Firefly algorithm with multiple workers for the power system unit commitment problem

Abstract

This paper proposes an improved firefly (FF) algorithm with multiple workers for solving the unit commitment (UC) problem of power systems. The UC problem is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under system and unit constraints. Nowadays, highly developed computer systems are available in plenty, and proper utilization of these systems will reduce the time and complexity of combinatorial optimization problems with large numbers of generating units. Here, multiple workers are assigned to solve a UC problem as well as the subproblem, namely economic dispatch (ED) in distributed memory models. The proposed method incorporates a group search in a FF algorithm and thereby a global search is attained through the local search performed by the individual workers, which fine tune the search space in achieving the final solution. The execution time taken by the processor and the solution obtained with respect to the number of processors in a cluster are thoroughly discussed for different test systems. The methodology is validated on a 100 unit system, an IEEE 118 bus system, and a practical Taiwan 38 bus power system and the results are compared with the available literature.

Energy management system, generation and demand predictors: a review

“…fuel cost for generator [95] 11 and residential users. A non-linear programming-based approach is mentioned in [100], to maximise the revenue generated through trade between the microgrid and the utility.…”

“…In [95], presented parallel clusters multiple workers-based firefly algorithm (FFA) for minimising a total cost in ED problem. This method is compared with several methods (dynamic programming, Lagrange relaxation, constraint logic programming, simulated annealing, fuzzy optimisation, matrix real-coded GA, memory-bounded ant colony optimisation, twofold simulated annealing, enhanced merit order-ALHN, heuristics and absolutely stochastic algorithm and fuzzy adaptive PSO), moreover, it is validated, but, the parallel memory mapping technique makes it slow to compute in real-time management problems.…”

Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

“…Ant colony optimization (ACO) [6] and particle swarm optimization (PSO) [7] mimics the social interaction, behavior, and coordination among the swarms. There is an extended list of nature inspired algorithms such as binary grey wolf optimization algorithm (BGWO) [8], ring crossover genetic algorithm (GA) [9], firefly algorithm (FF) [10], evolutionary algorithms (EA) [11] and improve binary cuckoo search (IBCS) [12] etc. to solve the UC optimization problem.…”

Global maximum operating point tracking for PV system using fast convergence Firefly algorithm

“…The performance of these methods varies in different aspects such as convergence speed, complexity in tuning the parameters, steady-state performance, cost, and capability to detect the GMOP under different irradiance patterns. Recently, a metaheuristic algorithm known as the firefly (FF) algorithm was developed [15], inspired by the social behavior of fireflies. In addition to our previous work, several works on the FF algorithm have been published [15].…”

“…Recently, a metaheuristic algorithm known as the firefly (FF) algorithm was developed [15], inspired by the social behavior of fireflies. In addition to our previous work, several works on the FF algorithm have been published [15]. Following the interest in this algorithm, this paper transforms the FF method for designing an intelligent MPPT scheme to determine GMOP, maintaining inheritance of firefly behavior while adding fast convergence properties.…”