Funding Source: Maine Turnpike Authority Total Budget: $25,000 Problem Statement A 30-mile segment of Maine Turnpike from mile 12 in the Town of York to mile 42 in Scarborough is proposed for widening and modernization. There are 53 different crossings associated with various streams, rivers and unnamed tributaries that are part of Maine's southern coastal drainage system. At least 17 out of the 51 streams support native populations of brook trout or brown trout. An estimated 1,892 linear feet of streambed will be lost due to extensions of existing culverts. This represents an average of about 36 feet per stream or 18 feet of streambed on each side of the Turnpike. There are also 17 streams in which some portion of the channel will need to be relocated due to either encroachment from the new embankment location or to connect the stream to the new culvert outlet location. Project Objective To mitigate for impacts to fish habitat caused by the stream crossings and relocations through a combination of habitat enhancement measures and fish passage improvements.

The EVALUATE (Electric Vehicle Assessment and Leveraging of Unified models toward AbatemenT of Emissions) project (Phases I and II) develops a rigorous methodology involving a high-fidelity system of systems model (i.e., vehicle powertrain, EV charging profiles and grid dispatch datasets) for the purpose of forecasting the emissions outputs of a class of vehicles and use cases. Phase I findings explored urban trips by households that operate light duty vehicles (LDVs) for daily personal use. Phase II, presented here, focuses on a series of targeted case studies that extend prior work from LDVs operated by individuals to service-oriented vehicles operated by small and medium businesses. Vehicles used in the present study are representative of public service fleets including the following: pickup trucks, vans, Medium Duty (MD) delivery vehicles, and refuse trucks. In one of the study’s simulations for a MD use case where a specific marginal grid generating resource is identified on an hourly basis as the grid’s means of supplying a particular EV charging event, estimated CO2 emissions could be as much as 42% lower than a conventional gasoline vehicle, or as much as 24% higher than a conventional gasoline vehicle. This large variance is purely a function of when and how quickly the vehicle is recharged, and upstream grid factors. This study reveals key insights: (1) Higher temporal resolution is important to develop more accurate estimates of EV CO2 emissions. Along with this, EV charge management is imperative for all use cases, and has profound implications on infrastructure and emissions; (2) Hybrid Electric Vehicles (HEVs) often performed as well as EVs in contemporary simulations on the basis of emissions benefits, suggesting that consideration of an array of vehicle technologies is important; (3) There is a growing need to focus on higher rate EV charging applications (e.g., DCFC), and related implications on grid demands and energy storage, as proxied by large vehicle batteries; and (4) The trend toward increasing electrification of the transportation sector will continue in conjunction with electrification across other sectors (e.g., buildings, data centers, industry). As such, associated cross-sector planning and study of concomitant emissions must be considered in context of other grid trends. Primary contributions of this effort are the development of new methodologies, integration of sub-system models and independent data sources, and decision support tools that estimate the environmental impacts of vehicle electrification. The study’s methodologies and use cases can enhance understanding and scale-up in additional EV-grid applications, sectors and regions.

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This research explores electric vehicle (EV) and grid interactions with a focus on CO2 emissions for future scenarios where EVs comprise growing market shares (e.g., 10% of the overall fleet mix). A major contribution of this effort has been to develop a methodology that integrates sub-system models and datasets that have previously stood alone, namely models and data that characterize: vehicle energy consumption, travel demands, vehicle charging, and temporal emission profiles associated with electric power generation dispatch. This convergence research helps quantify the relative emissions of light duty vehicle use and charging during various times of day to enable comparison of EV modes against one another and against conventional vehicle baselines. An initial use case involving light duty commuter and recharging scenarios has been explored as a means of validating and tuning the methodology. Under certain simulated scenarios, observed marginal emissions can be as much as 20% lower in the overnight hours compared to marginal CO2 emissions experienced during an identical charging event during the daytime. This study also confirms that marginal CO2 assumptions generally yield higher CO2 impacts than identical simulations that assume weighted average emissions. This variance is broad, ranging from 22% less to 97% greater, depending on a host of case-sensitive factors. These findings suggest that it will be essential to coordinate charging schedules and consider upstream grid implications in order to reduce the environmental impacts of EVs. By quantifying technical parameters related to both the magnitude and the range of possible emissions impacts, the study’s findings can be useful for education and awareness by all EV users, and will help decision-makers consider the importance of emission rate assumptions and the temporal granularity of the tools and data. More specifically, stakeholders should be incentivized to charge when marginal emissions are lowest whenever possible. This idea also has important implications about the location, type, cost and ownership models for tomorrow’s charging infrastructure. Translating and operationalizing this type of guidance will require some combination of education, access to rigorous and clear decision-support tools, signals between stakeholders (e.g., utilities and consumers), and behavioral change.

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