Abstract
The unit commitment (UC) problem which is an important subject in power system engineering is solved by using Lagragian relaxation (LR), penalty function (PF), and augmented Lagrangian penalty function (ALPF) methods due to their higher solution quality and faster computational time than metaheuristic approaches. This problem is considered to be a nonlinear programming-(NP-) hard problem because it is nonlinear, mixed-integer, and nonconvex. These three methods used for solving the problem are based on dual optimization techniques. ALPF method which combines the algorithmic aspects of both LR and PF methods is firstly used for solving the UC problem. These methods are compared to each other based on feasible schedule for each stage, feasible cost, dual cost, duality gap, duration time, and number of iterations. The numerical results show that the ALPF method gives the best duality gap, feasible and dual cost instead of worse duration time and the number of iterations. The four-unit Tuncbilek thermal plant which is located in Kutahya region in Turkey is chosen as a test system in this study. The programs used for all the analyses are coded and implemented using general algebraic modeling system (GAMS).
Highlights
The UC problem decides to which electricity generation units should be running in each stage to satisfy a predictably varying demand for electricity
Dual cost and duality gap values, duration time, and number of iterations are found for each method
Three different dual approach-based methods, LR, PF and ALPF methods, are used for solving the UC problem known as an important and hard-solving problem in power system engineering, and the results from the programs coded and implemented using GAMS for these methods are compared to each other according to feasible cost, dual cost, duality gap, duration time, and number of iterations
Summary
The UC problem decides to which electricity generation units should be running in each stage to satisfy a predictably varying demand for electricity. The binary variables cause a great deal of trouble and reason for the difficulty in solving the UC. There have been various methods which are based on mathematical programming and metaheuristic-based for solving the thermal and hydrothermal UC problem in literature. They are based on mathematical programming and metaheuristic based approaches. These major methods are priority list 3–5 , dynamic programming DP 6 , mixed-integer programming 7–9 , heuristic unit 10 , simulated annealing 11–13 , tabu search 14, 15 , evolutionary programming 16, , constraint logic programming , genetic algorithms 19–23 , LR 24–32 , interior point method , memetic algorithm , and neural network 35–37
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