Smart charging profiles for electric vehicles

Electric vehicles (EVs) can help decarbonise the transportation sector, which is responsible for a great share of greenhouse gas emissions. Although different measures have been introduced to foster the penetration of EVs in the society, they have not been deployed at a large scale yet. Electric companies are concerned about the effects of introducing EVs into the grid, especially with a large amount. The charging pattern of EVs is the main factor that determines these effects. Unregulated charging (probably when returning home) would have undesirable consequences (e.g. increase in variable costs, emissions, reduction of reliability) for the system, it is therefore necessary to develop an “intelligent” charging strategy. These characteristics justify the existence of different smart charging profiles. It is also important to assess the effect of using day-ahead management systems instead of pre-set profiles. This document compares different possible strategies for charging EVs and their consequences in the power system. The impact on variable costs, emissions and renewable energy sources integration will be obtained using an operation planning model. The Spanish power system for 2020 is analysed under different EV penetration levels and charging strategies. The results show the benefits of using smart charging profiles instead of an unregulated profile, obtaining large cost reductions and maintaining system reliability levels. Moreover, the benefits of using a day-ahead management system are also evaluated, resulting in a small reduction of system variable cost compared to the use of pre-defined charging profiles.     

Model-based regression clustering for high-dimensional data: application to functional data

Finite mixture regression models are useful for modeling the relationship between response and predictors arising from different subpopulations. In this article, we study high-dimensional predictors and high-dimensional response and propose two procedures to cluster observations according to the link between predictors and the response. To reduce the dimension, we propose to use the Lasso estimator, which takes into account the sparsity and a maximum likelihood estimator penalized by the rank, to take into account the matrix structure. To choose the number of components and the sparsity level, we construct a collection of models, varying those two parameters and we select a model among this collection with a non-asymptotic criterion. We extend these procedures to functional data, where predictors and responses are functions. For this purpose, we use a wavelet-based approach. For each situation, we provide algorithms and apply and evaluate our methods both on simulated and real datasets, to understand how they work in practice.     

Effect of stop line detection in queue length estimation at traffic signals from probe vehicles data

Stop line detectors are one of the most deployed traffic data collection technologies at signalized intersections today. Newly emerging probe vehicles are increasingly receiving more attention as an alternative means of real-time monitoring for better system operations, however, high market penetration levels are not expected in the near future. This paper focuses on real-time estimation of queue lengths by combining these two data types, i.e., actuation from stop line detectors with location and time information from probe vehicles, at isolated and undersaturated intersections. Using basic principles of statistical point estimation, analytical models are developed for the expected total queue length and its variance at the end of red interval. The study addresses the evaluation of such estimators as a function of the market penetration of probe vehicles. Accuracy of the developed models is compared using a microscopic simulation environment-VISSIM. Various numerical examples are presented to show how estimation errors behave by the inclusion of stop line detection for different volume to capacity ratio and market penetration levels. Results indicate that the addition of stop line detection improves the estimation accuracy as much as 14% when overflow queue is ignored and 24% when overflow queue is included for less than 5% probe penetration level.     

Location-allocation models for traffic police patrol vehicles on an interurban network

This research investigates the traffic police routine patrol vehicle (RPV) assignment problem on an interurban road network through a series of integer linear programs. The traffic police RPV’s main task, like other emergency services, is to handle calls-for-service. Emergency services allocation models are generally based on the shortest path algorithm however, the traffic police RPV also handles other roles, namely patrolling to create a presence that acts as a deterrence, and issuing tickets to offenders. The RPVs need to be located dynamically on both hazardous sections and on roads with heavy traffic in order to increase their presence and conspicuousness, in an attempt to prevent or reduce traffic offences, road accidents and traffic congestion. Due to the importance of the traffic patrol vehicle’s location with regard to their additional roles, allocation of the RPVs adheres to an exogenous, legal, time-to-arrival constraint. We develop location-allocation models and apply them to a case study of the road network in northern Israel. The results of the four models are compared to each other and in relation to the current chosen locations. The multiple formulations provide alternatives that jointly account for road safety and policing objectives which aid decision-makers in the selection of their preferred RPV assignments. The results of the models present a location-allocation configuration per RPV per shift with full call-for-service coverage whilst maximizing police presence and conspicuousness as a proxy for road safety.     

