PLANNING OF CHARGING INFRASTRUCTURE FOR ELECTRIC-DRIVE ROAD VEHICLESDAVIDOV, SRETEN (Avtor) Pantoš, Miloš (Mentor) charging infrastructurecharging reliabilitycharging stations location optimisationelectric vehiclesquality of servicestochastic scenarios.To deal with the massive deployment of electric vehicles, charging stations must be properly placed. This is an extremely important issue that must be resolved before many electric vehicles are manufactured and governmental authorities start adopting policies to initiate higher electromobility. A poor design of the charging infrastructure can cost a significant amount of resources and can disrupt the electric vehicle users’ convenience, can offer poor quality of service and other user dissatisfaction. A public charging location is defined as location in a road network, where any electric vehicle user can come to charge his battery. Due to various charging technologies, the charging service can be fulfilled by shorter or longer charging time depending on the power transfer capacity and the disposable charging time of the users. The charging station must be within the reachable driving range distance of the electric vehicles and must provide a charging service for majority of users at a lower cost and higher quality. In this dissertation, we present a new optimisation procedure for charging stations placement. The introduced methodology takes into account the electric vehicle users, the electric and road networks. The electric power system reliability check, quality of service and charging reliability of the charging infrastructure are used as optimisation criteria, while placing charging stations of different charging technology by minimal investment costs. Electric power system reliability check is incorporated in the optimisation constraints by using a DC model to calculate the power flows. In this part, the charging reliability criterion of the charging infrastructure is defined as selecting at least one candidate location within the driving range of the electric vehicles in order to ensure unlimited mobility. Another criterion is the quality of service required by the electric vehicle users, which considers the time the users are willing to spend for charging their battery, when traveling, to complete the trip. To please the requirements of the users regarding the quality of service of the charging infrastructure, different charging technology types are factored in the optimisation objective function. The optimisation model also includes the mobility behaviour of electric vehicles by involving their trajectories of movement at different time instances. By also analysing their mobility behaviour, the traffic load of the candidate locations is identified which exposes the number of electric vehicles that are going through a particular candidate location. The final optimal charging infrastructure expansion plan shows the optimal placement layout, number of locations and placement cost. We also elaborate a stochastic formulation of the optimisation placement procedure that takes into consideration the stochastic occurrences that can have a significant impact on the electric vehicles’ driving range i.e. battery charge, the charging time that the users are willing to spend while charging at candidate location and the charging stations’ investment costs. The stochastic formulation includes also a representative trajectories search and a scenario reduction method to form common stochastic scenarios to be executed by the proposed optimisation model. Besides the charging infrastructure placement plan, the optimal results of the stochastic scenarios can be used to calculate the placement probability of candidate locations, which is fundamental for the charging infrastructure planners in the decision-making part. The numeric results illustrate the application of the proposed charging infrastructure optimization on test road and electric power system by showing the optimal charging stations placement layout and overall placement costs for the optimization constraints set on the charging reliability, required quality of service and running a power system reliability check. Additionally, for the stochastic formulation of the optimization model, the results show the optimal charging locations and their placement probability, which exposes their importance to charging infrastructure planners in terms of prioritization and robust decision-making. For the detailed analysis made on the impact of the stochastic driving range scenarios on the optimization output regarding the placement cost and locations, it is ascertained that a shorter uncertainty distance increases the number of candidate locations included in the charging reliability criterion resulting in higher overall charging infrastructure placement costs and vice-versa.20182018-05-14 13:50:07Doktorsko delo/naloga101213VisID: 41961COBISS_ID: 295028992sl