A Hybrid Unit Commitment Approach Incorporating Modified Priority List with Charged System Search Methods

Abstract

This paper presents a new hybrid approach that combines Modified Priority List (MPL) with Charged System Search (CSS), termed MPL-CSS, to solve one of the most crucial power system's operational optimization problems, known as unit commitment (UC) scheduling. The UC scheduling problem is a mixed-integer nonlinear problem, highly-dimensional and extremely constrained. Existing meta-heuristic UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to largescale, heavily-constrained UC applications. In the first step of the proposed method, initial hourly optimum solutions of UC are obtained by Modified Priority List (MPL); however, the obtained UC solution may still be possible to be further improved. Therefore, in the second step, the CSS is utilized to achieve higher quality solutions. The UC is formulated as mixed integer linear programming to ensure the tractability of the results. The proposed method is successfully applied to a popular test system up to 100 units generators for both 24-hr and 168-hr system. Computational results show that both solution cost and execution time are superior to those of published methods.

Optimal Unit Commitment with Renewable Energy in Regulated and Deregulated Systems

“…TUs must be turned on at a certain hour before being shut down as [17]: up  n : Minimum on time of thermal unit ( n ). 4) Minimum down time for TUs [18].…”

Enhanced Lagrange relaxation for the optimal unit commitment of identical generating units

“…The mathematical methods include priority list schemes, mixed-integer programming and decomposition techniques. Although priority list constitutes the simplest and fastest method to UC, it possesses a weakness in concluding to a final solution which in many cases may stand out from the optimal [1]. Classified as linear [2], quadratic [3] or non-linear [4] programming, mixed-integer approaches are able to enhance the generation modelling accuracy though they require increased computational efforts to converge to the desired solution.…”