هدایت شارژ سریع وسایل نقلیه الکتریکی بر اساس داده های ترافیکی و سیستم های توان بی درنگ Rapid-Charging Navigation of Electric Vehicles Based on Real-Time Power Systems and Traffic Data

Abstract Electric vehicles (EVs) have attracted growing attention in recent years. However, daily charging of EVs, in particular rapid charging, may impact power systems, especially during peak hours, and the effects may occur in different places as traffic conditions change. To address these issues, we describe an integrated rapid-charging navigation strategy that considers both the traffic conditions and the status of the power grid. The system is based on an intelligent transport system (ITS), and contains four modules: a power system control center (PSCC), an ITS center, charging stations, and EV terminals. The PSCC calculates the available charging capacity and station charging capacity based on power grid data and transmits the results to the charging stations. The charging stations determine their charging plans and estimate the available charging power for future EVs (CPFE), and transmit these data to the ITS center. After receiving CPFE data and traffic data from the ITS center, the EV terminal estimates the total time for charging (TTC) for different stations, which includes the driving time, waiting time, and charging time. The driver can view these results and choose to be navigated to the charging station corresponding to the minimum TTC. The modular design of the navigation system reduces data transmission, which protects the drivers’ privacy since they can choose which charging station to use and are not required to send any data to the ITS system. Simulation results demonstrate the feasibility of the proposed method for different working conditions for power system and traffic conditions. I. INTRODUCTION WITH growing concern about the sustainability of energy resources and climate change, there has been much recent interest in electric vehicles (EVs) [1], [2]. EVs are zero-emission during driving and can be more energy-efficient than conventional vehicles with combustion engines. Besides, through proper regulation, EV charging loads could be utilized to help integrate renewable but intermittent energy sources for further carbon emission reduction as well [3]–[۵]. Though the EV have not been widely used now, it is expected to become an integral part of the traffic. In the USA, the Obama administration has embraced a goal of having one million electric-powered vehicles by 2015 [6]. Recently, a number of car manufactures, including Nissan and Toyota, have already developed commercially available electric vehicles [7]–[۹]. The necessary infrastructure, including the charging stations and charging poles, is currently being expanded in a number of countries across the world. However, as the number of EVs grows, the heavy and unpredictable loads due to charging may cause problems for the power system, such as thermal overloads and under-voltages [10]–[۱۲]. In order to mitigate these effects, studies have been carried out focusing on optimized charging methods, including controlling the charging duration and rate [13]–[۱۶], as well as using dynamic pricing to manage the time distribution of the load [17]–[۲۱]. In most of these studies, the goals have been to shift the load or to ensure the reliability of large-scale power systems. By assuming that the charging power can be fully adjusted, and the charging rate can be slowed down, these optimization methods have mainly been applied to residential charging, especially during the night when large numbers of EVs are plugged in. The charging facility can be viewed as an ordinary residential electric device and it can be connected directly to low voltage distribution system. Besides slow charging method, rapid charging method should also be considered. Slow charging method takes 6–۸ hours, mostly for long-term parked EVs, while the rapid charging method supplies higher power, which can reach 50–۱۰۰ kW [22], so that charging occurs over 15 minutes to 2 hours. However, it requires specific charging facilities and mostly takes place in commercial charging stations [23]. Because of the shorter charging time, some EV drivers may choose this method to continue their driving soon. It is especially applicable to those drivers who consume large amounts of energy and have a long-distance trip, such as taxi and shuttle drivers. Obviously, different from wide-spreading and low-power residential charging, rapid charging loads in charging stations are much more concentrated and heavy. Because of its high power level, rapid charging stations should be connected to medium voltage distribution system in three-phase rather than to low voltage distribution system in single-phase [22], [24]. In China, most rapid charging stations are connected to 10-kV feeder in three-phase [25]. Therefore, analyzing impact of rapid charging load should also take power system structure into consideration rather than a simple load curve [23]–[۲۷]. Besides, as rapid charging station operation is affected by traffic a lot, both planning [28], [29] and load analyzing [30]–[۳۲] of rapid charging station should consider traffic factor. In [32], a mathematical model of the EV charging demand for a rapid-charging station was reported and used to analyze the expected charging loads at different exits of a highway. This model used fluid dynamics to describe the arrival rate of EVs to aid the forecasting of demand and construction planning of charging stations. When operating a rapid-charging station, it is necessary not only to analyze the additional demand due to EV charging, but also to develop a strategy to mitigate the impact on the grid and maximize the available rapid-charging power