IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v47y1999i5p703-712.html
   My bibliography  Save this article

Asymptotic Mean and Variance of Electric Power Generation System Production Costs via Recursive Computation of the Fundamental Matrix of a Markov Chain

Author

Listed:
  • Fen-Ru Shih

    (Dae-Woo Institute of Technology and Commerce, Hsinchu, Taiwan, Republic of China)

  • Mainak Mazumdar

    (Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261)

  • Jeremy A. Bloom

    (Electric Power Research Institute, Palo Alto, California)

Abstract

The cost of producing electricity during a given time interval is a random variable that depends both on the availability of the generating units during the study horizon and on the magnitude of the load. Based upon a Markov model, we present a recursive scheme for estimating the asymptotic mean and variance of the production cost. These computations are difficult because the state space for a typical power generation system is very large and because the asymptotic variance depends upon the fundamental matrix. Its computation requires the inversion of a matrix whose dimension depends on the size of the state space. The recursion relations given here preclude the need for such matrix inversion and provide approximate estimates that compare very favorably with a realistic Monte Carlo simulation.

Suggested Citation

  • Fen-Ru Shih & Mainak Mazumdar & Jeremy A. Bloom, 1999. "Asymptotic Mean and Variance of Electric Power Generation System Production Costs via Recursive Computation of the Fundamental Matrix of a Markov Chain," Operations Research, INFORMS, vol. 47(5), pages 703-712, October.
  • Handle: RePEc:inm:oropre:v:47:y:1999:i:5:p:703-712
    DOI: 10.1287/opre.47.5.703
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.47.5.703
    Download Restriction: no

    File URL: https://libkey.io/10.1287/opre.47.5.703?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
    ---><---

    References listed on IDEAS

    as
    1. Ward Whitt, 1992. "Asymptotic Formulas for Markov Processes with Applications to Simulation," Operations Research, INFORMS, vol. 40(2), pages 279-291, April.
    2. Sarah M. Ryan & Mainak Mazumdar, 1992. "Chronological Influences of the Variance of Electric Power Production Costs," Operations Research, INFORMS, vol. 40(3-supplem), pages 284-292, June.
    Full references (including those not matched with items on IDEAS)

    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. Joost Berkhout & Bernd F. Heidergott, 2019. "Analysis of Markov Influence Graphs," Operations Research, INFORMS, vol. 67(3), pages 892-904, May.
    2. Kerry W. Fendick, 2013. "Pricing and Hedging Derivative Securities with Unknown Local Volatilities," Papers 1309.6164, arXiv.org, revised Oct 2013.
    3. Whitt, Ward, 2012. "Fitting birth-and-death queueing models to data," Statistics & Probability Letters, Elsevier, vol. 82(5), pages 998-1004.
    4. Jing Dong, 2022. "Metastability in queues," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 413-415, April.
    5. Yunan Liu & Ward Whitt & Yao Yu, 2016. "Approximations for heavily loaded G/GI/n + GI queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(3), pages 187-217, April.
    6. Choi, Michael C.H. & Li, Evelyn, 2019. "A Hoeffding’s inequality for uniformly ergodic diffusion process," Statistics & Probability Letters, Elsevier, vol. 150(C), pages 23-28.

    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:inm:oropre:v:47:y:1999:i:5:p:703-712. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.