@MASTERSTHESIS\{IMM2015-06871,
    author       = "P. J. Dinesen",
    title        = "Unit Commitment and Economic Model Predictive Control for Optimal Operation of Power Systems",
    year         = "2015",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Richard Petersens Plads, Building 324, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "{DTU} supervisor: John Bagterp J{\o}rgensen, jbjo@dtu.dk, {DTU} Compute",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "This thesis focuses on combining the Unit Commitment (UC) optimization problem and the economic Model Predictive Control (MPC) problem for optimal operation of power systems. The growing uncertainty associated with the increasing share of intermittent renewable energy sources in the power supply has presented new challenges for optimal operation of power systems. Motivated by these challenges, we present a novel control strategy that shows capability of managing uncertainty with flexibility.

The proposed hierarchy control structure consists of two-levels:
• Apply {UC} to determine which power plants are running as well as the main distribution of power production.
• Apply economic {MPC} to repeatedly reoptimize the production in a receding horizon manner while considering updated and more reliable forecasts of power supply from renewable energy sources.

We mathematically formalize the {UC} as a mixed integer linear programming problem and the control problem as a soft constrained linear economic {MPC} optimization problem. Deterministic and stochastic formulations are provided, as well as disturbance modeling for offset free {MPC}.

The developed control strategy is tested on a power system consisting of a portfolio of controllable power plants and non-controllable farms of wind turbines. The results of the simulations successfully show that the novel control strategy appears to provide a feasible and a promising solution to overcome some of the important challenges. Furthermore, it show that the economic {MPC} method play an important role in the control of optimal power system operations. We demonstrate significant savings in imbalance cost and potential reduction in the need of the expensive spinning reserve.

Additionally, results indicate that the coarse discretization and the input parameterization for the {UC} have a cost impact on the solution. Solving the {UC} problem with high resolution yields the optimal production plan. Comparing to the optimal production plan, the {UC} solution with coarse discretization obtain 2.63\% imbalance power while the economic {MPC} solution coincide with the optimal production plan. Simultaneous, the runtime for the economic {MPC} is 65x faster than solving the {UC} with high resolution."
}