IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v190y2021i1d10.1007_s10957-021-01876-1.html
   My bibliography  Save this article

Optimizing the Electricity Consumption with a High Degree of Flexibility Using a Dynamic Tariff and Stackelberg Game

Author

Listed:
  • Simona-Vasilica Oprea

    (The Bucharest University of Economic Studies)

  • Adela Bâra

    (The Bucharest University of Economic Studies)

  • George Adrian Ifrim

    (Dunărea de Jos University)

Abstract

Recent advancements in the sensor industry, smart metering systems and communication technology have led to interesting electricity consumption optimization opportunities that contribute to both peak reduction and bill savings and better integration of flexible appliances (including e-mobility). In combination with advanced tariffs, there has been a promising demand side management strategy devised from different perspectives: consumers interested in cost minimization, retailers and grid operators interested in peak minimization or hybrid solutions which reduce the costs to the extent of a certain peak level. In this paper, we propose an optimization algorithm that is significantly enhanced by a Stackelberg-type dynamic nonzero-sum game in which the consumers optimize and send their 24-h consumption schedules to the electricity retailer and receive the hourly tariff rates until their savings and the Flattening Index are maximized. Thus, it has been demonstrated that the one-iteration optimization is not as rewarding as the proposed game-optimization algorithm and that the results are heavily influenced by the degree of flexibility of the appliances. The algorithm is tested and validated using a large real input dataset, recorded at 15-min interval for a period of one year from a small residential community that consists of 11 modern houses with more than 300 appliances and high flexibility in terms of shifting, and the results highlight the consumers’ gain, FI and peak to average ratio indicators.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra & George Adrian Ifrim, 2021. "Optimizing the Electricity Consumption with a High Degree of Flexibility Using a Dynamic Tariff and Stackelberg Game," Journal of Optimization Theory and Applications, Springer, vol. 190(1), pages 151-182, July.
    • Handle: RePEc:spr:joptap:v:190:y:2021:i:1:d:10.1007_s10957-021-01876-1
    DOI: 10.1007/s10957-021-01876-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-021-01876-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10957-021-01876-1?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. Fernandez, Edstan & Hossain, M.J. & Nizami, M.S.H., 2018. "Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources," Applied Energy, Elsevier, vol. 232(C), pages 245-257.
    2. Yang, Liu & Dong, Ciwei & Wan, C.L. Johnny & Ng, Chi To, 2013. "Electricity time-of-use tariff with consumer behavior consideration," International Journal of Production Economics, Elsevier, vol. 146(2), pages 402-410.
    3. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
    4. Aqib Jamil & Turki Ali Alghamdi & Zahoor Ali Khan & Sakeena Javaid & Abdul Haseeb & Zahid Wadud & Nadeem Javaid, 2019. "An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
    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. Li, Jiamei & Ai, Qian & Yin, Shuangrui & Hao, Ran, 2022. "An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game," Applied Energy, Elsevier, vol. 323(C).
    2. Sadeq Neamah Bazoon Alhussein & Roohollah Barzamini & Mohammad Reza Ebrahimi & Shoorangiz Shams Shamsabad Farahani & Mohammad Arabian & Aliyu M. Aliyu & Behnaz Sohani, 2024. "Revolutionizing Demand Response Management: Empowering Consumers through Power Aggregator and Right of Flexibility," Energies, MDPI, vol. 17(6), pages 1-11, March.
    3. Oprea, Simona-Vasilica & Bâra, Adela & Ciurea, Cristian-Eugen, 2022. "A novel cost-revenue allocation computation for the competitiveness of balancing responsible parties, including RES. Insights from the electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 881-894.

    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. Li, Na & Okur, Özge, 2023. "Economic analysis of energy communities: Investment options and cost allocation," Applied Energy, Elsevier, vol. 336(C).
    2. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2021. "Cost allocation in integrated community energy systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2022. "Cost allocation in integrated community energy systems — Performance assessment," Applied Energy, Elsevier, vol. 307(C).
    4. Chen Wang & Kaile Zhou & Lanlan Li & Shanlin Yang, 2018. "Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(3), pages 1309-1327, February.
    5. Lavin, Luke & Apt, Jay, 2021. "The importance of peak pricing in realizing system benefits from distributed storage," Energy Policy, Elsevier, vol. 157(C).
    6. Østergaard, Poul Alberg & Andersen, Anders N., 2021. "Variable taxes promoting district heating heat pump flexibility," Energy, Elsevier, vol. 221(C).
    7. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    8. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    9. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    10. Sulaima, Mohamad Fani & Dahlan, Nofri Yenita & Yasin, Zuhaila Mat & Rosli, Marlinda Mohd & Omar, Zulkiflee & Hassan, Mohammad Yusri, 2019. "A review of electricity pricing in peninsular Malaysia: Empirical investigation about the appropriateness of Enhanced Time of Use (ETOU) electricity tariff," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 348-367.
    11. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    12. Richstein, Jörn C. & Hosseinioun, Seyed Saeed, 2020. "Industrial demand response: How network tariffs and regulation (do not) impact flexibility provision in electricity markets and reserves," Applied Energy, Elsevier, vol. 278(C).
    13. Misconel, Steffi & Zöphel, Christoph & Möst, Dominik, 2021. "Assessing the value of demand response in a decarbonized energy system – A large-scale model application," Applied Energy, Elsevier, vol. 299(C).
    14. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    15. Francesco Simmini & Tommaso Caldognetto & Mattia Bruschetta & Enrico Mion & Ruggero Carli, 2021. "Model Predictive Control for Efficient Management of Energy Resources in Smart Buildings," Energies, MDPI, vol. 14(18), pages 1-19, September.
    16. Parag, Yael, 2021. "Which factors influence large households’ decision to join a time-of-use program? The interplay between demand flexibility, personal benefits and national benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    17. Eid, Cherrelle & Bollinger, L. Andrew & Koirala, Binod & Scholten, Daniel & Facchinetti, Emanuele & Lilliestam, Johan & Hakvoort, Rudi, 2016. "Market integration of local energy systems: Is local energy management compatible with European regulation for retail competition?," Energy, Elsevier, vol. 114(C), pages 913-922.
    18. Jinho Shin & Jihwa Jung & Jaehaeng Heo & Junwoo Noh, 2022. "A Decision-Making Model for Optimized Energy Plans for Buildings Considering Peak Demand Charge—A South Korea Case Study," Energies, MDPI, vol. 15(15), pages 1-22, August.
    19. 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.
    20. Møller Sneum, Daniel, 2021. "Barriers to flexibility in the district energy-electricity system interface – A taxonomy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(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:spr:joptap:v:190:y:2021:i:1:d:10.1007_s10957-021-01876-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.