A comprehensive planning framework for electric vehicles fast charging station assisted by solar and battery based on Queueing theory and non-dominated sorting genetic algorithm-II in a co-ordinated transportation and power network. (May 2022) - Kumar, Nikhil; Kumar, Tushar; Nema, Savita; Thakur, Tripta
Highlights: For various battery-powered EVs, an adaptive technique is developed to compute the fewest number of charging stations required to serve the greatest number of EV requests in any transit system with a service range limitation. The service radius of a charging infrastructure is estimated for EVs with varying battery capacity and trip ranges. To minimise the net charging demand of EVs from the distribution network when arriving at a specified EVCS, the shortest path in the transportation network is adopted. Two-stage stochastic mixed integer non-linear programming ( M I N L P ) framework is formulated and solved by NSGA-II and fuzzy satisfaction approach for jointly locating and sizing the E V C S and solar P V plant, taking into account seasonal variations for the P V generation and weekly patterns for E V traffic flow. A modified queuing model M1/M2/N for EVs with different battery capacity in conjunction with the gravity interaction approach to model the spatial temporal based charging demand at each charging station in the transportation network under wait time constraints during each hour of the day. Abstract: The success of the electric vehicles ( EVs ) sector hinges on the deployment of fast charging electric vehicle charging station (EVCS) . The inclusion of clean energy into EV charging stations poses both risks and opportunities. A viable and adequate capacity setup with appropriate planning of EVCS is favourable and crucial. This paper proposes a two-stage sustainable framework for joint allocation of fast charging EVCS, solar photo voltaic (PV) and battery energy storage system (BESS) with dynamic charging and discharging under coupled distribution and transportation network. In the first stage, modified Queuing theory (M1/M2/N) is used in conjunction with the gravity interaction approach to model the stochastic charging demand of EVs with multiple batteries at each charging station, taking into account the spatial –temporal distribution of EVs and wait time limit. Further, non-dominated sorting genetic algorithm ( NSGA-II) and fuzzy satisfaction-based hybrid optimization are used to optimise the location and sizing of PV integrated EVCS across multiple objectives such as power loss, voltage deviation, served EV flow, and investment, as well as the operation and maintenance costs of the EVCS and PV system and then estimate the relevant factor such as; number of charging ports, sizing of solar PV system at EVCS and EV served flow. In the second stage, size of the BESS and the additional PV capacity needed to charge the BESS at each EVCS is computed using the Bi-section method while considering solar irradiance of all seasons (summer, spring rainy, winter). The proposed scheme is validated on an IEEE 123 bus unbalanced distribution system coupled to a 25-node transportation network under a variety of seasonal scenarios over a planning year. Numerical results reveal the multiple benefits of proposed framework such as reduction in active power losses, power drawn from system and voltage deviation at point of common coupling (PCC) that may occur due to the increased EVs charging demand.