IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1537-d114079.html
   My bibliography  Save this article

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Author

Listed:
  • Jimyung Kang

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, 2006 Seobu-ro, Jangan-gu, Suwon 440-746, Korea
    Korea Electrotechnology Research Institute, 111 Hanggaul-ro, Sangnok-gu, Ansan 426-910, Korea)

  • Jee-Hyong Lee

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, 2006 Seobu-ro, Jangan-gu, Suwon 440-746, Korea)

Abstract

Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider’s perspective.

Suggested Citation

  • Jimyung Kang & Jee-Hyong Lee, 2017. "Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers," Energies, MDPI, vol. 10(10), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1537-:d:114079
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1537/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1537/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Atkinson, Scott E., 1979. "Responsiveness to time-of-day electricity pricing : First empirical results," Journal of Econometrics, Elsevier, vol. 9(1-2), pages 79-95, January.
    2. Marcel Büther & Dirk Briskorn, 2012. "Reducing the 0-1 Knapsack Problem with a Single Continuous Variable to the Standard 0-1 Knapsack Problem," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 3(1), pages 1-12, January.
    3. Thomas Taylor & Peter Schwarz & James Cochell, 2005. "24/7 Hourly Response to Electricity Real-Time Pricing with up to Eight Summers of Experience," Journal of Regulatory Economics, Springer, vol. 27(3), pages 235-262, January.
    4. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    5. Jimyung Kang & Jee-Hyong Lee, 2015. "Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications," Energies, MDPI, vol. 8(10), pages 1-24, October.
    6. Fotouhi Ghazvini, Mohammad Ali & Faria, Pedro & Ramos, Sergio & Morais, Hugo & Vale, Zita, 2015. "Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market," Energy, Elsevier, vol. 82(C), pages 786-799.
    7. Hung-po Chao, 2011. "Demand response in wholesale electricity markets: the choice of customer baseline," Journal of Regulatory Economics, Springer, vol. 39(1), pages 68-88, February.
    8. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, September.
    9. Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
    10. Gao, Dian-ce & Sun, Yongjun & Lu, Yuehong, 2015. "A robust demand response control of commercial buildings for smart grid under load prediction uncertainty," Energy, Elsevier, vol. 93(P1), pages 275-283.
    11. 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)

    Citations

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


    Cited by:

    1. Zishan Guo & Zhenya Ji & Qi Wang, 2020. "Blockchain-Enabled Demand Response Scheme with Individualized Incentive Pricing Mode," Energies, MDPI, vol. 13(19), pages 1-17, October.
    2. 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).
    3. Tope Roseline Olorunfemi & Nnamdi I. Nwulu, 2021. "Multi-Agent Based Optimal Operation of Hybrid Energy Sources Coupled with Demand Response Programs," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    4. Jimyung Kang & Soonwoo Lee, 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System," Energies, MDPI, vol. 11(11), pages 1-14, October.
    5. Venkat Durvasulu & Timothy M. Hansen, 2018. "Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets," Energies, MDPI, vol. 11(12), pages 1-21, December.

    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. Jimyung Kang & Soonwoo Lee, 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System," Energies, MDPI, vol. 11(11), pages 1-14, October.
    2. 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.
    3. Duy Phuc Le & Duong Minh Bui & Cao Cuong Ngo & Anh My Thi Le, 2018. "FLISR Approach for Smart Distribution Networks Using E-Terra Software—A Case Study," Energies, MDPI, vol. 11(12), pages 1-33, November.
    4. Herter, Karen & Wayland, Seth, 2010. "Residential response to critical-peak pricing of electricity: California evidence," Energy, Elsevier, vol. 35(4), pages 1561-1567.
    5. Botelho, D.F. & de Oliveira, L.W. & Dias, B.H. & Soares, T.A. & Moraes, C.A., 2022. "Prosumer integration into the Brazilian energy sector: An overview of innovative business models and regulatory challenges," Energy Policy, Elsevier, vol. 161(C).
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. Moradi-Dalvand, M. & Mohammadi-Ivatloo, B. & Amjady, N. & Zareipour, H. & Mazhab-Jafari, A., 2015. "Self-scheduling of a wind producer based on Information Gap Decision Theory," Energy, Elsevier, vol. 81(C), pages 588-600.
    11. Sara Haghifam & Kazem Zare & Mehdi Abapour & Gregorio Muñoz-Delgado & Javier Contreras, 2020. "A Stackelberg Game-Based Approach for Transactive Energy Management in Smart Distribution Networks," Energies, MDPI, vol. 13(14), pages 1-34, July.
    12. S. Muhammad Bagher Sadati & Jamal Moshtagh & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Risk-Based Bi-Level Model for Simultaneous Profit Maximization of a Smart Distribution Company and Electric Vehicle Parking Lot Owner," Energies, MDPI, vol. 10(11), pages 1-16, October.
    13. David P. Brown & David E. M. Sappington, 2017. "Optimal policies to promote efficient distributed generation of electricity," Journal of Regulatory Economics, Springer, vol. 52(2), pages 159-188, October.
    14. 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.
    15. Ghasemi, Ahmad & Jamshidi Monfared, Houman & Loni, Abdolah & Marzband, Mousa, 2021. "CVaR-based retail electricity pricing in day-ahead scheduling of microgrids," Energy, Elsevier, vol. 227(C).
    16. Makena Coffman & Paul Bernstein & Sherilyn Wee & Aida Arik, 2016. "Estimating the Opportunity for Load-Shifting in Hawaii: An Analysis of Proposed Residential Time-of-Use Rates," Working Papers 2016-10, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    17. Saman Nikkhah & Adib Allahham & Janusz W. Bialek & Sara L. Walker & Damian Giaouris & Simira Papadopoulou, 2021. "Active Participation of Buildings in the Energy Networks: Dynamic/Operational Models and Control Challenges," Energies, MDPI, vol. 14(21), pages 1-28, November.
    18. Alasseri, Rajeev & Rao, T. Joji & Sreekanth, K.J., 2020. "Institution of incentive-based demand response programs and prospective policy assessments for a subsidized electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    19. Kazutoshi Tsuda & Michinori Uwasu & Keishiro Hara & Yukari Fuchigami, 2017. "Approaches to induce behavioral changes with respect to electricity consumption," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 7(1), pages 30-38, March.
    20. Lee, Kyungeun & Lee, Hyesu & Lee, Hyoseop & Yoon, Yoonjin & Lee, Eunjung & Rhee, Wonjong, 2018. "Assuring explainability on demand response targeting via credit scoring," Energy, Elsevier, vol. 161(C), pages 670-679.

    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:10:y:2017:i:10:p:1537-:d:114079. 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.