Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management

Charging Scheduling of Electric Vehicles with Local Renewable Energy under Uncertain Electric Vehicle Arrival and Grid Power Price

In the paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grid. The uncertainty of the EV arrival, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the charging energy for each EV is random. The goal is to minimize the mean waiting time of EVs under the long term constraint on the cost. We propose queue mapping to convert the EV queue to the charge demand queue and prove the equivalence between the minimization of the two queues' average length. Then we focus on the minimization for the average length of the charge demand queue under long term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this scheduling problem. The system state includes the charge demand queue length, the charge demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally the number of charging demands and the allocated energy from the storage battery compose the two-dimensional policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the two-dimensional policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally we investigate the proposed radical policy and conservative policy numerically

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

Online EV Charge Scheduling Based on Time-of-Use Pricing and Peak Load Minimization: Properties and Efficient Algorithms

Electric vehicles (EVs) endow great potentials for future transportation systems, while efficient charge scheduling strategies are crucial for improving profits and mass adoption of EVs. Two critical and open issues concerning EV charging are how to minimize the total charging cost (Objective 1) and how to minimize the peak load (Objective 2). Although extensive efforts have been made to model EV charging problems, little information is available about model properties and efficient algorithms for dynamic charging problems. This paper aims to fill these gaps. For Objective 1, we demonstrate that the greedy-choice property applies, which means that a globally optimal solution can be achieved by making locally optimal greedy choices, whereas it does not apply to Objective 2. We propose a non-myopic charging strategy accounting for future demands to achieve global optimality for Objective 2. The problem is addressed by a heuristic algorithm combining a multi-commodity network flow model with customized bisection search algorithm in a rolling horizon framework. To expedite the solution efficiency, we derive the upper bound and lower bound in the bisection search based on the relationship between charging volume and parking time. We also explore the impact of demand levels and peak arrival ratios on the system performance. Results show that with prediction, the peak load can converge to a globally optimal solution, and that an optimal look-ahead time exists beyond which any prediction is ineffective. The proposed algorithm outperforms the state-of-the-art algorithms, and is robust to the variations of demand and peak arrival ratios