IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i22p7574-d677950.html
   My bibliography  Save this article

Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters

Author

Listed:
  • Manasseh Obi

    (Portland General Electric, Portland, OR 97204, USA)

  • Cheryn Metzger

    (Pacific Northwest National Laboratory, Richland, WA 99354, USA)

  • Ebony Mayhorn

    (Pacific Northwest National Laboratory, Richland, WA 99354, USA)

  • Travis Ashley

    (Pacific Northwest National Laboratory, Richland, WA 99354, USA)

  • Walter Hunt

    (Pacific Northwest National Laboratory, Richland, WA 99354, USA)

Abstract

Deployment of CTA-2045–enabled devices is increasing in the U.S. market. These devices allow utilities or third-party aggregators to control appliance energy use in homes, and could also be applied to end uses in small commercial buildings. This study focuses on a field study using CTA-2045–enabled water heaters to shift electric load off the peak and toward periods when renewable resources are more prevalent (e.g., near noon for solar resources and near midnight for wind resources). The following load shifting strategies were compared to understand effects on the aggregate load-shifting capabilities of Heat Pump Water Heaters (HPWHs) and on consumer hot water supply: non-targeted (traditional), targeted (grouped, with different shifting schedules) and “smart” (adaptive control commands). The results of this study show that targeted and smart control strategies yield significantly more load-shifting potential from a population of water heaters than the non-targeted approach without sacrificing hot water supply to occupants. However, as control commands become more aggressive, aggregators may face challenges in meeting consumer hot water demand. The findings and lessons learned can benefit electric utilities and inform updates to manufacturer controls and communications standards. The data collected may also be useful for developing and validating HPWH models.

Suggested Citation

  • Manasseh Obi & Cheryn Metzger & Ebony Mayhorn & Travis Ashley & Walter Hunt, 2021. "Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters," Energies, MDPI, vol. 14(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7574-:d:677950
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/22/7574/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/22/7574/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    2. Ericson, Torgeir, 2009. "Direct load control of residential water heaters," Energy Policy, Elsevier, vol. 37(9), pages 3502-3512, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.
    2. Rosemary E. Alden & Huangjie Gong & Tim Rooney & Brian Branecky & Dan M. Ionel, 2023. "Electric Water Heater Modeling for Large-Scale Distribution Power Systems Studies with Energy Storage CTA-2045 Based VPP and CVR," Energies, MDPI, vol. 16(12), pages 1-22, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    2. Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
    3. Martin Spann & Bernd Skiera, 2020. "Dynamische Preisgestaltung in der digitalisierten Welt [Dynamic Pricing in a Digitized World]," Schmalenbach Journal of Business Research, Springer, vol. 72(3), pages 321-342, September.
    4. Schauf, Andrew & Oh, Poong, 2021. "Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games," SocArXiv w2cxp, Center for Open Science.
    5. Krishnendranath Mitra & Goutam Dutta, 2021. "A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 162-184, April.
    6. Linas Gelažanskas & Kelum A. A. Gamage, 2016. "Distributed Energy Storage Using Residential Hot Water Heaters," Energies, MDPI, vol. 9(3), pages 1-13, February.
    7. Michael J. Ritchie & Jacobus A.A. Engelbrecht & Marthinus J. Booysen, 2021. "Practically-Achievable Energy Savings with the Optimal Control of Stratified Water Heaters with Predicted Usage," Energies, MDPI, vol. 14(7), pages 1-23, April.
    8. Nikolas Schöne & Kathrin Greilmeier & Boris Heinz, 2022. "Survey-Based Assessment of the Preferences in Residential Demand Response on the Island of Mayotte," Energies, MDPI, vol. 15(4), pages 1-30, February.
    9. Correia-da-Silva, João & Soares, Isabel & Fernández, Raquel, 2020. "Impact of dynamic pricing on investment in renewables," Energy, Elsevier, vol. 202(C).
    10. Bertsch, Valentin & Harold, Jason & Fell, Harrison, 2019. "Consumer preferences for end-use specific curtailable electricity contracts on household appliances during peak load hours," Papers WP632, Economic and Social Research Institute (ESRI).
    11. Patrick Ludwig & Christian Winzer, 2022. "Tariff Menus to Avoid Rebound Peaks: Results from a Discrete Choice Experiment with Swiss Customers," Energies, MDPI, vol. 15(17), pages 1-21, August.
    12. Ndiritu, S. Wagura & Engola, Monica Katungi, 2020. "The effectiveness of feed-in-tariff policy in promoting power generation from renewable energy in Kenya," Renewable Energy, Elsevier, vol. 161(C), pages 593-605.
    13. Ericson, Torgeir, 2011. "Households' self-selection of dynamic electricity tariffs," Applied Energy, Elsevier, vol. 88(7), pages 2541-2547, July.
    14. Ritchie, M.J. & Engelbrecht, J.A.A. & Booysen, M.J., 2024. "Loadshedding-induced transients due to battery backup systems and electric water heaters," Applied Energy, Elsevier, vol. 367(C).
    15. Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
    16. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    17. Solji Nam & Jungwoo Shin & Bum Il Hong, 2024. "How consumers value improving energy efficiency policy in the electricity market: A contingent valuation experiment in South Korea," Energy & Environment, , vol. 35(6), pages 3144-3164, September.
    18. Pereira, Diogo Santos & Marques, António Cardoso, 2020. "How should price-responsive electricity tariffs evolve? An analysis of the German net demand case," Utilities Policy, Elsevier, vol. 66(C).
    19. Niranjan Devkota & Anish B. K & Nirash Paija & Udaya Raj Paudel & Udbodh Bhandari, 2022. "Mapping the industries’ willingness to pay for unrestricted electricity supply," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 666-682, January.
    20. Bejan, Ioana & Jensen, Carsten Lynge & Andersen, Laura M. & Hansen, Lars Gårn, 2021. "Inducing flexibility of household electricity demand: The overlooked costs of reacting to dynamic incentives," Applied Energy, Elsevier, vol. 284(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7574-:d:677950. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.