IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v6y2013i1p425-443d22919.html
   My bibliography  Save this article

Decisions on Energy Demand Response Option Contracts in Smart Grids Based on Activity-Based Costing and Stochastic Programming

Author

Listed:
  • Seog-Chan Oh

    (General Motors, 30500 Mound Road, Warren, MI 48090, USA)

  • Alfred J. Hildreth

    (General Motors, 30009 Van Dyke, Warren, MI 48090, USA)

Abstract

Smart grids enable a two-way energy demand response capability through which a utility company offers its industrial customers various call options for energy load curtailment. If a customer has the capability to accurately determine whether to accept an offer or not, then in the case of accepting an offer, the customer can earn both an option premium to participate, and a strike price for load curtailments if requested. However, today most manufacturing companies lack the capability to make the correct contract decisions for given offers. This paper proposes a novel decision model based on activity-based costing (ABC) and stochastic programming, developed to accurately evaluate the impact of load curtailments and determine as to whether or not to accept an energy load curtailment offer. The proposed model specifically targets state-transition flexible and Quality-of-Service (QoS) flexible energy use activities to reduce the peak energy demand rate. An illustrative example with the proposed decision model under a call-option based energy demand response scenario is presented. As shown from the example results, the proposed decision model can be used with emerging smart grid opportunities to provide a competitive advantage to the manufacturing industry.

Suggested Citation

  • Seog-Chan Oh & Alfred J. Hildreth, 2013. "Decisions on Energy Demand Response Option Contracts in Smart Grids Based on Activity-Based Costing and Stochastic Programming," Energies, MDPI, vol. 6(1), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:1:p:425-443:d:22919
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/6/1/425/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/6/1/425/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. João Soares & Bruno Canizes & Cristina Lobo & Zita Vale & Hugo Morais, 2012. "Electric Vehicle Scenario Simulator Tool for Smart Grid Operators," Energies, MDPI, vol. 5(6), pages 1-19, June.
    2. Jaeyeong Yoo & Byungsung Park & Kyungsung An & Essam A. Al-Ammar & Yasin Khan & Kyeon Hur & Jong Hyun Kim, 2012. "Look-Ahead Energy Management of a Grid-Connected Residential PV System with Energy Storage under Time-Based Rate Programs," Energies, MDPI, vol. 5(4), pages 1-19, April.
    3. Suvrajeet Sen & Julia L. Higle, 1999. "An Introductory Tutorial on Stochastic Linear Programming Models," Interfaces, INFORMS, vol. 29(2), pages 33-61, April.
    4. A. Charnes & W. W. Cooper, 1963. "Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints," Operations Research, INFORMS, vol. 11(1), pages 18-39, February.
    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. Richard P. van Leeuwen & Annelies E. Boerman & Edmund W. Schaefer & Gerwin Hoogsteen & Yashar S. Hajimolana, 2022. "Model Supported Business Case Scenario Analysis for Decentral Hydrogen Conversion, Storage and Consumption within Energy Hubs," Energies, MDPI, vol. 15(6), pages 1-22, March.
    2. Wen-Hsien Tsai, 2018. "A Green Quality Management Decision Model with Carbon Tax and Capacity Expansion under Activity-Based Costing (ABC)—A Case Study in the Tire Manufacturing Industry," Energies, MDPI, vol. 11(7), pages 1-30, July.
    3. Ying Yu & Tongdan Jin & Chunjie Zhong, 2015. "Designing an Incentive Contract Menu for Sustaining the Electricity Market," Energies, MDPI, vol. 8(12), pages 1-22, December.
    4. Seog-Chan Oh & Alfred J. Hildreth, 2014. "Estimating the Technical Improvement of Energy Efficiency in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches," Energies, MDPI, vol. 7(9), pages 1-27, September.
    5. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.

    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. Mike G. Tsionas & Dionisis Philippas & Constantin Zopounidis, 2023. "Exploring Uncertainty, Sensitivity and Robust Solutions in Mathematical Programming Through Bayesian Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 205-227, June.
    2. Meryem Masmoudi & Fouad Ben Abdelaziz, 2018. "Portfolio selection problem: a review of deterministic and stochastic multiple objective programming models," Annals of Operations Research, Springer, vol. 267(1), pages 335-352, August.
    3. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    4. Sarhadi, Hassan & Naoum-Sawaya, Joe & Verma, Manish, 2020. "A robust optimization approach to locating and stockpiling marine oil-spill response facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    5. Hang Li & Zhe Zhang & Xianggen Yin & Buhan Zhang, 2020. "Preventive Security-Constrained Optimal Power Flow with Probabilistic Guarantees," Energies, MDPI, vol. 13(9), pages 1-13, May.
    6. Wu, Desheng (Dash) & Lee, Chi-Guhn, 2010. "Stochastic DEA with ordinal data applied to a multi-attribute pricing problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1679-1688, December.
    7. Giada Spaccapanico Proietti & Mariagiulia Matteucci & Stefania Mignani & Bernard P. Veldkamp, 2024. "Chance-Constrained Automated Test Assembly," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 92-120, February.
    8. Guigues, Vincent & Juditsky, Anatoli & Nemirovski, Arkadi, 2021. "Constant Depth Decision Rules for multistage optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 295(1), pages 223-232.
    9. Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.
    10. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    11. M Kumral, 2011. "Incorporating geo-metallurgical information into mine production scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 60-68, January.
    12. Sandoval, Diego & Goffin, Philippe & Leibundgut, Hansjürg, 2017. "How low exergy buildings and distributed electricity storage can contribute to flexibility within the demand side," Applied Energy, Elsevier, vol. 187(C), pages 116-127.
    13. Gregory A. Godfrey & Warren B. Powell, 2001. "An Adaptive, Distribution-Free Algorithm for the Newsvendor Problem with Censored Demands, with Applications to Inventory and Distribution," Management Science, INFORMS, vol. 47(8), pages 1101-1112, August.
    14. Mahdi Takalloo & Changhyun Kwon, 2019. "On the Price of Satisficing in Network User Equilibria," Papers 1911.07914, arXiv.org.
    15. S. N. Gupta & A. K. Jain & Kanti Swarup, 1987. "Stochastic linear fractional programming with the ratio of independent Cauchy variates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(2), pages 293-305, April.
    16. Zervopoulos, Panagiotis & Emrouznejad, Ali & Sklavos, Sokratis, 2019. "A Bayesian approach for correcting bias of data envelopment analysis estimators," MPRA Paper 91886, University Library of Munich, Germany.
    17. R. Caballero & E. Cerda & M. Muñoz & L. Rey, 2002. "Analysis and comparisons of some solution concepts for stochastic programming problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 101-123, June.
    18. Stephan Nagl & Michaela Fürsch & Dietmar Lindenberger, 2013. "The Costs of Electricity Systems with a High Share of Fluctuating Renewables: A Stochastic Investment and Dispatch Optimization Model for Europe," The Energy Journal, , vol. 34(4), pages 151-180, October.
    19. Longsheng Sun & Mark H. Karwan & Changhyun Kwon, 2018. "Generalized Bounded Rationality and Robust Multicommodity Network Design," Operations Research, INFORMS, vol. 66(1), pages 42-57, 1-2.
    20. Lin, Sheng-Wei & Lu, Wen-Min, 2024. "A comparison of chance-constrained data envelopment analysis, stochastic nonparametric envelopment of data and bootstrap method: A case study of cultural regeneration performance of cities," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1179-1191.

    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:6:y:2013:i:1:p:425-443:d:22919. 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.