An evolutionary programming-based tabu search method for solving the unit commitment problem

This paper presents a new approach to solving the short-term unit commitment problem using an evolutionary programming-based tabu search (TS) method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Evolutionary programming, which happens to be a global optimization technique for solving unit commitment problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all of the units according to their initial status ("flat start"). Here, the parents are obtained from a predefined set of solutions (i.e., each and every solution is adjusted to meet the requirements). Then, a random decommitment is carried out with respect to the unit's minimum downtimes, and TS improves the status by avoiding entrapment in local minima. The best population is selected by evolutionary strategy. The Neyveli Thermal Power Station (NTPS) Unit-II in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consisting of 10, 26, and 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, Lagrangian relaxation, and simulated annealing and tabu search in reaching proper unit commitment.

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