A scalable dynamic parking allocation framework

International audienceCities suffer from high traffic c ongestion of which one of the main causes is the unorganized pursuit for available parking. Apart from traffic congestion, the blind search for a parking slot causes financial and environmental losses. We consider a general parking allocation scenario in which the GPS data of a set of vehicles, such as the current locations and destinations of the vehicles, are available to a central agency which will guide the vehicles toward a designated parking lot, instead of the entered destination. In its natural form, the parking allocation problem is dynamic, i.e., its input is continuously updated. Therefore, standard static allocation and assignment rules do not apply in this case. In this paper, we propose a framework capable of tackling these real-time updates. From a methodological point of view, solving the dynamic version of the parking allocation problem represents a quantum leap compared with solving the static version. We achieve this goal by solving a sequence of 0-1 programming models over the planning horizon, and we develop several parking policies. The proposed policies are empirically compared on real data gathered from three European cities: Belgrade, Luxembourg, and Lyon. The results show that our framework is scalable and can improve the quality of the allocation, in particular when parking capacities are low

Towards Structuring Smart Grid: Energy Scheduling, Parking Lot Allocation, and Charging Management

Nowadays, the conventional power systems are being restructured and changed into smart grids to improve their reliability and efficiency, which brings about better social, economic, and environmental benefits. To build a smart grid, energy scheduling, energy management, parking lot allocation, and charging management of plug-in electric vehicles (PEVs) are important subjects that must be considered. Accordingly, in this dissertation, three problems in structuring a smart grid are investigated. The first problem investigates energy scheduling of smart homes (SHs) to minimize daily energy consumption cost. The challenges of the problem include modeling the technical and economic constraints of the sources and dealing with the variability and uncertainties concerned with the power of the photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of power of PV panels, we propose a multi-time scale stochastic model predictive control (MPC). We use multi-time scale approach in the stochastic MPC to simultaneously have vast vision for the optimization time horizon and precise resolution for the problem variables. In addition, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool. Further, we propose cooperative distributed energy scheduling to enable SHs to share their energy resources in a distributed way. The simulation results demonstrate remarkable cost saving due to cooperation of SHs with one another and the effectiveness of multi-time scale MPC over single-time scale MPC. Compared to the previous studies, this work is the first study that proposes cooperative distributed energy scheduling for SHs and applies multi-time scale optimization. In the second problem, the price-based energy management of SHs for maximizing the daily profit of GENCO is investigated. The goal of GENCO is to design an optimal energy management scheme (optimal prices of electricity) that will maximize its daily profit based on the demand of active customers (SHs) that try to minimize their daily operation cost. In this study, a scenario-based stochastic approach is applied in the energy scheduling problem of each SH to address the variability and uncertainty issues of PV panels. Also, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool for the energy scheduling problem of a SH. Moreover, Lambda-Iteration Economic Dispatch and GA approaches are applied to solve the generation scheduling and unit commitment (UC) problems of the GENCO, respectively. The numerical study shows the potential benefit of energy management for both GENCO and SH. Moreover, it is proven that the GENCO needs to implement the optimal scheme of energy management; otherwise, it will not be effective. Compared to the previous studies, the presented study in this paper is the first study that considers the interaction between a GENCO and SHs through the price-controlled energy management to maximize the daily profit of the GENCO and minimize the operation cost of each SH. In the third problem, traffic and grid-based parking lots allocation and charging management of PEVs is investigated from a DISCO’s and a GENCO’s viewpoints. Herein, the DISCO allocates the parking lots to each electrical feeder to minimize the overall cost of planning problem over the planning time horizon (30 years) and the GENCO manages the charging time of PEVs to maximize its daily profit by deferring the most expensive and pollutant generation units. In both planning and operation problems, the driving patterns of the PEVs’ drivers and their reaction respect to the value of incentive (discount on charging fee) and the average daily distance from the parking lot are modeled. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing (SA) algorithm (QSA algorithm) and genetic algorithm (GA), respectively. We demonstrate that the behavioral model of drivers and their driving patterns can remarkably affect the outcomes of planning and operation problems. We show that optimal allocation of parking lots can minimize every DISCO’s planning cost and increase the GENCO’s daily profit. Compared to the previous works, the presented study in this paper is the first study that investigates the optimal parking lot placement problem (from every DISCO’s view point) and the problem of optimal charging management of PEVs (from a GENCO’s point of view) considering the characteristics of electrical distribution network, driving pattern of PEVs, and the behavior of drivers respect to value of introduced incentive and their daily distance from the suggested parking lots. In our future work, we will develop a more efficient smart grid. Specifically, we will investigate the effects of inaccessibility of SHs to the grid and disconnection of SHs in the first problem, model the reaction of other end users (in addition to SHs) based on the price elasticity of demand and their social welfare in the second problem, and propose methods for energy management of end users (in addition to charging management of PEVs) and model the load of end users in the third problem

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Study on Profitable Shared Parking Management Based on Day-to-Day Evolution Model

Parking problems are getting increasingly serious in the urban area. However, the parking spots in the urban area are underutilized rather than really scarce. There is a large number of private spots in the residential areas that have the potential of being shared. Due to its private nature, shared parking is usually operated by a profitable mode. To study the utilization of shared parking and its impact on the morning commute, this paper proposes an evolution model. The supply side is a profit-chasing manager who decides on the selling prices and the business scale, while the demand side refers to travellers who respond to costs and choose the trip mode. By analysing the behaviour (strategy) of both sides, the study covers: 1 - the attraction and competition between parking lots and trip modes, 2 - the utilization and user composition of the parking lots. By inducing two numerical examples, the conclusions are that 1 - managers can achieve maximum profit and optimal allocation through price adjustment and quantity control; 2 - publicity (system cost minimization) and profitability (profit maximization) are consistent under certain threshold conditions; 3 - competition exists between parking lots as well as trip modes; some parking lots are even in short supply; profitable management does not create a market monopoly

Improved utilization for “smart parking systems” based on paging technique

Considering the rapid urbanization and the road congestion, the development of smart parking solutions becomes more crucial, especially in terms of economic interests. Thanks to IoT-connectivity and the cloud-integrated platforms, drivers can easily find a vacant parking lot with smart parking services. This paper intervenes in the profit of parking management systems. The paper proposes a new technique “paging technique” which increases the utilization factor of parking slots. The proposed method takes advantage of the idle time that exists between two successful parking services in the same slot. Besides, it investigates the possibility of using the idle times from different parking slots to provide a continuous parking time for an additional car. The paging technique is optimally implemented using mixed-integer linear programming that maximizes the utilization factor for the parking slots with minimum car transitions. Moreover, a data model for the parking management system has been constructed while considering the three major customers, namely, regular, prepaid, and walk-in customers. The difference between fixed and dynamic pricing for parking has been investigated. The technique has been validated using GAMS optimization software and hardware using DSP with Coin-or branch and cut solver (CBC) under real-life conditions. The statistical results prove that the revenue for the proposed parking system has increased significantly. Finally, a comparative analysis is performed, benchmarking our proposed method against recent competing algorithms in real world applications to demonstrate its superiority