Browsing by Author "Boyd, Gale Allen"

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    HanesBrands Renewable Energy Prioritization
    (2017-04-28) Erdem, Serkan; Feng, Xiaonan; Moss, Joseph; Regan, Liam; Yang, Will
    HanesBrands Inc. (HBI), a world-wide leading apparel manufacturer, has established the sustainability goals of reducing its energy intensity, carbon emission intensity, and increasing their renewable energy use. Duke Team has focused on HBI’s commitment of increasing company-wide renewable energy use to 40% by 2020 through prioritizing renewable energy investment options in their global portfolio. Based on the energy use breakdown & electricity costs of facilities, the Duke Team identified 10 facilities for a deeper dive. In order to better target facilities for the scalable Solar Photovoltaic (PV) & Solar PV + Battery technologies, a utility compilation database with electricity invoices detailing demand, consumption and administrative charges was created. Interval data was used to support the utility database by analyzing the energy consumption profiles of qualifying facilities with the best compatibility. Renewable energy investment options were explored in National Renewable Energy Laboratory’s (NREL) System Advisor Model (SAM) model, with the support of thorough geo-market research. Ultimately, technical & financial feasibility analyses were the basis for recommendations in the areas of  Solar PV & Solar PV+Storage, (Synthetic) Power Purchase Agreements (sPPAs), and RECs that could facilitate HBI’s goal regarding renewables.
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    Price Response in Connecticut Light & Power's Variable Peak Pricing Option
    (2012-04-27) Suzenski, David
    In 2007, the Connecticut General Assembly passed Public Act No. 07-242, An Act Concerning Electricity and Energy Efficiency, requiring electric distribution companies to offer voluntary critical peak pricing or real-time pricing to all customer classes. In 2008, Connecticut Light & Power implemented Variable Peak Pricing (VPP), a rate option that allows time-of-use rate customers to purchase on-peak electricity at daily-varying prices linked to the clearing of New England’s day-ahead wholesale electricity market. This study uses regression analysis to estimate the hourly price elasticity of demand for electricity of the 24 residential VPP customers. Using average hourly load data from 2008 to 2011, hourly electricity demand is modeled as a function of price, temperature, relative humidity, and hourly, weekday and household effects. A modified difference in differences estimation technique is also employed to in an attempt to make causal inference about the “treatment effect” of variable on-peak prices using a larger control group of residential time-of-use rate customers. The fully-specified demand models for the VPP subset indicate that consumers increase their hourly demand when prices increase; in contrast, the difference in differences technique produces a small but statistically significant hourly price elasticity of demand of -0.1. In other words, a 10% increase in hourly price results in an approximately 1% reduction in hourly consumption. This estimate compares favorably with the larger control group, with an estimated price elasticity of demand between -.05 and -0.1. Low observed price elasticities of demand comport with previous research on dynamic pricing pilots indicating that large on- and off-peak price differentials are necessary to encourage load shifting. Causal inference is rendered difficult by the non-experimental nature of this study and the small number of VPP customers. To the extent that the estimates are biased, the results are not generalizable to the residential customer class as a whole, given the voluntary nature of the program.
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    Toyota Motor Energy Analysis and Modeling
    (2016-04-27) Liu, Yang; Sun, Rui; Wang, Ziren
    The automobile manufacturing industry in the U.S. uses over 800 trillion British Thermal Units (Btus) of energy and spends about $3.6 billion on it each year. As a leading automobile manufacturing corporation, our client Toyota Motor Engineering & Manufacturing North America, Inc. (Toyota) seeks opportunities to improve their accuracy in predicting manufacture energy use. With a better understanding of future energy use, Toyota will be able to identify energy efficiency opportunities and improve the overall budgeting accuracy at corporate level. To accomplish this goal, this project uses five years of historical energy consumption data to test existing energy models and identify possible improvements that can be done on a process and plant level. The products of this study include revised energy models for manufacture energy use and administrative energy use for both electricity and total energy consumption. Toyota could use our models as a reliable reference to predict future energy performance.