Abstract
Unit Commitment (UC) is a key task in electric power system operation, aiming at minimizing the total cost of power generation. It is essential to monitor wide range of activities and practices of UC necessary to determine the operating plan of generating units. The UC problem is particularly crucial, when the behavior of loads at every hour interval, is oscillatory and with different operational constraints and environments. Many works have been proposed, with different optimization methods to solve the UC problem. This paper gives a detailed review of the evolutionary optimization techniques, employed for solving UC problem, by collecting them from lots of peer reviewed published papers. This review was carried out under many sections, based on various evolutionary optimization techniques, to help new researchers, dealing with modern UC problem solutions, under different situations of power system.
Highlights
The Unit Commitment (UC) problem is related to the trustworthy operating state of power system network, intended for the functioning status of thermal units as well as the power dispatch, which involves distributing the system load demand to the committed thermal units [1]
Nontraditional artificial intelligence based optimization approaches like Network Programming (NP) [80], Tabu Search (TS) [81], Hybrid fuzzy based TS [82], Heuristic search techniques [83]–[85], Simulated Annealing (SA) [86]–[89], Twofold SA [90], [91], Adaptive SA [92], Enhanced SA [93], [94], Stochastic SA [95], [96], Ant Colony Optimization (ACO) [97], [98], ACO with Random Perturbation [99], Memory Bounded ACO [100], Nodal ACO [101], Hybrid Taguchi ACO [102], Fuzzy Logic [103]–[107], Fuzzy based SA [108], [109], Fuzzy Dynamic Programming (DP) [110], Fuzzy Hierarchical Bi-Level Modelling [111], Artificial Neural Network (ANN) [112]–[119], Hybrid ANN [120]–[122], Hopfield ANN [123]–[127], Expert System [128]–[131] and QuasiOppositional Teaching Learning Algorithm [132], could cope with the convergence properties, intricacy of computational operation and give innovative solutions against conservative methods
This paper proposes to present a complete review of the UC problem, integrated with numerous types of evolutionary optimization techniques like UC problem incorporated with GA [133]–[159], UC problem incorporated with PSO [160]–[170], UC problem incorporated with EA [171]–[177], UC problem incorporated with EP [178]–[183], UC problem incorporated with DE [184]–[190], UC problem incorporated with SFLA [191]–[193], UC problem incorporated with FA [194]–[199], UC problem incorporated with other evolutionary optimization techniques like BFA [200], FSA [201], [202] and Cuckoo Search Algorithm (CSA) [203], UC problem incorporated with Hybrid evolutionary optimization techniques [204]–[244], in the subsequent sections
Summary
The Unit Commitment (UC) problem is related to the trustworthy operating state of power system network, intended for the functioning status of thermal units as well as the power dispatch, which involves distributing the system load demand to the committed thermal units [1]. The main objective of this paper was to present comprehensive review, about the different evolutionary optimization techniques, to deal with various dimensions of UC problems, under different constraints and environments. Clear reviews about the UC problem, with different evolutionary optimization techniques like GA COMMON BACKDROP CONCERNING UC PROBLEM UC is an extremely important optimization assignment since the best possible scheduling of commitment, can reduce the enormous amount of total production costs while overcoming a variety of unit and system constraints.
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