Python-PowerFactory co-simulation for the optimal location of electric vehicle charging stations. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Cadena Zarate, Cristian David; Caballero, Juan; Rojas Perez, Hugo Andres; Solano, Javier; Quiroga, Oscar Arnulfo
• Is Part Of: International journal of ambient energy. Volume 43:Issue 1(2022)
• Page Start: 7541
• Page End: 7547
• Publication Date: 2022-12-31
• Subjects: Genetic Algorithms -- DIgSILENT PowerFactory -- electric vehicle charging stations -- Python -- co-simulation; Power resources -- Periodicals; Renewable energy sources -- Periodicals; 621.04205
• DOI: 10.1080/01430750.2022.2068060
• Languages: English
• ISSNs: 0143-0750

Abstract

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.