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.
Highlights
Greenhouse gas emissions, and pollution in general, are affecting negatively cities
A model to represent the charging of electric vehicles at a station with multiple sockets is presented, to define an optimization problem whose solution is compatible with real-time operations
It has been assumed that the main decision variables of the problem, namely the power flows to the vehicles and the those from/to the main grid are kept constant within each time interval between two successive service completion time instants, referring to two different vehicles
Summary
Greenhouse gas emissions, and pollution in general, are affecting negatively cities. Sustainable energy sources and new technologies can help to reconcile the huge energy demand with an acceptable climatic impact. The power grid can be harmfully affected by uncontrolled charging, long charging times, and interruptions (Sbordone et al 2015; Qian et al 2015) New technologies, such as Vehicle-to-Grid (V2G), Smart Charging (SC), and Vehicle to Building (V2B), would allow the vehicles to inject power into the electrical grid and/or modulating power during the charging process. Several approaches that aim at the optimal scheduling of electric vehicles are based on discrete time decision models and result in problems difficult to be solved, especially for the high number of variables. For this reason, heuristics and metaheuristics have been applied as well as decision architectures based on decentralized optimization.
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