**7. Conclusions**

In this research, an algorithm has been provided, which explains the hierarchy of the way the locations of EV charging stations should be determined by experimental exemplification from some literatures and the Ankara case study. The algorithm has been made up of three sections: Finding a location by specifications of the grid infrastructure, categorizing the capacity of transformers, and transformer analysis. It has been created especially for grid designers who have recently started to determine the points for charging stations in cities. In the Ankara case study, considerations for stability of the grid has been eliminated from the algorithm, because the grid can be considered to be stable for having adequate reserved power from neighboring control areas. The novelty of this research is the assertion that some limited parameters can show grid equipment conditions in the past to solve grid problems. Finally, this research has built a congruency and connection between the algorithm theories, programming languages, and practical strategy for the grid. The research has indicated that the transformers' condition, electric infrastructure, budget, and various other aspects, which are influenced by energy consumption, are important factors in approaching this issue. Furthermore, this research has shown that the maintenance history of the grid equipment and infrastructure are the key points in determining the locations of electric vehicles charge stations. The optimum locations have been determined using a Genetic Algorithm, which has been mathematically interpreted and analyzed. EVs and storage are garnering interests to the People's Republic of China, Europe, Japan, the United States of America, and recently, it has caught India's attention for the purpose of decreasing GHG emission as soon as possible. Science and technology is improving every second. This algorithm can be a stepping stone in the field of technology, which is used for autonomous systems on storages supplied by the grid or DC link.

**Author Contributions:** All authors contributed equally to the research activities and for its final presentation as a full manuscript.

**Funding:** No source of funding was attained for this research activity.

**Acknowledgments:** The authors would like to acknowledge the support and technical expertise received from the center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, Esbjerg, Denmark, which made this publication possible.

**Conflicts of Interest:** The authors declare no conflict of interest.
