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

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  • 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
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    References listed on IDEAS

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    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, April.
    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.
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    Cited by:

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    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).
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    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.

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