Intégration des véhicules électriques dans le réseau électrique résidentiel : impact sur le déséquilibre et stratégies V2G innovantes

The study of the Vehicle to Grid (V2G) interactions is the main contribution of this research work. To charge an electric vehicle (EV) battery the overloading in low voltage (LV) residential networks is expected to be between 2 kW and maximum 10kW. To avoid power quality deterioration a battery recharge management is a priority for the charging infrastructure business. Our work has been, first, to study the impact of a significant number of EVs in recharge mode on the voltage and current unbalances in a LV residential electric network scenario and second to develop charging strategies to minimize those unbalances.First, we defined why it is important for the LV residential network to minimize the unbalances both in current and in voltage. Then, we studied the impact of different market penetration rates of the EV on the unbalances by estimating the sensibility of the statistical parameters describing them. Finally we developed several charging/discharging strategies in order to minimize the current unbalance by using optimization algorithms in the continuous and discrete domains. Several constraints were formulated in order to preserve power limits and an enough state of charge for the mobility.Ces travaux de recherche constituent une contribution à l'étude des interactions entre le réseau électrique et le véhicule électrique (VE) en mode de recharge (Vehicle-to-Grid V2G). La recharge des VEs engendrant des surconsommations variant entre deux et plusieurs dizaines de kilowatts, occasionne des perturbations sur la qualité de l'énergie du réseau auquel ils sont connectés ; la gestion de l'énergie délivrée au VE est donc une priorité pour les différents acteurs industriels qui ont établi les infrastructures de recharge. Dans cette thèse nous proposons d'étudier l'impact des nombreux VEs en mode de recharge sur le déséquilibre en courant et en tension du réseau de distribution basse tension ainsi que sur les stratégies de recharge à mettre en œuvre pour améliorer la qualité de l'énergie, et notamment minimiser les taux de déséquilibre. Nous commençons par définir le besoin de réduire le déséquilibre en courant et en tension dans le réseau résidentiel de basse tension. Ensuite, nous étudions l'impact du taux d'insertion des VEs sur ces déséquilibres en estimant la sensibilité des paramètres statistiques les décrivant. Enfin, nous proposons des stratégies de gestion de la recharge et de la décharge cherchant à minimiser les déséquilibres occasionnés tout en respectant les contraintes de confort, c'est-à-dire de la recharge du VE avant le départ et les limites structurelles du système

Machine learning approach for electric vehicle availability forecast to provide vehicle-to-home services

In this study, we propose a machine learning (ML) model to predict the availability of an electric vehicle (EV) providing vehicle to home (V2H) services. Electric vehicles are able to store and give back energy directly to consumers and/or the grid using V2H and/or vehicle to grid (V2G) technologies. However, there is a limited understanding of what impact vehicle availability has on the its capacity to engage in such services. Using five different vehicle usage profiles, classified by the number of trips made per week, the machine learning model proposed is used to predict the availability of an EV. An optimisation model is then used on each profile to obtain the minimum electricity bill for each profile class assuming V2H service provision. PV generation providing power to the house was also considered. The ML model had an accuracy of over 85% and R2 value of 0.78 in predicting the location and distance travelled for the EV respectively. Final results showed that the less an EV is used for travelling, the greater its availability to participate in V2H services. Also, all categories of EV user benefited from reduced power bills when deploying V2H. An electricity cost reduction of at least 46% on average was obtained when V2H is implemented with an agile electricity price structure regardless of the level of vehicle usage

System Component Modelling of Electric Vehicles and Charging Infrastructure

The objective of this research is to develop a model for the electrical components that are involved in charging and discharging of an electric vehicle (EV). This will enable testing differ-ent energy management strategies that improve energy efficiency, battery lifetime, and ener-gy availability. Furthermore, the model will enable the investigation of vehicle to grid (V2G), thermal preconditioning of vehicles, and an economic analysis and optimization. In order to achieve the above goals, the effects that determine the performance of the infra-structure, rectifier, and battery are investigated and included as a second step in a parameter-ized model. Implementing an open loop control enables sample times of one minute and, by simplifying the process for a low runtime, multiple EVs can be included in the simulation of a smart grid. The structure is designed in a way as to support both uncontrolled and controlled charging with variable charging power and variable charging current. For the battery, a simple model approach was developed that limits the computational com-plexity and the effort for parameterization. It was found that the energy management of an electric vehicle is a complex process with battery handling being a key issue. The performance is dependent on various parameters that involve battery temperature, depth of discharge, and charge and discharge rates

Modelling of System Components for Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) Applications with Plug-in Hybrid Electric Vehicles (PHEVs)

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