Final report: Workshop on: Integrating electric mobility systems with the grid infrastructure

EXECUTIVE SUMMARY: This document is a report on the workshop entitled “Integrating Electric Mobility Systems with the Grid Infrastructure” which was held at Boston University on November 6-7 with the sponsorship of the Sloan Foundation. Its objective was to bring together researchers and technical leaders from academia, industry, and government in order to set a short and longterm research agenda regarding the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. The report is a summary of their insights based on workshop presentations and discussions. The list of participants and detailed Workshop program are provided in Appendices 1 and 2. Public and private decisions made in the coming decade will direct profound changes in the way people and goods are moved and the ability of clean energy sources – primarily delivered in the form of electricity – to power these new systems. Decisions need to be made quickly because of rapid advances in technology, and the growing recognition that meeting climate goals requires rapid and dramatic action. The blunt fact is, however, that the pace of innovation, and the range of business models that can be built around these innovations, has grown at a rate that has outstripped our ability to clearly understand the choices that must be made or estimate the consequences of these choices. The group of people assembled for this Workshop are uniquely qualified to understand the options that are opening both in the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. They were asked both to explain what is known about the choices we face and to define the research issues most urgently needed to help public and private decision-makers choose wisely. This report is a summary of their insights based on workshop presentations and discussions. New communication and data analysis tools have profoundly changed the definition of what is technologically possible. Cell phones have put powerful computers, communication devices, and position locators into the pockets and purses of most Americans making it possible for Uber, Lyft and other Transportation Network Companies to deliver on-demand mobility services. But these technologies, as well as technologies for pricing access to congested roads, also open many other possibilities for shared mobility services – both public and private – that could cut costs and travel time by reducing congestion. Options would be greatly expanded if fully autonomous vehicles become available. These new business models would also affect options for charging electric vehicles. It is unclear, however, how to optimize charging (minimizing congestion on the electric grid) without increasing congestion on the roads or creating significant problems for the power system that supports such charging capacity. With so much in flux, many uncertainties cloud our vision of the future. The way new mobility services will reshape the number, length of trips, and the choice of electric vehicle charging systems and constraints on charging, and many other important behavioral issues are critical to this future but remain largely unknown. The challenge at hand is to define plausible future structures of electric grids and mobility systems, and anticipate the direct and indirect impacts of the changes involved. These insights can provide tools essential for effective private ... [TRUNCATED]Workshop funded by the Alfred P. Sloan Foundatio

A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems

The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable. The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk. Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible. The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints. An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response. The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Simulating The Impact of Emissions Control on Economic Productivity Using Particle Systems and Puff Dispersion Model

A simulation platform is developed for quantifying the change in productivity of an economy under passive and active emission control mechanisms. The program uses object-oriented programming to code a collection of objects resembling typical stakeholders in an economy. These objects include firms, markets, transportation hubs, and boids which are distributed over a 2D surface. Firms are connected using a modified Prim’s Minimum spanning tree algorithm, followed by implementation of an all-pair shortest path Floyd Warshall algorithm for navigation purposes. Firms use a non-linear production function for transformation of land, labor, and capital inputs to finished product. A GA-Vehicle Routing Problem with multiple pickups and drop-offs is implemented for efficient delivery of commodities across multiple nodes in the economy. Boids are autonomous agents which perform several functions in the economy including labor, consumption, renting, saving, and investing. Each boid is programmed with several microeconomic functions including intertemporal choice models, Hicksian and Marshallian demand function, and labor-leisure model. The simulation uses a Puff Dispersion model to simulate the advection and diffusion of emissions from point and mobile sources in the economy. A dose-response function is implemented to quantify depreciation of a Boid’s health upon contact with these emissions. The impact of emissions control on productivity and air quality is examined through a series of passive and active emission control scenarios. Passive control examines the impact of various shutdown times on economic productivity and rate of emissions exposure experienced by boids. The active control strategy examines the effects of acceptable levels of emissions exposure on economic productivity. The key findings on 7 different scenarios of passive and active emissions controls indicate that rate of productivity and consumption in an economy declines with increased scrutiny of emissions from point sources. In terms of exposure rates, the point sources may not be the primary source of average exposure rates, however they significantly impact the maximum exposure rate experienced by a boid. Tightening of emissions control also negatively impacts the transportation sector by reducing the asset utilization rate as well as reducing the total volume of goods transported across the economy

Concepts, Mechanisms, and Algorithms to Measure the Potential of Container Sharing in Seaport Hinterland Transportation

This thesis analyzes how trucking companies of a hinterland region can improve their routes if shipping companies allow the mutual exchange of their containers. In this case, trucking companies that are assigned by shipping companies cooperate by sharing information regarding which locations empty containers are currently stacked. These containers can then be integrated into a vehicle's route of any operating trucking company in the hinterland. The investigation aims at measuring the quantitative potential of the container sharing idea by means of problem settings illustrating realistic hinterland regions of a seaport. As a first step, the impact of street turns on the transportation costs of a trucking company should be measured. By forbidding or allowing the use of street turns for a single trucking company, the potential of the container sharing idea can be indicated, and the interrelation of empty container movements and transportation costs can be shown. As a further step, the benefit of exchanging empty containers between several trucking companies needs to be analyzed. In doing so, it is possible to investigate the potential and realistic limits of container sharing