Discrete event optimization of a vehicle charging station with multiple sockets

Abstract

The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management

“…Zhaohao et al [17] select the charging pricing as a tool for optimally coordinating the aggregator and EV charging stations and demonstrate that the total cost can be greatly reduced by applying the suggested model. Other approaches, such as in [18,19], are related to a discrete-event formalization, which offers the advantage of reducing the number of decision variables, though losing some details in the time domain representation. However, both discrete-time and discrete-event approaches offer the possibility to improve local energy utilization, reduce the need for investments in stationary batteries, provide demand response services and integrate EVs in the smart grid [20], and drastically reduce emissions when the system model couples EVs and renewables [21,22].…”