Impact of uncoordinated plug-in electric vehicle charging on residential power demand - supplementary data

This file includes electricity demand profiles for 200 households randomly selected among the ones available in the 2009 RECS data set for the Midwest region of the United States. The profiles have been generated using the modeling proposed by Muratori et al. [2], [3], that produces realistic patterns of residential power consumption, validated using metered data, with a resolution of 10 minutes. Households vary in size and number of occupants and the profiles represent total electricity use, in watts. This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Please cite as: "Matteo Muratori, Impact of uncoordinated plug-in electric vehicle charging on residential power demand - supplementary data, 2017 [1]. Based on modeling reported in [2] and [3]." [1] M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Forthcoming. [2] M. Muratori, M. C. Roberts, R. Sioshansi, V. Marano, and G. Rizzoni, "A highly resolved modeling technique to simulate residential power demand, "Applied Energy, vol. 107, no. 0, pp. 465 - 473, 2013. [3] M. Muratori, V. Marano, R. Sioshansi, and G. Rizzoni, "Energy consumption of residential HVAC systems: a simple physically-based model," in 2012 IEEE Power and Energy Society General Meeting. San Diego, CA, USA: IEEE, 22-26 July 2012. The author would like to thank G. Rizzoni, M. Moran, R. Sioshansi, B.-A. Schuelke-Leech, and M. Roberts for their contributions on this research and J. Eichman and A. Meintz for their valuable comments. Results are based upon modeling work supported by the National Science Foundation under Grant No. 1029337. This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with Alliance for Sustainable Energy, LLC, the Manager and Operator of the National Renewable Energy Laboratory. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. The views and opinions expressed in this paper are those of the author alone.

This file includes in-home plug-in electric vehicle recharging profiles for 348 vehicles associated with 200 households randomly selected among the ones available in the 2009 RECS data set for the Midwest region of the United States. The profiles have been generated using the modeling proposed by Muratori et al. [2], that produces real-world recharging demand profiles, with a resolution of 10 minutes. Vehicles are assumed to be 60% battery electric vehicles with 200 miles range and 40% plug-in hybrid electric vehicles with 40 mile all-electric range. Level 1 charging (1920 W) is assumed and the profiles represent total plug-in electric vehicle charging demand, in watts. This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Please cite as: "Matteo Muratori, Impact of uncoordinated plug-in electric vehicle charging on residential power demand - supplementary data, 2017 [1]. Based on modeling reported in [2]." [1] M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Forthcoming. [2] M. Muratori, M. J. Moran, E. Serra, and G. Rizzoni, "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, vol. 58, no. 0, pp. 168 - 177, 2013. The author would like to thank G. Rizzoni, M. Moran, R. Sioshansi, B.-A. Schuelke-Leech, and M. Roberts for their contributions on this research and J. Eichman and A. Meintz for their valuable comments. Results are based upon modeling work supported by the National Science Foundation under Grant No. 1029337. This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with Alliance for Sustainable Energy, LLC, the Manager and Operator of the National Renewable Energy Laboratory. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. The views and opinions expressed in this paper are those of the author alone.

This file includes in-home plug-in electric vehicle recharging profiles for 348 vehicles associated with 200 households randomly selected among the ones available in the 2009 RECS data set for the Midwest region of the United States. The profiles have been generated using the modeling proposed by Muratori et al. [2], that produces real-world recharging demand profiles, with a resolution of 10 minutes. Vehicles are assumed to be 60% battery electric vehicles with 200 miles range and 40% plug-in hybrid electric vehicles with 40 mile all-electric range. Level 2 charging (6600 W) is assumed and the profiles represent total plug-in electric vehicle charging demand, in watts. This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Please cite as: "Matteo Muratori, Impact of uncoordinated plug-in electric vehicle charging on residential power demand - supplementary data, 2017 [1]. Based on modeling reported in [2]." [1] M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Forthcoming. [2] M. Muratori, M. J. Moran, E. Serra, and G. Rizzoni, "Highly-resolved modeling of personal transportation energy consumption in the United States, " Energy, vol. 58, no. 0, pp. 168 - 177, 2013. The author would like to thank G. Rizzoni, M. Moran, R. Sioshansi, B.-A. Schuelke-Leech, and M. Roberts for their contributions on this research and J. Eichman and A. Meintz for their valuable comments. Results are based upon modeling work supported by the National Science Foundation under Grant No. 1029337. This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with Alliance for Sustainable Energy, LLC, the Manager and Operator of the National Renewable Energy Laboratory. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. The views and opinions expressed in this paper are those of the author alone.