Determining electric vehicle charging point locations considering drivers' daily activities

In this paper the daily temporal and spatial behavior of electric vehicles (EVs) is modelled using an activity-based (ActBM) microsimulation model for Flanders region (Belgium). Assuming that all EVs are completely charged at the beginning of the day, this mobility model is used to determine the percentage of Flemish vehicles that cannot cover their programmed daily trips and need to be recharged during the day. Assuming a variable electricity price, an optimization algorithm determines when and where EVs can be recharged at minimum cost for their owners. This optimization takes into account the individual mobility constraint for each vehicle, as they can only be charged when the car is stopped and the owner is performing an activity. From this information, the aggregated electric demand for Flanders is obtained, identifying the most overloaded areas at the critical hours. Finally it is also analyzed what activities EV owners are underway during their recharging period. From this analysis, different actions for public charging point deployment in different areas and for different activities are proposed

15-08 Community-Aware Charging Station Network Design for Electrified Vehicles in Urban Areas: \u3c/i\u3e Reducing Congestion, Emissions, Improving Accessibility, and Promoting Walking, Bicycling, and use of Public Transportation\u3c/i\u3e

A major challenge for achieving large-scale adoption of EVs is an accessible infrastructure for the communities. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations due to mutual dependence of EV sales and public infrastructure deployment. Such infrastructure deployment also presents a number of unique opportunities for promoting livability while helping to reduce the negative side-effects of transportation (e.g., congestion, emissions, and noise pollution). In this phase, we develop a modeling framework (MF) to consider various factors and their associated uncertainties for an optimal network design for electrified vehicles. The factors considered in the study include: state of charge, dwell time, Origin-Destination (OD) pair

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

California public electric vehicle charging stations’ accessibility to amenities: A GIS network analysis approach

In California, the number of electric vehicles (EVs) on the roads has been increasing over the past several years. As EVs continue to grow, additional electric vehicle charging stations (EVCSs) will be needed for EV drivers to utilize. However, before implementing EVCSs in the public, there are various criteria that need to be considered. One of these criteria is public EVCSs’ accessibility to amenities. When people are charging their EVs that require a significant amount of waiting time, having amenities nearby will provide them with the option to spend their time efficiently on worthwhile activities. To understand the accessibility of California public EVCSs to amenities, existing charging stations were examined with two popular amenities. Closest facility analysis from ArcGIS 10.4.1 was used to analyze and compute the distance from each of the public charging station to the closest amenity. The accessibility was based on whether the distances between the EVCSs and the amenities are within a tolerable walking distance. From the data analysis, two results were produced for the amenities examined and presented different percentages of the accessibility. For more precise results, further examination of public EVCSs’ accessibility to amenities is needed and can be accomplished by considering additional amenities in the data analysis. Additionally, this study provides an approach to evaluate the accessibility of charging stations to amenities, which can be useful for locating optimal EVCS sites

A multistage design procedure for planning and implementing public charging infrastructures for electric vehicles

Presented in this paper is a Multistage Design Procedure (MSDP) for planning and implementing Public Charging Infrastructures (PCIs) to satisfy intracity charging demand of Electric Vehicles (EVs). The proposed MSDP splits planning and design processes into multiple stages, from macroscale to fine-scale levels. Consequently, the preliminary results achieved at each stage can be refined at the subsequent stages, leading to determine the accurate number and precise geographical location of each charging point. The main advantage of the proposed approach is that it splits a very complicated procedure into multiple and simpler stages, at each of which appropriate goals, targets and constraints can be included. As a result, the iterative interactions among all the stakeholders involved in the PCI design process are significantly simplified. The proposed MSDP has been employed in the planning and design of the PCI of the Italian island of Sardinia, accordingly to all the public bodies

17-07 Phase-II: Community-Aware Charging Station Network Design for Electrified Vehicles in Urban Areas: \u3c/i\u3e Reducing Congestion, Emissions, Improving Accessibility, and Promoting Walking, Bicycling, and use of Public Transportation

A major challenge for achieving large-scale adoption of EVs is an accessible infrastructure for the communities. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations due to mutual dependence of EV sales and public infrastructure deployment. Such infrastructure deployment also presents a number of unique opportunities for promoting livability while helping to reduce the negative side-effects of transportation (e.g., congestion, emissions, and noise pollution). In this phase, we develop a modeling framework (MF) to consider various factors and their associated uncertainties for an optimal network design for electrified vehicles. The factors considered in the study include: state of charge, dwell time, Origin-Destination (OD) pair

