Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation

As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bi-directional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation. . The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease

Recarga de vehículos eléctricos mediante una optimización entera mixta con participación de respuesta de la demanda

The micro networks of commercial buildings will play an important role for an intelligent city, since having electric vehicle charging (EV) activities can cause performance degradation and overloads in the distribution system.  It is proposed to minimize the costs in the consumption of electric energy of a commercial type building when the recharging of electric vehicles is carried out, it will be carried out by means of installation of photovoltaic panels for which in the study the opening hours of the building will be taken fulfilling the need of the demand, for the optimization process it will be done through a dispatch through allocation of resources.  An integral result obtained from the simulation tests showed that the proposed strategy has satisfactory results and high efficiency.Las micro redes de edificios comerciales jugarán un papel importante para una ciudad inteligente, al tener actividades de carga de vehículos eléctricos (EV) puede causar degradaciones del rendimiento y sobrecargas en el sistema de distribución. Se propone minimizar los costos en el consumo de energía eléctrica de un edificio tipo comercial cuando se realiza la recarga de vehículos eléctricos, se lo realizará mediante instalación de paneles fotovoltaicos para lo cual en el estudio se tomará el horario de atención del edificio cumpliendo la necesidad de la demanda, para el proceso de optimización se hará por medio de un despacho mediante asignación de recursos. Un resultado integral obtenido de las pruebas de simulación se demostró que la estrategia propuesta tiene resultados satisfactorios y alta eficiencia

Smart PEV Charging Station Operation and Design Considering Distribution System Impact

Penetration of plug-in electric vehicles (PEVs) into the market is expected to be large in the near future. Also, as stated by the Ontario Ministry of Transportation, the province is investing $20 million from Ontario's Green Investment Fund to build nearly 500 electric vehicle charging stations (EVCSs) at over 250 locations in Ontario by 2017. Therefore, estimating PEV charging demand at an EVCS with their complex charging behavior, their impact on the power grid, and the optimal design of EVCS need be investigated. This thesis first presents a queuing analysis based method for modeling the 24-hour charging load profile of EVCSs. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day; considering customer convenience and charging price as the factors that influence the hourly arrival rate of vehicles at the EVCS. One of the main contributions of the thesis is to model the PEV service time considering the state-of-charge of the battery and the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined using the stochastic optimal power flow and Model Predictive Control approaches. The thesis presents a novel mathematical model for representing the total charging load at an EVCS in terms of controllable parameters; the load model developed using a queuing model followed by a neural network (NN). The queuing model constructs a data set of PEV charging parameters which are input to the NN to determine the controllable EVCS load model. The smart EVCS load is a function of the number of PEVs charging simultaneously, total charging current, arrival rate, and time; and various class of PEVs. The EVCS load is integrated within a distribution operations framework to determine the optimal operation and smart charging schedules of the EVCS. Objective functions from the perspective of the local distribution company (LDC) and EVCS owner are considered for studies. The performance of a smart EVCS vis-à-vis an uncontrolled EVCS is examined to emphasize the demand response (DR) contributions of a smart EVCS and its integration into distribution operations. Finally, the thesis presents the optimal design of an EVCS with the goal of minimizing the life-cycle cost, while taking into account environmental emissions. Different supply options such as renewable energy technology based and diesel generation, with realistic inputs on their physical, operating and economic characteristics are considered, in order to arrive at the optimal design of EVCS. The charging demand of the EVCS is estimated considering real drive data. Analysis is also carried out to compare the economics of a grid-connected EVCS with an isolated EVCS and the optimal break-even distance is determined. Also, the EVCS is assumed to be connected to the grid as a smart energy hub based on different supply options