IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v31y2006i13p2469-2485.html
   My bibliography  Save this article

Long-term electricity contract optimization with demand uncertainties

Author

Listed:
  • Chan, Pang
  • Hui, Chi-Wai
  • Li, Wenkai
  • Sakamoto, Haruo
  • Hirata, Kentaro
  • Li, Pu

Abstract

This paper presents a study on selecting electricity contracts for a large-scale chemical production plant, which requires electricity importation, under demand uncertainty. Two common types of electricity contracts are considered, time zone (TZ) contract and loading curve (LC) contract. A multi-period linear probabilistic programming model is adopted for the contract selection and optimization. Hence, by using the probabilistic programming, a solution procedure is proposed that allow users to determine the best electricity contract according to their desired confident level of the uncertainties. In addition, due to the fact that the demand of product is uncertain, if one considers the overage and shortage of the products in the market as well, an interesting result can be obtained. The methodology is explained in the paper.

Suggested Citation

  • Chan, Pang & Hui, Chi-Wai & Li, Wenkai & Sakamoto, Haruo & Hirata, Kentaro & Li, Pu, 2006. "Long-term electricity contract optimization with demand uncertainties," Energy, Elsevier, vol. 31(13), pages 2469-2485.
    • Handle: RePEc:eee:energy:v:31:y:2006:i:13:p:2469-2485
    DOI: 10.1016/j.energy.2005.10.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544205002367
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2005.10.035?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. Zimmermann, H. -J., 2000. "An application-oriented view of modeling uncertainty," European Journal of Operational Research, Elsevier, vol. 122(2), pages 190-198, April.
    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. Bazmi, Aqeel Ahmed & Zahedi, Gholamreza, 2011. "Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3480-3500.

    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. Dias, Luis C. & Climaco, Joao N., 2005. "Dealing with imprecise information in group multicriteria decisions: a methodology and a GDSS architecture," European Journal of Operational Research, Elsevier, vol. 160(2), pages 291-307, January.
    2. Kikuchi, Shinya & Chakroborty, Partha, 2006. "Place of possibility theory in transportation analysis," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 595-615, September.
    3. Paulo Afonso & Vishad Vyas & Ana Antunes & Sérgio Silva & Boris P. J. Bret, 2021. "A Stochastic Approach for Product Costing in Manufacturing Processes," Mathematics, MDPI, vol. 9(18), pages 1-23, September.
    4. Püschel, Tim & Schryen, Guido & Hristova, Diana & Neumann, Dirk, 2015. "Revenue management for Cloud computing providers: Decision models for service admission control under non-probabilistic uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 637-647.
    5. Wulf, David & Bertsch, Valentin, 2016. "A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making," MPRA Paper 75025, University Library of Munich, Germany.
    6. Hocine, Amine & Kouaissah, Noureddine, 2020. "XOR analytic hierarchy process and its application in the renewable energy sector," Omega, Elsevier, vol. 97(C).
    7. Kangas, Annika S. & Kangas, Jyrki, 2004. "Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis," Forest Policy and Economics, Elsevier, vol. 6(2), pages 169-188, March.
    8. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    9. Coit, David W. & Zio, Enrico, 2019. "The evolution of system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    10. Violeta Keršulienė & Zenonas Turskis, 2014. "A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(2), pages 232-252, April.
    11. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.
    12. Amin Vafadarnikjoo & Madjid Tavana & Tiago Botelho & Konstantinos Chalvatzis, 2020. "A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria," Annals of Operations Research, Springer, vol. 289(2), pages 391-418, June.
    13. Abdelkader Baaziz & Luc Quoniam, 2013. "Réduire les risques des décisions stratégiques dans les nouveaux environnements concurrentiels incertains : Cas des Entreprises Publiques Algériennes," Post-Print hal-00822969, HAL.
    14. Janssen, J.A.E.B. & Krol, M.S. & Schielen, R.M.J. & Hoekstra, A.Y. & de Kok, J.-L., 2010. "Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models," Ecological Modelling, Elsevier, vol. 221(9), pages 1245-1251.
    15. de Brito, Marisa P. & van der Laan, Erwin A., 2009. "Inventory control with product returns: The impact of imperfect information," European Journal of Operational Research, Elsevier, vol. 194(1), pages 85-101, April.
    16. Kreye, M.E. & Goh, Y.M. & Newnes, L.B. & Goodwin, P., 2012. "Approaches to displaying information to assist decisions under uncertainty," Omega, Elsevier, vol. 40(6), pages 682-692.
    17. Sönmez, Erkut & Kekre, Sunder & Scheller-Wolf, Alan & Secomandi, Nicola, 2013. "Strategic analysis of technology and capacity investments in the liquefied natural gas industry," European Journal of Operational Research, Elsevier, vol. 226(1), pages 100-114.
    18. Zhang, Guoquan & Shang, Jennifer & Li, Wenli, 2011. "Collaborative production planning of supply chain under price and demand uncertainty," European Journal of Operational Research, Elsevier, vol. 215(3), pages 590-603, December.
    19. Zio, Enrico & Aven, Terje, 2011. "Uncertainties in smart grids behavior and modeling: What are the risks and vulnerabilities? How to analyze them?," Energy Policy, Elsevier, vol. 39(10), pages 6308-6320, October.
    20. Manuel E. SANSALVADOR & José M. BROTONS, 2017. "The Application of OWAs in Expertise Processes: The Development of a Model for the Quantification of Hidden Quality Costs," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(3), pages 73-90.

    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:energy:v:31:y:2006:i:13:p:2469-2485. 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.journals.elsevier.com/energy .

    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.