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Performance Analysis of MATLAB Solvers in the Case of a Quadratic Programming Generation Scheduling Optimization Problem

Authors: Dávid Csercsik, Péter Kádár


In the case of the proposed method, the problem is parallelized by considering multiple possible mode of operation profiles, which determine the range in which the generators operate in each period. For each of these profiles, the optimization is carried out independently, and the best resulting dispatch is chosen. For each such profile, the resulting problem is a quadratic programming (QP) problem with a potentially negative definite Q quadratic term, and constraints depending on the actual operation profile. In this paper we analyze the performance of available MATLAB optimization methods and solvers for the corresponding QP.

Keywords: Optimization, MATLAB, economic dispatch, quadratic programming

Digital Object Identifier (DOI):

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[1] H. Y. Yamin, “Review on methods of generation scheduling in electric power systems,” Electric Power Systems Research, vol. 69, no. 2, pp. 227–248, 2004.
[2] X. Xia and A. Elaiw, “Optimal dynamic economic dispatch of generation: a review,” Electric Power Systems Research, vol. 80, no. 8, pp. 975–986, 2010.
[3] A. Pre´kopa, J. Mayer, B. Strazicky, I. Dea´k, J. Hoffer, A´ . Ne´meth, and B. Potecz, Scheduling of Power Generation. Springer, 2014.
[4] D. Csercsik and P. K´ad´ar, “A distributed optimal power flow approach based on the decomposition of generation characteristics,” in Environment and Electrical Engineering (EEEIC), 2016 IEEE 16th International Conference on. IEEE, 2016, pp. 1–6.
[5] P. So˝re´s, D. Dive´nyi, B. Polga´ri, D. Raisz, and A´ . Sleisz, “Day-ahead market structures for co-optimized energy and reserve allocation,” in European Energy Market (EEM), 2015 12th International Conference on the. IEEE, 2015, pp. 1–5.
[6] T. Wu, M. Rothleder, Z. Alaywan, and A. D. Papalexopoulos, “Pricing energy and ancillary services in integrated market systems by an optimal power flow,” IEEE Transactions on power systems, vol. 19, no. 1, pp. 339–347, 2004.
[7] B. Polga´ri, P. So˝re´s, D. Dive´nyi, A´ . Sleisz, and D. Raisz, “New offer structure for a co-optimized day-ahead electricity market,” in European Energy Market (EEM), 2015 12th International Conference on the. IEEE, 2015, pp. 1–5.
[8] P. Gonz´alez, J. Villar, C. A. D´ıaz, and F. A. Campos, “Joint energy and reserve markets: Current implementations and modeling trends,” Electric Power Systems Research, vol. 109, pp. 101–111, 2014.
[9] J. Twidell and T. Weir, Renewable energy resources. Routledge, 2015.
[10] F. Vasilyev and A. Y. Ivanitskiy, “Dual simplex method,” in In-Depth Analysis of Linear Programming. Springer, 2001, pp. 119–166.
[11] T. Achterberg, “SCIP: solving constraint integer programs,” Mathematical Programming Computation, vol. 1, no. 1, pp. 1–41, 2009.
[12] E. M. Gertz and S. J. Wright, “Object-oriented software for quadratic programming,” ACM Transactions on Mathematical Software (TOMS), vol. 29, no. 1, pp. 58–81, 2003.
[13] J. Gondzio, “Multiple centrality corrections in a primal-dual method for linear programming,” Computational optimization and applications, vol. 6, no. 2, pp. 137–156, 1996.
[14] A. W¨achter and L. T. Biegler, “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming,” Mathematical programming, vol. 106, no. 1, pp. 25–57, 2006.
[15] MATLAB, version 7.10.0 (R2010a). Natick, Massachusetts: The MathWorks Inc., 2010.
[16] J. Currie and D. I. Wilson, “Opti: lowering the barrier between open source optimizers and the industrial matlab user,” Foundations of computer-aided process operations, vol. 24, p. 32, 2012.
[17] J. Lofberg, “Yalmip : a toolbox for modeling and optimization in matlab,” in 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508), Sept 2004, pp. 284–289.