Python-PowerFactory co-simulation for the optimal location of electric vehicle charging stations

Abstract

This paper presents a genetic algorithm-based methodology for optimal placement of Electric Vehicles Charging Stations using co-simulation between Python and DigSILENT PowerFactory. The medium voltage power system is modelled in DigSILENT PowerFactory, a powerful software widely used by electric network operators. On the other hand, the genetic algorithm is implemented in Python, one of the most used software in engineering. The objective function considers the cost of EVCS power losses and their construction costs, and it is solved using a genetic algorithm. A method to communicate the two software is proposed. The methodology presented is evaluated using a 33-node power network for different numbers of EVCS location in the medium voltage grid. Results show that the Python-PowerFactory co-simulation is extremely useful when analysing multiple cases of location of EVCS on the network, which could help Network Operators analyse the impact of including EVCS to the network.

KEYWORDS:

• Genetic Algorithms
• DIgSILENT PowerFactory
• electric vehicle charging stations
• Python
• co-simulation

Acknowledgements

The authors thank the financial support given by the project 2435, entitled ‘Integración óptima de estaciones de recarga de vehículos eléctricos en el sistema de distribución local’, by the Vicerrectoría de Investigación y Extensión of the Universidad Industrial de Santander.

Disclosure statement

No potential conflict of interest was reported by the author(s).