Energies 2015, 8, 1256-1272; doi:10.3390/en8021256 energies
ISSN 1996-1073 www.mdpi.com/journal/energies
Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale
Yuqing Yang 1,2, Weige Zhang 1,2,*, Liyong Niu 1,2 and Jiuchun Jiang 1,2 1 National Active Distribution Network Technology Research Center (NANTEC),
Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China;
E-Mails: firstname.lastname@example.org (Y.Y.); email@example.com (L.N.); firstname.lastname@example.org (J.J.) 2 Collaborative Innovation Center of Electric Vehicles in Beijing, No. 3 Shang Yuan Cun,
Haidian District, Beijing 100044, China * Author to whom correspondence should be addressed; E-Mail: email@example.com;
Tel.: +86-138-0100-6306; Fax: +86-10-5168-3907.
Academic Editor: Paul Stewart
Received: 5 December 2014 / Accepted: 29 January 2015 / Published: 5 February 2015
Abstract: Currently, electric taxis have been deployed in many cities of China. However, the charging unbalance in both temporal and spatial scale has become a rising problem, which leads to low charging efficiency or charging congestion in different stations or time periods. This paper presents a multi-objective coordinated charging strategy for electric taxis in the temporal and spatial scale. That is, the objectives are maximizing the utilization efficiency of charging facilities, minimizing the load unbalance of the regional power system and minimizing the customers’ cost. Besides, the basic configuration of a charging station and operation rules of electric taxis would be the constraints. To tackle this multi-objective optimizing problems, a fuzzy mathematical method has been utilized to transfer the multi-objective optimization to a single optimization issue, and furthermore, the Improved
Particle Swarm Optimization (IPSO) Algorithm has been used to solve the optimization problem. Moreover, simulation cases are carried out, Case 1 is the original charging procedure, and Cases 2 and 3 are the temporal and spatial scale optimized separately, followed with Case 4, the combined coordinated charging. The simulation shows the significant improvement in charging facilities efficiency and users’ benefits, as well as the better dispatching of electric taxis’ charging loads.
Energies 2015, 8 1257
Keywords: electric taxis; temporal scale; spatial scale; particle swarm optimization 1. Introduction
In recent years, Electric Vehicle (EV) technologies have developed rapidly with the high attention from governments all over the world. In China, the government document, Energy Saving and
New Energy Vehicles Industry Development Planning (2011–2020), put forward that the number of EVs should reach 5 million in 2020, and according to the research report from the Ministry of Industry and
Information Technology, the figure is predicted to be 60 million in 2030 . At the same time, with the development of the EV industry, related charging facilities must be built to meet the anticipated significantly increased charging demand. There are also many local plans for the construction of charging stations, chargers and intelligent charging service system, such as in Beijing, Shenzhen.
As pioneers, electric buses and electric taxis are first to be demonstrated for utilization of EVs.
In Shenzhen, an EV charging network has been set up with electric buses, electric taxis, charging stations and related charging services.
In this paper, only electric taxis are considered. After running for a while, some issues have arisen with the operation of electric taxis. According to investigation of operational data from Shenzhen, taxi drivers work two shifts, one for day-time, the other for night. The shift-swapping time and position are not strict, but are usually around 5:00/17:00 and somewhere near the drivers’ places. In terms of the hidden rules that electric taxis should start with full State of Charge (SOC), two charging peaks occur before the work shifts. Moreover, charging twice is not enough to operate for the whole day, and another two charges are needed during the operation periods. All of these facts result in four load peaks in the temporal scale. At the same time, unbalanced distribution in the spatial scale also arises for the reason that most drivers prefer the charging station to be near their places.
If the charging load distribution of EVs is unbalanced in either the temporal or spatial scale, the utilization efficiency of charging facilities will be much lower. It may also trigger some relative load unbalance problems when EV load penetration gets higher, for example, more charging costs for drivers and more feeder losses in regional power systems.
Some researchers have proposed some charging strategies to solve similar electric taxi operation problems. In , with some investigation of EV taxi data from Shenzhen, the authors understood that the status (e.g., operational patterns, driver income and charging behaviors) of EV taxis can provide invaluable information to policy makers and studied the patterns from two aspects: operational behaviors and charging behaviors, but a rescheduling strategy was not proposed. In [3–6], the research group mainly focused on maximizing the profit to reach optimal charging for electric taxis, minimizing their charging cost in face of time-varying electricity prices and some pricing schemes for electric taxis to track the load profile, whose scope is mainly for cost or benefit optimization from a temporal perspective without consideration of the spatial scope. Besides, other work  proposed a facility optimization model to minimize the life circle cost (LCC) of charging/swapping facilities, the time value of electric taxis under the constraints of queuing model and the price spread between oil and electricity. A new dispatching policy also presented in  with consideration of the taxi demand, the remaining power of
Energies 2015, 8 1258 electrical taxis, and the availability of battery charging/switching stations in order to reduce the waiting time for power recharging and thus increase the workable hours for taxi drivers.