration** & **Quantity \& Cost** & **LPSP (0\%)** & **LPSP (1\%)** & **LPSP (2\%)** & **LPSP (3\%)** & **LPSP (4\%)** & **LPSP (5\%)** \\ \hline \multirow{2}{*}{PVP/ BIOMG/ BATTERY (Ni-Fe)} & N\({}_{\text{PV}}\) & 511 & 442 & 429 & 412 & 399 & 398 \\ \cline{2-8}  & N\({}_{\text{BAT}}\) & 351 & 332 & 322 & 316 & 311 & 301 \\ \cline{2-8}  & LCC in USD & 367,586 & 339,523 & 330,600 & 322,939 & 316,847 & 310,933 \\ \cline{2-8}  & LPSH & 0 & 182 & 317 & 430 & 564 & 632 \\ \hline \end{tabular}
\end{table}
Table 6: Optimum configuration results for various LPSP values.