Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing

This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms.