research-article

EVHomeShifter: evaluating intelligent techniques for using electrical vehicle batteries to shift when homes draw energy from the grid

Authors:

A. J. Bernheim Brush,

Shwetak PatelAuthors Info & Claims

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 1077 - 1088

https://doi.org/10.1145/2750858.2804274

Published: 07 September 2015 Publication History

Abstract

Time of use tiered pricing schedules encourage shifting electricity demand from peak to off-peak hours. Charging times for electric vehicles (EV) can be shifted into overnight hours, which are usually off-peak. EVs can also be used as energy storage devices, available during certain peak hours to power a house with electricity stored during off-peak hours. Studies suggest both techniques are practical, but were based on simulated demand patterns or large commercial fleets. To investigate feasibility on a per home basis, we collected data from 15 EV homes using the Lab of Things sensing infrastructure. We evaluate a scheme that powers homes with their car battery during expensive electricity periods and then charges the battery during cheaper periods. We show an average potential savings of $10.91/month for shifting charging times, and an additional $13.58/month for powering the home from the EV, even accounting for the inefficiencies of electric conversion.

References

[1]

Ahmadi, L, Fowler, M, Young, S., Fraser, R., Gaffney, Walker, S., Sustainable Energy Technologies and Assessments, 8, Dec. 2014, 9--17.

[2]

Ardakanian, O., Rosenberg, C., Keshav, S., Distributed Control of Electric Vehicle Charging, In Proc. e-Energy 2013, ACM (2013).

Digital Library

[3]

Aumi, M., Gupta, S., Pickett, C., Reynolds, M., Patel, S., A Self-Calibrating Approach to Whole-Home Contactless Power Consumption Sensing. In Proc. UbiComp 2014, ACM (2014) 361--371.

Digital Library

[4]

Brush, A, Jung, J., Mahajan, R., Scott, J., HomeLab: Shared Infrastructure for Home Technology Field Studies, HomeSys Workshop at Ubicomp 2012, Proc. Ubicomp 2012, ACM (2012) 1108--1113.

Digital Library

[5]

California ISO, What the duck curve tells us about managing a green grid, http://www.caiso.com/Documents/FlexibleResourcesHelpRenewables_FastFacts.pdf

[6]

California Plug-in Electric Vehicle Owner Survey, Center For Sustainable Energy, July 2012, http://energycenter.org/clean-vehicle-rebate-project/vehicle-owner-survey/july-2012-survey

[7]

Clear, A., Morely, J., Hazas, M., Friday, A., Bates, O., Understanding Adaptive Thermal Comfort: New Directions for Ubicomp. In Proc UbiComp 2013, ACM (2013) 113--122.

Digital Library

[8]

Davies-Shawhyde, J. and Kurani. K, Variation in Charging of Privately-Held PEVs: Implications for Analysis, Markets, and Policy. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-13-09, 2013.

[9]

Dillahunt, T., Mankoff, J., Paulos, E., Understanding conflict between landlords and tenants: implications for energy sensing and feedback. In Proc. UbiComp 2010, ACM (2010) 149--158.

Digital Library

[10]

Dillahunt, T., Mankoff, J., Understanding factors of successful engagement around energy consumption between and among households. In Proc. CSCW 2014. ACM (2014) 1246--1257.

Digital Library

[11]

Dixon, C., Mahajan, R., Agarwal, S., Brush. A., Lee, B., Saroiu, S., Bahl, P., An Operating System for the Home, In Proc. NSDI 2012.

Digital Library

[12]

EVWorld, Toyota Preparing Vehicle-to-Home Backup Power System. Published June 8, 2012, http://evworld.com/news.cfm?newsid=28088.

[13]

Froehlich, J., Findlater, L, Landay, J., The Design of Eco-Feedback Technology, In Proc. CHI 2010, ACM (2010).

Digital Library

[14]

Gan, L, U. Topcu, U., Low. S., Optimal decentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems, 28, 2. 940--951, 2013.

[15]

Gerding, E., Robu, V., Stein, S., Parkes, D., Rogers, A., Jennings, A., Online mechanism design for electric vehicle charging. In Proc. of AAMAS, 2011.

Digital Library

[16]

Grid Energy Storage, U. S. Department of Energy, December 2013. http://energy.gov/oe/downloads/grid-energy-storage-december-2013

[17]

Han, S., Han S., Sezaki, K. Estimation of Achievable Power Capacity From Plug-in Electric Vehicles for V2G Frequency Regulation: Case Studies for Market Participation. IEEE Transactions on Smart Grid, 2, 4. 2011.

[18]

Hnat, T., Srinivasan, V., Lu, J., Sookoor, T., Dawson, R., Stankovic, J., Whitehouse, K., The Hitchhiker's Guide to Successful Residential Sensing Deployments. In Proc. SenSys 2011, ACM (2011).

Digital Library

[19]

Kalogridis, G., Efthymiou, C., Denic, S., Lews, T., Cepeda, R., Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures. In Proc. SmartGridComm 2010, IEEE (2010), 232--237.

[20]

Kamboj, S., Kempton, W., Decker, K. Deploying Power Grid-Integrated Electric Vehicles as a Multi-Agent System. In Proc. AAMAS 2011.

Digital Library

[21]

Kempton, W., and Tomić. J. Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Power Sources, 144, 1, (2005), 268--279.

[22]

Kempton, W., and Tomić, J. Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. Power Sources, 144, 1 (2005), 280--294.

[23]

Kurani, K., TyreeHageman, J., Caperello N., Potential Consumer Response to Electricity Demand Response Mechanisms: Early Plug-in Electric Vehicle Drivers in San Diego, California. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-13-12, 2013.

[24]

Lab of Things, Microsoft Research, http://www.lab-of-things.com/

[25]

Lab of Things Community, http://www.lab-of-things.com/community.html

[26]

Lu, J., Sookoor, T., Srinivasan, V., Ge., G, Holben, B., Stankovic, J., Field, E., Whitehouse, K. The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes. In Proc SenSys 2010, ACM (2010).

Digital Library

[27]

Ma, Z., Callaway, D., Hiskens. I., Decentralized charging control of large populations of plug-in electric vehicles. In Proc. CDC 2010, IEEE 206--212, 2010

[28]

Misha, A., Irwin, D., Shenoy, P., Zhu, T., Scaling Distributed Energy Storage for Peak Demand, In Proc. e-Energy 2013, ACM (2013).

Digital Library

[29]

Mishra, A., Irwin, D., Shenoy, P., Kurose, J., Zhu, T., SmartCharge: Cutting the Electricity Bill in Smart Homes with Energy Storage, In Proc. e-Energy 2012 ACM (2012).

Digital Library

[30]

Nest Energy Partner website, https://nest.com/energy-partners/

[31]

Nicanfar, H., TalebiFard, P., Hosseininezhad, S., Leung, V., Damm, M., Security and Privacy of Electric Vehicles in the Smart Grid Context: Problem and Solution, In Proc. DIVANet '13, ACM (2013).

Digital Library

[32]

Pecan Street, http://www.pecanstreet.org/about/

[33]

Pedrasa, A., Spooner, T., MacGill, I., Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Transactions on Smart Grid, 1, 2, (2010) 134.--143

[34]

Popper, B., Elon Musk says Telsa will unveil a new kind of battery to power your home. The Verge, Published Feb. 11, 2015, http://www.theverge.com/2015/2/11/8023443/tesla-home-consumer-battery-elon-musk

[35]

Riofrio, M., Here's everything Toyota will give you if you buy the hydrogen-power Mirai, PCWorld, Published Jan. 6, 2015. http://www.pcworld.com/article/2863411/heres-everything-toyota-will-give-you-if-you-buy-the-hydrogen-powered-mirai.html

[36]

Scott, J., Brush, A., Krumm, J., Meyers, B., Hazas, M., Hodges, S., Villar, N. PreHeat: controlling home heating using occupancy prediction. In Proc. UbiComp 2011. ACM (2011), 281--290.

Digital Library

[37]

Sortomme, E., Hindi, M., MacPherson, S., Venkata, S., Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses. IEEE Trans. Smart Grid, 2, 1, 198--205, 2011

[38]

Newcombe, C., Nissan Begins Testing V2H Technology with Leaf-to-Home Charging Units. The AutoFuture, Published Nov. 1, 2014. http://www.theautofuture.com/2014/11/01/nissan-testing-v2h-technology-leaf-home-charging-units/

[39]

Sen, S., Joe-Wong, C., Ha, S., Bawa, J., Chiang, M., When the price is right: enabling time-dependent pricing of broadband data. In Proc. CHI 2013. ACM (2013) 2477--2486.

Digital Library

[40]

Tomić. J. and Kempton W., Using fleets of electric-drive vehicles for Grid Support. Power Sources (2007) 168, 20, 459--468.

[41]

Tuttle, D., Fares, R., Baldick R., Webber, M. Plug-In Vehicle to Home (V2H) Duration and Power Output Capability. In Proc. ITEC 2013, IEEE Press (2013).

[42]

U.S. Department of Transportation, Federal Highway Administration, 2009 National Household Travel Survey. http://nhts.ornl.gov

[43]

University of Delaware Grid-Integrated Vehicle Group, http://www.udel.edu/V2G/ArticlesandPapers.html.

