Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

The unit commitment problem in power system is a highly nonlinear, nonconvex, multiconstrained, complex, highly dimensional, mixed integer and combinatorial generation selection problem. The phenomenon of committing and decommitting represents a discrete problem that requires binary/discrete optimization techniques to tackle with unit commitment optimization problem. The key functions of the unit commitment optimization problem involve deciding which units to commit and then to decide their optimum power (economic dispatch). This paper confers a binary grasshopper optimization algorithm to solve the unit commitment optimization problem under multiple constraints. The grasshopper optimization algorithm is a metaheuristic, multiple solutions-based algorithm inspired by the natural swarming behavior of grasshopper towards food. For solving the binary unit commitment optimization problem, the real/continues value grasshopper optimization algorithm is mapped into binary/discrete search-space by using an Sshaped sigmoid function. The proposed algorithm is tested on IEEE benchmark systems of 4, 5, 6, 10, 20, 26, 40, 60, 80, and 100 generating units including the IEEE 118-bus system and the results are compared with different classical, heuristics, metaheuristics, quantum, and hybrid approaches. The results confer better performance of binary grasshopper optimization algorithm to solve the unit commitment optimization problem.