4,565 research outputs found

    Ready To Roll: Southeastern Pennsylvania's Regional Electric Vehicle Action Plan

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    On-road internal combustion engine (ICE) vehicles are responsible for nearly one-third of energy use and one-quarter of greenhouse gas (GHG) emissions in southeastern Pennsylvania.1 Electric vehicles (EVs), including plug-in hybrid electric vehicles (PHEVs) and all-electric vehicles (AEVs), present an opportunity to serve a significant portion of the region's mobility needs while simultaneously reducing energy use, petroleum dependence, fueling costs, and GHG emissions. As a national leader in EV readiness, the region can serve as an example for other efforts around the country."Ready to Roll! Southeastern Pennsylvania's Regional EV Action Plan (Ready to Roll!)" is a comprehensive, regionally coordinated approach to introducing EVs and electric vehicle supply equipment (EVSE) into the five counties of southeastern Pennsylvania (Bucks, Chester, Delaware, Montgomery, and Philadelphia). This plan is the product of a partnership between the Delaware Valley Regional Planning Commission (DVRPC), the City of Philadelphia, PECO Energy Company (PECO; the region's electricity provider), and Greater Philadelphia Clean Cities (GPCC). Additionally, ICF International provided assistance to DVRPC with the preparation of this plan. The plan incorporates feedback from key regional stakeholders, national best practices, and research to assess the southeastern Pennsylvania EV market, identify current market barriers, and develop strategies to facilitate vehicle and infrastructure deployment

    Estimating the Benefits of Electric Vehicle Smart Charging at Non-Residential Locations: A Data-Driven Approach

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    In this paper, we use data collected from over 2000 non-residential electric vehicle supply equipments (EVSEs) located in Northern California for the year of 2013 to estimate the potential benefits of smart electric vehicle (EV) charging. We develop a smart charging framework to identify the benefits of non-residential EV charging to the load aggregators and the distribution grid. Using this extensive dataset, we aim to improve upon past studies focusing on the benefits of smart EV charging by relaxing the assumptions made in these studies regarding: (i) driving patterns, driver behavior and driver types; (ii) the scalability of a limited number of simulated vehicles to represent different load aggregation points in the power system with different customer characteristics; and (iii) the charging profile of EVs. First, we study the benefits of EV aggregations behind-the-meter, where a time-of-use pricing schema is used to understand the benefits to the owner when EV aggregations shift load from high cost periods to lower cost periods. For the year of 2013, we show a reduction of up to 24.8% in the monthly bill is possible. Then, following a similar aggregation strategy, we show that EV aggregations decrease their contribution to the system peak load by approximately 40% when charging is controlled within arrival and departure times. Our results also show that it could be expected to shift approximately 0.25kWh (~2.8%) of energy per non-residential EV charging session from peak periods (12PM-6PM) to off-peak periods (after 6PM) in Northern California for the year of 2013.Comment: Pre-print, under review at Applied Energ

    Optimisation algorithms for the charge dispatch of plug-in vehicles based on variable tariffs

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    Plug-in vehicles powered by renewable energies are a viable way to reduce local and total emissions and could also support a highly efficient grid operation. Indirect control by variable tariffs is one option to link charging or even discharging time with the grid load and the renewable energy production. Algorithms are required to develop tariffs and evaluate grid impacts of variable tariffs for electric vehicles (BEV) as well as to schedule the charging process optimisation. Therefore a combinatorial optimisation algorithm is developed and an algorithm based on graph search is used and customised. Both algorithms are explained and compared by performance and adequate applications. The developing approach and the correctness of the quick combinatorial algorithm are proved within this paper. For vehicle to grid (V2G) concepts, battery degradation costs have to be considered. Therefore, common life cycle assumptions based on the battery state of charge (SoC) have been used to include degradation costs for different Li-Ion batteries into the graph search algorithm. An application of these optimisation algorithms, like the onboard dispatcher, which is used in the German fleet test "Flottenversuch Elektromobiliät". Grid impact calculations based on the optimisation algorithm are shown. --BEV,V2G,Plug-In-Vehicles (PHEV),optimisation,mobile dispatcher,demand side management,charging,combinatorial algorithm,graph search algorithm,indirect control by variable tariffs

    The Critical Role of Public Charging Infrastructure

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    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
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