Carbon Free Boston: Transportation Technical Report

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical ReportOVERVIEW: Transportation connects Boston’s workers, residents and tourists to their livelihoods, health care, education, recreation, culture, and other aspects of life quality. In cities, transit access is a critical factor determining upward mobility. Yet many urban transportation systems, including Boston’s, underserve some populations along one or more of those dimensions. Boston has the opportunity and means to expand mobility access to all residents, and at the same time reduce GHG emissions from transportation. This requires the transformation of the automobile-centric system that is fueled predominantly by gasoline and diesel fuel. The near elimination of fossil fuels—combined with more transit, walking, and biking—will curtail air pollution and crashes, and dramatically reduce the public health impact of transportation. The City embarks on this transition from a position of strength. Boston is consistently ranked as one of the most walkable and bikeable cities in the nation, and one in three commuters already take public transportation. There are three general strategies to reaching a carbon-neutral transportation system: • Shift trips out of automobiles to transit, biking, and walking;1 • Reduce automobile trips via land use planning that encourages denser development and affordable housing in transit-rich neighborhoods; • Shift most automobiles, trucks, buses, and trains to zero-GHG electricity. Even with Boston’s strong transit foundation, a carbon-neutral transportation system requires a wholesale change in Boston’s transportation culture. Success depends on the intelligent adoption of new technologies, influencing behavior with strong, equitable, and clearly articulated planning and investment, and effective collaboration with state and regional partners.Published versio

Optimization of Vehicle to Grid System in a Power System With Unit Commitment

This thesis provides a comprehensive overview and analysis of the benefits of using plug-in electric vehicles (PEVs) in solving the unit commitment problem. PEVs are becoming more attractive and a rapid replacement of conventional fuel vehicles due to their environmental-friendly operation. Through collective control by an aggregator, PEVs batteries can also provide ancillary services such as load leveling and frequency regulation to improve the quality of power supplied in the power grid and reduce the cost of power generation. This study presents the modeling, simulation, and analysis of a vehicle-to-grid (V2G) system connected to a smart power grid. The model considers different penetration levels of PEVs in a system and investigates the economic and technical effects of using PEVs to support the grid. The model is tested using an IEEE 24 bus network to verify the effects that PEVs penetration has on generation cost in power systems. A comparison has been made between a system without V2G and a system with V2G to produce justification for the role that V2G can play in solving the unit commitment problem. The results of this study show that the optimal scheduling of PEVs was effective in flattening the load profile through valley filling and peak load reduction

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

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system

Integrated modelling framework for the analysis of demand side management strategies in urban energy systems

Influenced by environmental concerns and rapid urbanisation, cities are changing the way they historically have produced, distributed and consumed energy. In the next decades, cities will have to increasingly adapt their energy infrastructure if new low carbon and smart technologies are to be effectively integrated. In this context, advanced planning tools can become crucial to successfully design these future urban energy systems. However, it is not only important to analyse how urban energy infrastructure will look like in the future, but also how they will be operated. Advanced energy management strategies can increase the operational efficiency, therefore reducing energy consumption, CO2 emissions, operational costs and network investments. However, the design and analysis of these energy management strategies are difficult to perform at an urban scale considering the spatial and temporal resolution and the diversity in users energy requirements. This thesis proposes a novel integrated modelling framework to analyse flexible transport and heating energy demand and assess different demand-side management strategies in urban energy systems. With a combination of agent-based simulation and multi-objective optimisation models, this framework is tested using two case studies. The first one focuses on transport electrification and the integration of electric vehicles through smart charging strategies in an urban area in London, UK. The results of this analysis show that final consumer costs and carbon emissions reductions (compared to a base case) are in the range of 4.3-45.0% and 2.8-3.9% respectively in a daily basis, depending on the type of tariff and electricity generation mix considered. These reductions consider a control strategy where the peak demand is constrained so the capacity of the system is not affected. In the second case study, focused on heat electrification, the coordination of a group of heat pumps is analysed, using different scheduling strategies. In this case, final consumer costs and carbon emissions can be reduced in the range of 4-41% and 0.02-0.7% respectively on a daily basis. In this case, peak demand can be reduced in the range of 51-62% with respect to the baseline. These case studies highlight the importance of the spatial and temporal characterisation of the energy demand, and the level of flexibility users can provide to the system when considering a heterogeneous set of users with different technologies, energy requirements and behaviours. In both studies, trade-offs between the environmental and economic performance of demand-side management strategies are assessed using a multi-objective optimisation approach. Finally, further applications of the integrated modelling framework are described to highlight its potential as a decision-making support tool in sustainable and smart urban energy systems.Open Acces

Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable Last-Mile Strategies

In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. While this has brought prosperity for the e-retailers, the ever-increasing consumer demand has brought more trucks to the residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. To cope with this, different logistics strategies such as the introduction of micro-hubs, alternative delivery points, and use of cargo bikes and zero emission vehicles for the last mile have been introduced and, in some cases, implemented as well. This project, hence, aims to develop an analytical framework to model urban last mile delivery. In particular, this study will build upon the previously developed econometric behavior models that capture e-commerce demand. Then, based on continuous approximation techniques, the authors will model the last-mile delivery operations. And finally, using the cost-based sustainability assessment model (developed in this study), the authors will estimate the economic and environmental impacts of residential deliveries under different city logistics strategies.View the NCST Project Webpag

Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

Constrained Route Optimization With Fleet Considerations for Electrified Heavy-Duty Freight Vehicles

Almost 75% of traffic-related emissions are caused by heavy-duty freight trucks and significantly impact neighborhoods, schools, and communities around shipping and distribution lines. With poor air quality and respiratory health, many children in at-risk and disadvantaged communities experience high rates of asthma, lower attendance in school, and lower concentration. This research creates to improve the impacts of heavy-duty electric freight by improving the route efficiency (in terms of energy, time, or route distance) of EV trucks. Our software and algorithms are tested in a simulation environment using data from several thousand fleet trucks operating in the Salt Lake City area. The software shows an anticipated energy reduction of ~ 6% to ~ 10% at the cost of ~ 3% increases in vehicle travel distance. Further, we anticipate positive health impacts in areas of dense trucking as we reduce the energy needs of electrification for fleet operators