A probabilistic capacity planning methodology for plug-in electric vehicle charging lots with on-site energy storage systems. (December 2020) - Bayram, I. Safak; Galloway, Stuart; Burt, Graeme
Highlights: Operation of charging station with an on-site storage is modelled with an MMPP. Outage probability is used as the main performance metric. Station and storage capacity are calculated by a Matrix-geometric algorithm. Investigate the relationship between PEV statistics, storage size, and grid power. Case studies show peak demand and electricity bills can be reduced drastically. Abstract: Plug-in electric vehicles (PEV) have gained popularity to support environmental sustainability and reach net-zero emission goals. However, accommodating large numbers of PEVs is a complex problem as concurrent PEV demand significantly increases peak demand and stresses supporting network elements. In this paper, we present a large-scale PEV charging lot equipped with an on-site storage. Power drawn from the grid is utilized to meet customer demand and charge the storage unit which, in return, is employed to lower peak load and demand charges. By considering the probabilistic nature of the customer demand, the proposed architecture is modelled by a Markov-modulated Poisson Process and a matrix-geometric based algorithm is developed to solve the associated capacity planning problem. Station outage probability (defined as the probability of not serving PEV demand) is used as the main metric to size station resources. Case studies show that by accounting for the statistical variations in customer demand, the power required for the station is significantly less than the sum of chargers' rated power. In addition, on-site storage can considerably reduce the stress on the supporting grid components and lower stations' running cost.