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Electric Power Bid Determination and Evaluation for Price Taker Units under Price Uncertainty

Author

Listed:
  • Ahmet Yucekaya

    (Department of Industrial Engineering, Kadir Has University, 34083, Turkey)

  • Jorge Valenzuela

    (Department of Industrial and Systems Engineering, 304 Shelby Center, Auburn University, Auburn, Alabama 36849, USA.)

Abstract

Power companies aim to maximize their profit which is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize their profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. Price taker units have no power to affect the prices but need to determine their best bidding strategy to maximize their profit assuming a quadratic cost function and uncertain market prices. Price taker units also need to evaluate their bidding strategy under different price scenarios. In this paper, we first model the bidding problem for a price taker unit and then propose quadratic programming, nonlinear programming and marginal cost based bidding models under price uncertainty. We use case studies to study the computation burden and limitation to reach a solution. We also propose a simulation methodology to evaluate the performance of each bidding strategy for different market prices in an effort to help decision makers to assess their bidding decisions.

Suggested Citation

  • Ahmet Yucekaya & Jorge Valenzuela, 2021. "Electric Power Bid Determination and Evaluation for Price Taker Units under Price Uncertainty," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 35-42.
  • Handle: RePEc:eco:journ2:2021-06-5
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    References listed on IDEAS

    as
    1. Peter Cramton, 2017. "Electricity market design," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 589-612.
    2. Davatgaran, Vahid & Saniei, Mohsen & Mortazavi, Seyed Saeidollah, 2018. "Optimal bidding strategy for an energy hub in energy market," Energy, Elsevier, vol. 148(C), pages 482-493.
    3. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
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    More about this item

    Keywords

    Bidding; nonlinear programming; quadratic programming; simulation; electricity markets;
    All these keywords.

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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