during operation. In contrast to the adjustable slow-charging method, rapid charging offers high power immediately following the start of charging, so that the controlling strategies for slow-charging method cannot employ temporal optimization. In this case, with considering EV drivers’ subjectivities, spatial optimization may be a more valuable approach. There are two aspects to the optimization of EV charging considered here. First, the driver should reach the charging station as soon as possible; second, the load should have minimal impact on the operation of the power grid. Now, GPS-based navigation systems are installed in many vehicles, which are employed to navigate the vehicle to a certain destination and can be utilized for EV charging navigation. Some applications are even able to account for real-time traffic information to select routes that avoid traffic congestion, such as Baidu Map [33], which also has a phone version, and it offers application programming interface (API) for software developing [34]. However, such navigation cannot consider the power system information or constraints. Sometimes, distribution system may be partially overloaded because of unexpectable reasons, such as residential air conditioning load in an extremely hot day, or sudden change of distributed generation’s (DG’s) power output. Under these scenarios, if too many EVs are guided to a charging station and the feeder this charging station connected to has already been overloaded, this local overload may become more serious. Besides, compared with other power loads, spatial distribution of EV rapid charging load is easier and more applicable to adjust to help alleviate local overload of the power system, thus ensure safety of the power system. Therefore, it could be beneficial to consider the spatial distribution of the load due to EV charging when providing route information to drivers. A navigation strategy considering power grid operation that revises the traffic distance to an electrical distance has been reported previously [35], where the basic architecture of the charging and navigation system was introduced. However, it is difficult to transform the power system information into a distance term, and the system might not be exploited by EV drivers, who may choose to disregard the proposed routes because the electrical distance makes little sense to them. Here, we report a follow-up study, where we aim to build a more integrated navigation system and put forward a more practical and efficient navigation strategy. There are two major challenges that must be addressed to implement such an integrated navigation system. The first is finding a suitable method of transforming the demand on the operation of the power grid into the navigation of EVs. In this paper, traffic and power grid information are unified into a “time” term, and the drivers are expectedto choosethe optionthatistheleasttime consuming, where this time includes the driving time, waiting time, and chargingtime. The second challenge is reducingtheinformation exchange as well as protecting the drivers’ privacy during navigation. To solve these problems, an integrated architecture was designed in a modular fashion. Each module performs calculations locally, reducing the data exchange required between modules compared with a centralized calculation method. The EV terminal can completethe calculations and determine the best route based on broadcast data about the status of the power grid and the traffic system. No data about the EV will be sent to the server, which ensures the privacy of the driver. The remainder of this paper is organized as follows. Section II introduces the intelligent transport system (ITS) as well as the architecture of the integrated charging guide system. Section III describes the functions and strategies of each system module as well as the data transmission between modules, and presents a minimum total time for charging (TTC) strategy, then discusses how this charging navigation method helps reduce information transmission and protect privacy. Section IV presents a simulation example to assess the performance of the navigation strategy. Section V concludes with a summary of the study findings. II. ARCHITECTURE To effectively apply a navigation strategy that considers the location of charging stations, a comprehensive system that combines the power grid data and traffic data is required. The power grid data may be obtained from a network control center, and the traffic data may be obtained from an intelligent transport system (ITS). A. Brief Introduction of ITS An ITS is a transport management system where the goal is to reduce traffic congestion by optimizing the routing of vehicles. It integrates advanced information, data communication, electronic sensor, and electronic control technologies [36]–[۴۰]. Now many countries are actively developing this technology, including the USA and China [41], [42]. There are many applications of ITS technologies, of which the most widely-used one is vehicle navigation system. In order to implement location and navigation, vehicle navigation system includes geographic information systems (GIS), global positioning system (GPS), image monitoring, and wireless communication technology [43], [44]. Besides, vehicle navigation system is able to obtain real-time traffic data with other assistive tools, such as Traffic Message Channel (TMC) [45] and Vehicle Information and Communication System (VICS) [46]. These tools allow delivery of dynamic traffic information to vehicle terminals through conventional FM radio broadcasts without interrupting normal broadcast services [47]. The real-time traffic data could be utilized to improve navigation by avoid traffic congestion [48].