A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving

Planificación óptima del intercambio de energía de vehículos eléctricos en estacionamientos considerando incertidumbres

Se espera que los vehículos eléctricos (VE) desempeñen un papel importante en el sistema de transporte debido a los problemas ambientales y las crisis energéticas que hoy en día afectan al planeta. El despliegue de una gran cantidad de vehículos requiere una gestión de demanda adecuada, con el fin de proporcionar menores costos de ampliación e inversión, así como evitar futuras congestiones en la red eléctrica de distribución. Este articulo analiza el intercambio de energía entre los vehículos eléctricos y la red de distribución considerando la capacidad V2G en las estaciones de carga y bajo un esquema de precios según el tiempo de uso (TOU), incentivo para que se carguen y descarguen los VE cuando los precios de la energía eléctrica asociados a la demanda cambien. En este estudio, el agregador de vehículos eléctricos es responsable de proporcionar energía y controlar el patrón de carga de los vehículos eléctricos dentro de los estacionamientos. El modelo propuesto permite determinar los patrones óptimos de carga y descarga del estacionamiento en base a un método que maximiza las ganancias del agregador. Adicionalmente, se aplica el método Monte Carlo para manejar las incertidumbres asociadas al comportamiento de los vehículos durante el día.Electric vehicles (EVs) are expected to play an important role in the transportation system due to environmental problems and energy crises that affect the planet today. The deployment of a large number of vehicles requires adequate demand management, in order to provide lower expansion and investment costs, as well as avoid future congestion in the electrical distribution network. This article analyzes the energy exchange between electric vehicles and the distribution network considering the V2G capacity in the charging stations and under a price scheme according to time of use (TOU), an incentive for EVs to be charged and discharged when electricity prices associated with demand change. In this study, the electric vehicle aggregator is responsible for providing energy and controlling the charging pattern of electric vehicles within parking lots. The proposed model allows determining the optimal parking lot loading and unloading patterns based on a method that maximizes the aggregator's profits. In addition, the Monte Carlo technique is applied to manage the uncertainties associated with the behavior of vehicles during the dayIngeniero EléctricoCuenc

Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

A Smart Parking Lot Management System for Scheduling the Recharging of Electric Vehicles

International audienceIn this paper, we propose a centralized electric vehicles (EVs) recharge scheduling system for parking lots using a realistic vehicular mobility/parking pattern focusing on individual parking lots. We consider two different types of EV based on their mobility/parking patterns; regular EVs and irregular EVs. An extensive trace-based vehicular mobility model collected from the Canton of Zurich is used for the regular EVs and a probabilistic pattern built on top of this trace is used for modeling the behavior of irregular EVs. To the extend of our knowledge, this is the first EV charging scheduling study in the literature that takes into account a realistic vehicular mobility pattern focusing on individual parking lots. We compare the performance of our proposed system with two well-known basic scheduling mechanisms, First Come First Serve and Earliest Deadline First with regard to two objective functions: maximizing the total parking lot revenue and maximizing the total number of EVs fulfilling their requirements. Comparison results show that our proposed system outperforms well-known basic scheduling mechanisms with regards to both objectives. Parking lots managing the recharging of a high number of EVs will greatly benefit from using such recharge scheduling systems in the context of Smart Cities

Development of the Plug-in Electric Vehicle Charging Infrastructure via Smart-Charging Algorithms

Electricity generation and the transportation sector make up a large portion of greenhouse gas emissions in the United States. Meeting ambitious reductions in greenhouse gasses requires large scale adoption of plug-in electric vehicles (PEVs) and has led to several policies and laws aimed at incentivizing PEV sales. An inadequate charging infrastructure, however, could be a major obstacle for a large-scale adoption of PEVs. Large electrical demands from PEVs could negatively affect circuitry, increase electricity costs, and exacerbate stress to local electrical components during times of high electricity usage. These issues, however, can be addressed by deploying smart-charging strategies.This work is focused on the development of smart-charging protocols for workplace battery electric vehicle (BEV) charging. Three comprehensive smart-charging protocols with different applications are proposed. Each protocol is developed with varying degrees of focus on communication requirements and privacy concerns. The BEV-based Optimization Protocol is a decentralized, non-iterative strategy that allows BEVs to individually schedule their charging schedules. The Octopus Charger-based MILP Protocol allows octopus chargers (i.e., charging stations with multiple cables) to independently schedule charging for their assigned BEVs. The Real-Time Octopus Charger-based MILP Protocol allows octopus chargers to schedule BEV charging in real time, without prior information from BEVs. By using the appropriate cost signal and assignment algorithms, the proposed protocols can manage a parking structure demand load while reducing the number of installed charging stations. Driving patterns from the National Household Travel Survey were used to perform simulations, to verify and quantify the effectiveness of each protocol. The proposed protocols resulted in improved peak load reductions for all simulated smart-charging scenarios, when compared with uncontrolled charging. By using octopus chargers, all protocols were able to reduce the number of charging stations needed at parking structures, while meeting the charging requests of all BEVs. Time-Of-Use rate plans from Southern California Edison were used to estimate monthly electricity costs for the simulated parking structures. The smart-charging protocols resulted in reduced electricity costs for most cases studied, when compared to uncontrolled charging