Locating a metro line in a historical city centre: application to Sevilla

When constructing a metro alignment under a historical city centre, it is important to generate a cost-effective path while maintaining a minimum distance between the alignment and historical buildings. This paper describes a simple methodology for generating a set of good alternative solutions. It is based on the use of Voronoi diagrams. The method was applied to data from the city of Sevilla.     

Sparsity-promoting distributed charging control for plug-in electric vehicles over distribution networks

Uncoordinated charging of plug-in electric vehicles brings a new challenge on the operation of a power system as it causes power flow fluctuations and even unacceptable load peaks. To ensure the stability of power network, plug-in charging needs to be scheduled against the base load properly. In this paper, we propose a sparsity-promoting charging control model to address this issue. In the model, the satisfaction of customers is improved through sparsity-promoting charging where the numbers of charging time slots are optimized. Dynamic feeder overload constraints are imposed in the model to avoid any unacceptable load peaks, and thus ensure the network stability. Then, a distributed solution strategy is developed to solve the problem based on the alternating direction method of multipliers (ADMM) since most of power networks are managed typically in a distributed manner. During solving process, Lagrangian duality is used to transform the original problem into an equivalent dual problem, which can be decomposed into a set of homogeneous small-scaled sub-problems. Particularly, each sub-problem either has a closed-form solution or can be solved locally by an accelerated dual gradient method. The global convergence of the proposed algorithm is also established. Finally, numerical simulations are presented to illustrate our proposed method. In contrast to traditional charging models, our sparsity-promoting charging model not only ensures the stability of power network, but also improves the satisfaction of customers.     

Supply chain analysis under a price-discount incentive scheme for electric vehicles

We investigate an automobile supply chain where a manufacturer and a retailer serve heterogeneous consumers with electric vehicles (EVs) under a government’s price-discount incentive scheme that involves a price discount rate and a subsidy ceiling. We show that the subsidy ceiling is more effective in influencing the optimal wholesale pricing decision of the manufacturer with a higher unit production cost. However, the discount rate is more effective for the manufacturer with a lower unit production cost. Moreover, the expected sales are increasing in the discount rate but may be decreasing in the subsidy ceiling. Analytic results indicate that an effective incentive scheme should include both a discount rate and a subsidy ceiling. We also derive the necessary condition for the most effective discount rate and subsidy ceiling that maximize the expected sales of EVs, and obtain a unique discount rate and subsidy ceiling that most effectively improve the manufacturer’s incentive for EV production.     

Nonparametric spatial models for extremes: application to extreme temperature data

Estimating the probability of extreme temperature events is difficult because of limited records across time and the need to extrapolate the distributions of these events, as opposed to just the mean, to locations where observations are not available. Another related issue is the need to characterize the uncertainty in the estimated probability of extreme events at different locations. Although the tools for statistical modeling of univariate extremes are well-developed, extending these tools to model spatial extreme data is an active area of research. In this paper, in order to make inference about spatial extreme events, we introduce a new nonparametric model for extremes. We present a Dirichlet-based copula model that is a flexible alternative to parametric copula models such as the normal and t-copula. The proposed modelling approach is fitted using a Bayesian framework that allow us to take into account different sources of uncertainty in the data and models. We apply our methods to annual maximum temperature values in the east-south-central United States.     

