Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system. Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services. Although it is generally infeasible for EVs to dictate their routes, we can design AV travel plans to fulfill certain system-wide objectives. In this paper, we focus on the AVs looking for parking and study how they can be led to appropriate parking facilities to support V2G services. We formulate the coordinated parking problem (CPP), which can be solved by a standard integer linear program solver but requires long computational time. To make it more practical, we develop a distributed algorithm to address CPP based on dual decomposition. We carry out a series of simulations to evaluate the proposed solution methods. Our results show that the distributed algorithm can produce nearly optimal solutions with substantially less computational time. A coarser time scale can improve computational time but degrade the solution quality resulting in possible infeasible solution. Even with communication loss, the distributed algorithm can still perform well and converge with only little degradation in speed.
There is some work studying intelligent parking in general. @cite_6 investigated availability of parking facilities for parking guidance and information systems. It developed a multivariate autoregressive model to account for the temporal and spatial relationship of parking availability. @cite_17 studied the uncoordinated parking space allocation for inexpensive limited on-street parking spots and expensive oversized parking lots. Some work focuses on AV parking. @cite_19 developed a control system for AV valet parking with a focus on steering control. @cite_39 designed an intelligent vehicle system to implement the AV valet parking service. However, they mainly targeted AV parking control in a confined parking area. Some investigate the parking issue for a larger area. @cite_34 proposed intelligent parking assistant architecture to manage parking spots to improve the quality of urban mobility. @cite_35 analyzed the impact of charging and discharging of EVs in parking lots on the power grid probabilistically. However, there is no thorough study on V2G based on AVs. In this work, we aim to bridge this research gap.
@cite_35 @cite_6 @cite_39 @cite_19 @cite_34 @cite_17
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In almost every major city in the U.S. and internationally, parking problems are ubiquitous. It is well known that the limited availability of parking contributes to roadway congestion, air pollution, and driver frustration and that the cost of expanding traditional parking capacity is frequently prohibitive. However, less research has addressed the effect of insufficient parking at transit stations on transit use. In the San Francisco Bay Area, parking has recently been at or near capacity at many of the 31 Bay Area Rapid Transit (BART) District stations with parking facilities. Smart parking management technologies may provide a cost-effective tool to address near-term parking constraints at BART transit stations. This report presents early findings from an application of advanced parking technologies to maximize existing parking capacity at the Rockridge BART station, which was launched in December 2004 in the East San Francisco Bay Area. The smart parking system includes traffic sensors that count the number of vehicles entering and exiting the parking lots at the station. A reservation system allows travelers to reserve spaces by Internet, personal digital assistant (PDA), phone, and cell phone. The real-time information obtained from the sensors and the reservation system is displayed on variable message signs (VMS) (on Highway 24 leading to the station) to alert drivers of parking space availability. Before and after surveys and focus groups will be used to evaluate the travel effects, economic potential, and system technology of the field test. This report consists of three major sections: * A literature review in which the effectiveness of different types and applications of smart parking management systems are evaluated; * A feasibility analysis, including focus groups, surveys, and observational analyses, which guides the development and initial evaluation of the smart parking field test; and * A smart parking project description, which includes the applied demonstration design and technology. An automatic Vehicle-to-Grid (V2G) technology can contribute to the utility grid. V2G technology has drawn great interest in the recent years. Success of the sophisticated automatic V2G research depends on efficient scheduling of gridable vehicles in constrained parking lots. Parking lots have constraints of space and current limits for V2G. However, V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO optimizes the on off states of power generating units easily. Vehicles are presented by signed integer number instead of 0 1 to reduce the dimension of the problem. Typical discrete version of PSO has less balance between local and global searching abilities to optimize the number of charging discharging gridable vehicles in the constrained system. In the same model, balanced PSO is proposed to optimize the V2G part in the constrained parking lots. Finally, results show a considerable amount of profit for using proper scheduling of gridable vehicles in constrained parking lots. This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle-to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end. Vehicle-to-grid (V2G) units are gaining prominence and may dominate the auto-market in the near future. The V2G batteries require corporate parking lots for charging and discharging operations. The electric power capacity of an existing parking lot can be increased by the installation of photovoltaic (PV) rooftops. This paper describes mathematical models for estimating the electric power capacity of a V2G parking lot (VPL) system with PV canopy. The electric vehicle (EV) demand supply model was formulated as a queuing theory problem, exhibiting stochastic characteristics. New formulae were developed to address the impacts of battery charger efficiency on the amount of power demand during battery charging, and also how the latter is effected by inverter efficiency during discharging. Mathematical models for grid gain factor were developed. The proposed models were tested using Tesla Roadster EV and Nissan leaf EV. Promising simulation results are gained leading to a conclusion that vehicle parking lots with PV facilities can potentially relieve and enhance the grid capacity. Results show that 60 gain factor is possible. The effect of weather uncertainties and energy market price were studied. The study could be useful in battery-charger control studies.