Solution of unit commitment problem using quasi-oppositional teaching learning based algorithm

A novel reliability oriented bi-objective unit commitment problem

“…In the first part of numerical simulation, in order to show the capability of proposed method, a comparison between the obtained result of MIWO algorithm and those reported in the literature including PSO [1], BGSA [13], TLBO [8], QOTLBO [8], GHS-JGT [14] and BSA [15] algorithms, for Case I are provided in TABLE I. According to this table, it is obvious that the proposed algorithm can converge to a better solution comparing to the other optimization algorithms.…”

“…The shuffle frog leaping algorithms (SFLA) for solving the UC problem is developed and compared with the results of different EAs in [6]. A quasi-oppositional teaching learningbased optimization (QOTLBO) algorithm is introduced in [8] for solving the UC problem considering spinning reserve and ramp rate of generating unit.…”

A hybrid meta-heuristic method for unit commitment considering flexible charging and discharging of plug-in electric vehicles

“…The binary variables denoting the on/off status of units are generated based on (10)- (12) while the PEV variables are updated based on the initialisation values or updated values from the previous iteration according to (13)- (16).…”

“…However, the significantly increased system complexity due to the introduction of a number of new players into the power system in recent years, such as distributed reneable generations brings considerable computational requirement on these convetional approaches, making them technically less appealing. On the other hand, intelligent mix-coded meta-heuristic algorithms such as genetic algorithm (GA) [9], harmony search (HS) [10], particle swarm optimisation (PSO) [11], firefly algorithm [12] and teaching learning based optimisation (TLBO) [13] have been employed to solve UC problems and achieved some good results. These methods however require a large number of iterations to ensure the algorithm convergence, which may reduce their computational efficiency.…”

A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment

“…Featured MA tools have been adopted in solving UC problem include genetic algorithms (GAs) [11,12], simulated annealing (SA) [13], particle swarm optimization (PSO) [14] and ACO [15], as well as teaching-learning based optimization (TLBO) [16], etc. Some popular PSO variants such as binary particle swarm optimization (BPSO) [17,18], symmetric binary particle swarm optimization [19], quantum-inspired particle swarm optimization (QPSO) [20], hybrid particle swarm optimization (HPSO) [21,22] etc., have also been used.…”