[44]

WattTime, http://www.watttime.org/

Cited By

Zhang LZheng DYuan MHan FWu ZLiu MLi X(2023)MultiSense: Cross-labelling and Learning Human Activities Using Multimodal Sensing DataACM Transactions on Sensor Networks10.1145/357826719:3(1-26)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1145/3578267
Wang GChen YWang SZhang FZhang D(2023)ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi NetworksACM Transactions on Sensor Networks10.1145/357095819:3(1-25)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3570958
Cao ZZheng XMa QMiao X(2023)COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle NetworksACM Transactions on Sensor Networks10.1145/357016319:3(1-21)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1145/3570163
Show More Cited By

Index Terms

EVHomeShifter: evaluating intelligent techniques for using electrical vehicle batteries to shift when homes draw energy from the grid
1. Applied computing
  1. Computers in other domains
  2. Operations research
    1. Industry and manufacturing
      1. Command and control

Recommendations

Driving on sunshine: aligning electric vehicle charging and household electricity production
NordiCHI '18: Proceedings of the 10th Nordic Conference on Human-Computer Interaction

Electric vehicle seems to go well together with the growing societal trend of becoming more self-supplying with renewable electricity produced in the household. However, aligning household electricity production and electric vehicle charging have ...
Coordination Dispatch of Electric Vehicles Charging/Discharging and Renewable Energy Resources Power in Microgrid

A large number of distributed powers access to the distribution network have a multifaceted impact on the power grid, while microgrid is an effective way to solve the impact of distributed generation on the power grid. In microgrid, the proportion of ...
Genetic Algorithm for Optimal Charge Scheduling of Electric Vehicle Fleet
NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security

Electric Vehicles (EV) are gradually conquering more roads and replacing pollutant conventional vehicles. They seem to be used to store energy in clean and smart grids to mitigate greenhouse gas emissions and eliminate harmful peak loads. This paper ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2015

1302 pages

ISBN:9781450335744

DOI:10.1145/2750858

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Yahoo! Japan: Yahoo! Japan
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
FX Palo Alto Laboratory, Inc.: FX Palo Alto Laboratory, Inc.
Google Inc.
ACM: Association for Computing Machinery
Rakuten Institute of Technology: Rakuten Institute of Technology
Microsoft: Microsoft
Bell Labs: Bell Laboratories
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
KDDI
Panasonic: Panasonic Corporation
NTT DoCoMo
Telefónica: Telefónica
ISTC-PC: Intel Science and Technology Center for Pervasive Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '15

Sponsor:

Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

Acceptance Rates

UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Upcoming Conference

UBICOMP '24

Sponsor:
sigchi
sigchi

The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

Melbourne , VIC , Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
314
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang LZheng DYuan MHan FWu ZLiu MLi X(2023)MultiSense: Cross-labelling and Learning Human Activities Using Multimodal Sensing DataACM Transactions on Sensor Networks10.1145/357826719:3(1-26)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1145/3578267
Wang GChen YWang SZhang FZhang D(2023)ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi NetworksACM Transactions on Sensor Networks10.1145/357095819:3(1-25)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3570958
Cao ZZheng XMa QMiao X(2023)COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle NetworksACM Transactions on Sensor Networks10.1145/357016319:3(1-21)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1145/3570163
Adil MAli JJadoon MAlotaibi SKumar NFarouk ASong H(2023)COVID-19: Secure Healthcare Internet of Things Networks, Current Trends and Challenges with Future Research DirectionsACM Transactions on Sensor Networks10.1145/355851919:3(1-25)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3558519
Chen MChiang P(2023)COVID-19 Diagnosis System Based on Chest X-ray Images Using Optimized Convolutional Neural NetworkACM Transactions on Sensor Networks10.1145/355809819:3(1-22)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3558098
Lee MCarter SIliev RBravo NVan MDenoue LKimani EFilipowicz AShamma DSieck KHogan CWu C(2023)Understanding People’s Perception and Usage of Plug-in Electric HybridsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581301(1-21)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581301
Hasselqvist HRenström SStrömberg HHåkansson M(2022)Household energy resilience: Shifting perspectives to reveal opportunities for renewable energy futures in affluent contextsEnergy Research & Social Science10.1016/j.erss.2022.10249888(102498)Online publication date: Jun-2022
https://doi.org/10.1016/j.erss.2022.102498
Svangren MJensen RSkov MKjeldskov JBratteteig TSandnes FBerget G(2018)Driving on sunshineProceedings of the 10th Nordic Conference on Human-Computer Interaction10.1145/3240167.3240179(439-451)Online publication date: 29-Sep-2018
https://dl.acm.org/doi/10.1145/3240167.3240179
Jensen RKjeldskov JSkov M(2018)Assisted Shifting of Electricity UseACM Transactions on Computer-Human Interaction10.1145/321031025:5(1-33)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3210310
Jensen RRaptis DKjeldskov JSkov MKoskinen ILim YCerratto-Pargman TChow KOdom W(2018)Washing with the WindProceedings of the 2018 Designing Interactive Systems Conference10.1145/3196709.3196779(1387-1400)Online publication date: 8-Jun-2018
https://dl.acm.org/doi/10.1145/3196709.3196779
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents