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Minimization of Fuel Costs and Gaseous Emissions of Electric Power Generation by Model Predictive Control

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  • A. M. Elaiw
  • X. Xia
  • A. M. Shehata

Abstract

The purpose of this paper is to present a model predictive control (MPC) approach for the periodic implementation of the optimal solutions of two optimal dynamic dispatch problems with emission and transmission line losses. The first problem is the dynamic economic emission dispatch (DEED) which is a multiobjective optimization problem which minimizes both fuel cost and pollutants emission simultaneously under a set of constraints. The second one is the profit-based dynamic economic emission dispatch (PBDEED) which is also a multi-objective optimization problem which maximizes the profit and minimizes the emission simultaneously under a set of constraints. Both the demand and energy price are assumed to be periodic and the total transmission loss is assumed to be a quadratic function of the generator power outputs. We assume that there are certain disturbances or uncertainties in the execution of the optimal controller and in the forecasted demand. The convergence and robustness of the MPC algorithm are demonstrated through the application of MPC to the DEED and PBDEED problems with five-unit and six-unit test systems, respectively.

Suggested Citation

  • A. M. Elaiw & X. Xia & A. M. Shehata, 2013. "Minimization of Fuel Costs and Gaseous Emissions of Electric Power Generation by Model Predictive Control," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-15, April.
    • Handle: RePEc:hin:jnlmpe:906958
    DOI: 10.1155/2013/906958
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    Cited by:

    1. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.

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