Essays On Stochastic Programming In Service Operations Management

Deterministic mathematical modeling is a branch of optimization that deals with decision making in real-world problems. While deterministic models assume that data and parameters are known, these numbers are often unknown in the real world applications.The presence of uncertainty in decision making can make the optimal solution of a deterministic model infeasible or sub-optimal. On the other hand, stochastic programming approach assumes that parameters and coefficients are unknown and only their probability distribution can be estimated. Although stochastic programming could include uncertainties in objective function and/or constraints, we only study problems that the goal of stochastic programming is to maximize (minimize) the expectation of the objective function of random variables. Stochastic programming has a wide range of application in manufacturing production planning, machine scheduling, dairy farm expansion planning, asset liability management, traffic management, and automobile dealership inventory management that involve uncertainty in decision making. One limitation of stochastic programming is that considering uncertainty in mathematical modeling often leads to a large-scale programming problem. The most widely used stochastic programming model is two-stage stochastic programming. In this model, first-stage decision variables are determined before observing the realization of uncertainties and second-stage decision variables are selected after exposing first-stage variables into the uncertainties. The goal is to determine the value of first-stage decisions in a way to maximize (minimize) the expected value of second-stage objective function. 1.1 Motivation for Designing Community-Aware Charging Network for Electric Vehicles Electric vehicles (EVs) are attracting more and more attentions these days due to increase concern about global warming and future shortage of fossil fuels. These vehicles have potential to reduce greenhouse gas emissions, improve public health condition by reducing air pollution and improving sustainability, and addressing diversication of transportation energy feedstock. Governments and policy makers have proposed two types of policy incentives in order to encourage consumers to buy an EV: direct incentives and indirect incentives. Direct incentives are those that have direct monetary value to consumers and include purchase subsidies, license tax/fee reductions, Electric Vehicle Supply Equipment (EVSE) financing, free electricity, free parking and emission test exemptions. On the other hand, indirect incentives are the ones that do not have direct monetary value and consist of high-occupancy vehicle access, emissions testing exemption time savings, and public charger availability. Lack of access to public charging network is considered to be a major barrier in adoption of EVs [38]. Access to public charging infrastructure will provide confidence for EV owners to drive longer distances without going out of charge and encourage EV ownership in the community. The current challenge for policy makers and city planners in installing public charging infrastructure is determining the location of these charging service stations, number of required stations and level of charging since the technology is still in its infancy and the installation cost is high. Since recharging of EV battery takes more time than refueling conventional vehicles, parking lots and garages are considered as potential locations for installing charging stations. The aim of this research is to develop a mathematical programming model to find the optimal locations with potentially high utilization rate for installing community-aware public EV charging infrastructure in order to improve accessibility to charging service and community livability metrics. In designing such charging network, uncertainties such as EV market share, state of battery charge at the time of arrival, driver’s willingness to charge EV away from home, arrival time to final destination, driver’s activity duration (parking duration), and driver’s walking distance preference play major role. Incorporating these uncertainties in the model, we propose a two-stage stochastic programming approach to determine the location and capacity of public EV charging network in a community. 1.2 Motivation for Managing Access to Care at Primary Care Clinics Patient access to care along with healthcare efficiency and quality of service are dimensions of health system performance measurement [1]. Improving access to primary care is a major step of having a high-performing health care system. However, many patients are struggling to get an in-time appointment with their own primary care provider (PCP). Even two years aer health insurance coverage was expanded, new patients have to wait 82% longer to get an internal-medicine appointment. A national survey shows that percentage of patients that need urgent care and could not get an appointment increased from 53% to 57% between 2006 and 2011 [30]. This delay may negatively impact health status of patients and may even lead to death. Patients that cannot get an appointment with their PCP may seek care with other providers or in emergency departments which will decrease continuity of care and increase total cost of health system. The main issue behind access problem is the imbalance between provider capacity and patient demand. While provider panel size is already large, the shortage in primary care providers and increasing number of patients mean that providers have to increase their panel size and serve more patients which will potentially lead to lower access to primary care. The ratio of adult primary care providers to population is expected to drop by 9% between 2005 and 2020 [12]. Moreover, patient flow analysis can increase efficiency of healthcare system and quality of health service by increasing patient and provider satisfaction through better resource allocation and utilization [39]. Effective resource allocation will smooth patient ow and reduce waste which will in turn results in better access to care. One way to control patient flow in clinic is managing appointment supply through appointment scheduling system. A well-designed appointment scheduling system can decrease appointment delay and waiting time in clinic for patients and idle time and/or overtime for physicians at the same time and increase their satisfaction. Appointment scheduling requires to make a balance between patient needs and facility resources [13]. The purpose of this study is to gain a better understanding for managing access to care in primary care outpatient clinics through operations management research. As a result of this under standing, we develop appointment scheduling models using two-stage stochastic programming to improve access while maintaining high levels of provider capacity utilization and improving patient flow in clinic by leveraging uncertainties in patient demand, patient no-show and provider service time variability

Management Strategies for Electric Vehicle Fleets

The research leading to these results has received funding from the EU 7th FP under the project DATA science for SIMulating the era of electric vehicles (DATASIM, FP7-ICT-270833). DATA SIM aims at providing an entirely new and highly detailed spatial-temporal microsimulation methodology for human mobility with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles. The objective of this work is focused in the development of charging management strategies for electric vehicle (EV) fleets. Its purpose is to maximize the integration of EVs in the current electric grid considering their consumption and their charging limits, both temporal and spatially. The main contribution of this work is the development of a novel Peer to Peer Energy Trading System (P2PETS) between EVs in order to reduce the impact of charging EVs over the electric grid

The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change