Incentivizing the adoption of electric vehicles under subsidy schemes: A duopoly analysis

We analyze the competition between an electric vehicle (EV) manufacturer and an internal combustion vehicle manufacturer, under a government's subsidy scheme that provides a per-unit subsidy to the EV manufacturer or a price discount subsidy to EV consumers. The government should adopt the per-unit subsidy scheme, because, compared to the price-discount scheme, the government under the per-unit scheme can achieve the same EV sales and social welfare but pay for a smaller total subsidy.     

State space collapse with application to heavy traffic limits for multiclass queueing networks

Heavy traffic limits for multiclass queueing networks are a topic of continuing interest. Presently, the class of networks for which these limits have been rigorously derived is restricted. An important ingredient in such work is the demonstration of state space collapse. Here, we demonstrate state space collapse for two families of networks, first-in first-out (FIFO) queueing networks of Kelly type and head-of-the-line proportional processor sharing (HLPPS) queueing networks. We then apply our techniques to more general networks. To demonstrate state space collapse for FIFO networks of Kelly type and HLPPS networks, we employ law of large number estimates to show a form of compactness for appropriately scaled solutions. The limits of these solutions are next shown to satisfy fluid model equations corresponding to the above queueing networks. Results from Bramson [4,5] on the asymptotic behavior of these limits then imply state space collapse. The desired heavy traffic limits for FIFO networks of Kelly type and HLPPS networks follow from this and the general criteria set forth in the companion paper Williams [41]. State space collapse and the ensuing heavy traffic limits also hold for more general queueing networks, provided the solutions of their fluid model equations converge. Partial results are given for such networks, which include the static priority disciplines. This revised version was published online in June 2006 with corrections to the Cover Date.     

An application of deterministic chaotic maps to model packet traffic

We investigate the application of deterministic chaotic maps to model traffic sources in packet based networks, motivated in part by recent measurement studies which indicate the presence of significant statistical features in packet traffic more characteristic of fractal processes than conventional stochastic processes. We describe one approach whereby traffic sources can be modeled by chaotic maps, and illustrate the traffic characteristics that can be generated by analyzing several classes of maps. We outline a potential performance analysis approach based on chaotic maps that can be used to assess the traffic significance of fractal properties. We show that low order nonlinear maps can capture several of the fractal properties observed in actual data, and show that the source characteristics observed in actual traffic can lead to heavy-tailed queue length distributions. It is our conclusion that while there are considerable analytical difficulties, chaotic maps may allow accurate, yet concise, models of packet traffic, with some potential for transient and steady state analysis.     

A new algorithm for solving variational inequalities with application to the traffic assignment problem

The variational inequality problem in Euclidian space is formulated as a nonconvex, nondifferentiable optimization problem. We show that any stationary point is optimal, and we propose a solution algorithm that decreases the nondifferential objective monotonically. Application to the asymmetric traffic assignment problem is considered.Research supported by C.R.S.H. (Canada) grant #410-81-0722-RL and F.C.A.C. (Québec) grant # 83-AS-0026.     

An application of DPCA to oil data for CBM modeling

In multivariate time series analysis, dynamic principal component analysis (DPCA) is an effective method for dimensionality reduction. DPCA is an extension of the original PCA method which can be applied to an autocorrelated dynamic process. In this paper, we apply DPCA to a set of real oil data and use the principal components as covariates in condition-based maintenance (CBM) modeling. The CBM model (Model 1) is then compared with the CBM model which uses raw oil data as the covariates (Model 2). It is shown that the average maintenance cost corresponding to the optimal policy for Model 1 is considerably lower than that for Model 2, and when the optimal policies are applied to the oil data histories, the policy for Model 1 correctly indicates almost twice as many impending system failures as the policy for Model 2.     

The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology

In this paper, we present a two-echelon capacitated electric vehicle routing problem with battery swapping stations (2E-EVRP-BSS), which aims to determine the delivery strategy under battery driving range limitations for city logistics. The electric vehicles operating in the different echelons have different load capacities, battery driving ranges, power consumption rates, and battery swapping costs. We propose an integer programming formulation and a hybrid algorithm that combines a column generation and an adaptive large neighborhood search (CG-ALNS) to solve the problem. We conducted extensive computational experiments, demonstrate the applicability of the proposed model, and show the efficiency of the CG-ALNS algorithm. In addition, we explore the interplay between battery driving range and the effectiveness of vehicle emission reduction through sensitivity analysis.     

T-product factorization method for internet traffic data completion with spatio-temporal regularization

Computational Optimization and Applications - Recovery of network traffic data from incomplete observed data is an important issue in internet engineering and management. In this paper, by fully...     

Well adjusted: Using expediting and cancelation to manage store replenishment inventory for a seasonal good

We consider an inventory problem that can be translated into a two-period newsvendor setting where the day prior to sales, the newsvendor places an initial preliminary order—a semi-binding forecast—with the publisher. At the beginning of the actual day of sales, the newsvendor has a better forecast for the day’s demand: based on knowing the actual content of the paper, he knows whether it will be a high-demand day due to breaking news or a low-demand day due to slow news. He then can revise the preliminary order quantity by expediting additional papers or canceling all or part of the order, but each of these activities has an associated cost.     

Multi-objective metaheuristics for a location-routing problem with multiple use of vehicles on real data and simulated data

We address an integrated logistic system where decisions on location of depot, vehicle routing and assignment of routes to vehicles are considered simultaneously. Total cost and workload balance are common criteria influencing decision-making. Literature on location-routing problems addressed the location and vehicle routing decisions with a common assumption of assigning one route to one vehicle. However, the cost of acquiring vehicles (and crew) is often more significant than the routing cost. This notion of assigning several routes to a vehicle during the routing procedure is explored in our integrated model. We apply metaheuristics of tabu search and simulated annealing on real data and simulated data, to compare their performances under two versions: simultaneous or sequential routes assignment to vehicles. A new statistical procedure is proposed to compare two algorithms on the strength of their multi-objective solutions. Results show that the simultaneous versions have advantage over the sequential versions in problems where routes are capacity-constrained, but not in the time dimension. The simultaneous versions are also more effective in generating non-dominated solutions than the sequential versions.     

An application of the greedy heuristic of set cover to traffic checks

Let us suppose that X is a given, finite, not empty set and ${\mathcal F}Let us suppose that X is a given, finite, not empty set and is a given collection of subsets of X such that their union equals X, in other words covers X. Set cover is the problem of selecting as few as possible subsets from such that their union covers X. Max k-cover is the problem of selecting k subsets from such that their union has maximum cardinality. Both problems are NP-hard. There is a polynomial time greedy heuristic that iteratively selects the subset from that covers the largest number of yet uncovered elements. We implemented this greedy algorithm to support the planning of a checking system that is aimed to check the vehicles in a road network. We would like to answer such questions:

–	How many and which links are sufficient to check a given percentage of all traffic flow?
–	What percentage of traffic can be checked with given links?

This paper defines the necessary data and basic knowledge, gives algorithms to answer the previous questions and also shows the results of an implementation in a road network that contains about 11,000 junctions, 3,000 origin–destination junctions and 26,000 links.     

Optimal control of generalized multiobjective games with application to traffic networks modeling

The purpose of this paper is to study some new results on the existence and convergence of the solutions to controlled systems of generalized multiobjective games, controlled systems of traffic networks, and optimal control problems (OCPs). First, we introduce the controlled systems of generalized multiobjective games and establish the existence of the solutions for these systems using Browder-type fixed point theorem in the noncompact case and the $C_i$-quasi-concavity. Results on the convergence of controlled systems of the solutions for such problems using the auxiliary solution sets and the extended $C_i$-convexity of the objective functions are studied. Second, we investigate OCPs governed by generalized multiobjective games. The existence and convergence of the solutions to these problems are also obtained. Finally, as a real-world application, we consider the special case of controlled systems of traffic networks. Many examples are given for the illustration of our results.