Coordinated charging strategy for a network of photovoltaic charging stations (PVCSs) : a trade-off between stations operator and electric vehicles (EVs) users

La movilidad eléctrica se ha posicionado con fuerza en los últimos años como una de las tendencias de transporte. Esta tecnología se ha consolidado como una alternativa prometedora para dar respuesta al impacto ambiental causado por el sistema de transporte actual. Por lo tanto, se han realizado esfuerzos para incentivar e impulsar el uso de vehículos eléctricos, dando como resultado un rápido crecimiento de este mercado. A pesar de las ventajas que puede brindar la movilidad eléctrica, aun se debe hacer frente a ciertos retos que trae una implementación masiva de esta tecnología. Entre estos retos se encuentran el tener una infraestructura de carga y un suministro energético adecuado para abastecer la recarga de los vehículos. Para incentivar la adopción de esta tecnología, es necesario analizar alternativas para la recarga y la gestión inteligente de estas, por lo cual, se han desarrollado esquemas y estrategias de carga coordinada que permiten controlar la operación de los puntos de carga. Además, el crecimiento del mercado de los vehículos eléctricos conlleva a una mayor demanda energética sobre la red de distribución, por lo cual, la integración de fuentes energéticas alternativas, como la energía fotovoltaica, en la infraestructura de carga ha surgido como solución para satisfacer, de manera limpia y equilibrada, la necesidad de complementar la energía disponible para la carga de estos vehículos

New Perspectives on Modelling and Control for Next Generation Intelligent Transport Systems

This PhD thesis contains 3 major application areas all within an Intelligent Transportation System context. The first problem we discuss considers models that make beneficial use of the large amounts of data generated in the context of traffic systems. We use a Markov chain model to do this, where important data can be taken into account in an aggregate form. The Markovian model is simple and allows for fast computation, even on low end computers, while at the same time allowing meaningful insight into a variety of traffic system related issues. This allows us to both model and enable the control of aggregate, macroscopic features of traffic networks. We then discuss three application areas for this model: the modelling of congestion, emissions, and the dissipation of energy in electric vehicles. The second problem we discuss is the control of pollution emissions in eets of hybrid vehicles. We consider parallel hybrids that have two power units, an internal combustion engine and an electric motor. We propose a scheme in which we can in uence the mix of the two engines in each car based on simple broadcast signals from a central infrastructure. The infrastructure monitors pollution levels and can thus make the vehicles react to its changes. This leads to a context aware system that can be used to avoid pollution peaks, yet does not restrict drivers unnecessarily. In this context we also discuss technical constraints that have to be taken into account in the design of traffic control algorithms that are of a microscopic nature, i.e. they affect the operation of individual vehicles. We also investigate ideas on decentralised trading of emissions. The goal here is to allocate the rights to pollute fairly among the eet's vehicles. Lastly we discuss the usage of decentralised stochastic assignment strategies in traffic applications. Systems are considered in which reservation schemes can not reliably be provided or enforced and there is a signifficant delay between decisions and their effect. In particular, our approach facilitates taking into account the feedback induced into traffic systems by providing forecasts to large groups of users. This feedback can invalidate the predictions if not modelled carefully. At the same time our proposed strategies are simple rules that are easy to follow, easy to accept, and significantly improve the performance of the systems under study. We apply this approach to three application areas, the assignment of electric vehicles to charging stations, the assignment of vehicles to parking facilities, and the assignment of customers to bike sharing stations. All discussed approaches are analysed using mathematical tools and validated through extensive simulations