IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v126y2014icp297-306.html
   My bibliography  Save this article

Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response

Author

Listed:
  • Zheng, Menglian
  • Meinrenken, Christoph J.
  • Lackner, Klaus S.

Abstract

Demand response (DR) is one of many approaches to address temporal mismatches in demand and supply of grid electricity. More common in the commercial sector, DR usually refers to reducing consumption at certain hours or seasons, thus reducing peak demand from the grid. In the residential sector, where sophisticated appliance-level controls such as automatic dimming of lights or on-demand lowering of air conditioning are less common, building-based electricity storage to shift grid consumption from peak to off-peak times could provide DR without requiring consumers to operate their appliances on shifted or reduced schedules: Storage would be dispatched to appliances as needed while still shaving peaks on the grid. Technologically, storage and two-way-inverters are readily available to enable such residential DR. Economically, however, the situation is less clear. Specifically, are time-varying electricity tariffs available such that electricity cost reduction via arbitrage could offset manufacturing, financing, and installation costs of the required storage? To address this question we (i) devise an agent-based appliance-level stochastic model to simulate the electricity demand of an average U.S. household; (ii) loadshift the demand via simple dispatch strategies; and (iii) determine potential profits to the building owner, i.e. reduced electricity cost of the modified demand with realistic tariffs (Con Edison, NY) minus storage cost. We determine the economic viability for a range of traditional and advanced storage technologies as well as their optimum storage capacities to maximize profits. We find that (i) profits can range from <1% to 48% of annual electricity costs of a typical household; and (ii) optimum capacities, while approximately equal to households’ kWh consumption during peak hours, is affected by stochastic variations in daily and seasonal consumption. Future improvements to storage technology, arbitrage strategies, and tariffs are discussed. Details of the storage technologies, agent-based model, testing, and benchmarking are supplied as Supplementary Data.

Suggested Citation

  • Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    • Handle: RePEc:eee:appene:v:126:y:2014:i:c:p:297-306
    DOI: 10.1016/j.apenergy.2014.04.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914003651
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2014.04.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
    2. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    3. Douglas W. Caves & Joseph A. Herriges & Kathleen A. Kuester, 1989. "Load Shifting Under Voluntary Residential Time-of-Use Rates," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 83-99.
    4. Walawalkar, Rahul & Fernands, Stephen & Thakur, Netra & Chevva, Konda Reddy, 2010. "Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO," Energy, Elsevier, vol. 35(4), pages 1553-1560.
    5. Wright, Andrew & Firth, Steven, 2007. "The nature of domestic electricity-loads and effects of time averaging on statistics and on-site generation calculations," Applied Energy, Elsevier, vol. 84(4), pages 389-403, April.
    6. Moura, Pedro S. & de Almeida, Aníbal T., 2010. "The role of demand-side management in the grid integration of wind power," Applied Energy, Elsevier, vol. 87(8), pages 2581-2588, August.
    7. M. Armand & J.-M. Tarascon, 2008. "Building better batteries," Nature, Nature, vol. 451(7179), pages 652-657, February.
    8. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
    9. Defeuilley, Christophe, 2009. "Retail competition in electricity markets," Energy Policy, Elsevier, vol. 37(2), pages 377-386, February.
    10. Wissner, Matthias, 2011. "The Smart Grid - A saucerful of secrets?," Applied Energy, Elsevier, vol. 88(7), pages 2509-2518, July.
    11. Lunz, Benedikt & Yan, Zexiong & Gerschler, Jochen Bernhard & Sauer, Dirk Uwe, 2012. "Influence of plug-in hybrid electric vehicle charging strategies on charging and battery degradation costs," Energy Policy, Elsevier, vol. 46(C), pages 511-519.
    12. Dlamini, Ndumiso G. & Cromieres, Fabien, 2012. "Implementing peak load reduction algorithms for household electrical appliances," Energy Policy, Elsevier, vol. 44(C), pages 280-290.
    13. Baker, John, 2008. "New technology and possible advances in energy storage," Energy Policy, Elsevier, vol. 36(12), pages 4368-4373, December.
    14. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    15. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    Full references (including those not matched with items on IDEAS)

    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. Alasseri, Rajeev & Tripathi, Ashish & Joji Rao, T. & Sreekanth, K.J., 2017. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 617-635.
    2. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    3. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    4. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    5. Schroeder, Andreas, 2011. "Modeling storage and demand management in power distribution grids," Applied Energy, Elsevier, vol. 88(12), pages 4700-4712.
    6. Stötzer, Martin & Hauer, Ines & Richter, Marc & Styczynski, Zbigniew A., 2015. "Potential of demand side integration to maximize use of renewable energy sources in Germany," Applied Energy, Elsevier, vol. 146(C), pages 344-352.
    7. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    8. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    9. Gallo, A.B. & Simões-Moreira, J.R. & Costa, H.K.M. & Santos, M.M. & Moutinho dos Santos, E., 2016. "Energy storage in the energy transition context: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 800-822.
    10. Carreiro, Andreia M. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2017. "Energy management systems aggregators: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1160-1172.
    11. Li, Lanlan & Gong, Chengzhu & Wang, Deyun & Zhu, Kejun, 2013. "Multi-agent simulation of the time-of-use pricing policy in an urban natural gas pipeline network: A case study of Zhengzhou," Energy, Elsevier, vol. 52(C), pages 37-43.
    12. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    13. 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.
    14. Khan, Agha Salman M. & Verzijlbergh, Remco A. & Sakinci, Ozgur Can & De Vries, Laurens J., 2018. "How do demand response and electrical energy storage affect (the need for) a capacity market?," Applied Energy, Elsevier, vol. 214(C), pages 39-62.
    15. Bae, Mungyu & Kim, Hwantae & Kim, Eugene & Chung, Albert Yongjoon & Kim, Hwangnam & Roh, Jae Hyung, 2014. "Toward electricity retail competition: Survey and case study on technical infrastructure for advanced electricity market system," Applied Energy, Elsevier, vol. 133(C), pages 252-273.
    16. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    17. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    18. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    19. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    20. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.

    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:eee:appene:v:126:y:2014:i:c:p:297-306. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.