Unit commitment by heuristics and absolutely stochastic simulated annealing

Abstract

A new approach to unit commitment problem is presented using absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in simulated annealing minimisation method, a higher cost feasible solution is accepted with temperature-dependent probability; however, other solutions are accepted deterministically. That may lead to the near optimisation slowly. However, all the solutions of both higher and lower costs, are associated with acceptance probabilities to make an early jump from one local minimum to other so that it can search and compare more local minima within the same time/iteration limit. Besides, the number of bits flipping is decided by the appropriate distribution. Excess units with system-dependent probability distribution handle constraints efficiently. Sensitivity of the distribution parameters is tolerable. To reduce economic load dispatch calculations, a sign bit vector is introduced as well. The proposed method is then tested using the reported problem data set. 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.