Browsing by Author "Marcovecchio, Marian G."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Deterministic optimization of the thermal Unit Commitment problem: A Branch and Cut searchPublication . Marcovecchio, Marian G.; Novais, Augusto Q.; Grossmann, Ignacio E.This paper proposes a novel deterministic optimization approach for the Unit Commitment (UC) problem, involving thermal generating units. A mathematical programming model is first presented, whichincludes all the basic constraints and a set of binary variables for the on/off status of each generator ateach time period, leading to a convex mixed-integer quadratic programming (MIQP) formulation. Then,an effective solution methodology based on valid integer cutting planes is proposed, and implementedthrough a Branch and Cut search for finding the global optimal solution. The application of the pro-posed approach is illustrated with several examples of different dimensions. Comparisons with other mathematical formulations are also presented.
- On the computational studies of deterministic global optimization of head dependent short-term hydro schedulingPublication . Lima, Ricardo M.; Marcovecchio, Marian G.; Novais, Augusto Q.; Grossmann, Ignacio E.This paper addresses the global optimization of the short term scheduling for hydroelectric power generation. A tailored deterministic global optimization approach, denominated sHBB, is developed and its performance is analyzed. This approach is applied to the optimization of a mixed integer nonlinear programming (MINLP) model for cascades of hydro plants, each one with multiple turbines, and characterized by a detailed representation of the net head of water, and a nonlinear hydropower generation function. A simplified model is also considered where only the linear coefficients of the forebay and tailrace polynomial functions are retained. For comparison purposes, four case studies are addressed with the proposed global optimization strategy and with a commercial solver for global optimization. The results show that the proposed approach is more efficient than the commercial solver in terms of finding a better solution with a smaller optimality gap, using less CPU time. The proposed method can also find alternative and potentially more profitable power production schedules. Significant insights were also obtained regarding the effectiveness of the proposed relaxation strategies.