A Simulated Annealing Based Hybrid Optimization Approach for a Fuzzy Modelled Unit Commitment Problem

Abstract

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid simulated annealing (SA), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid SA-PSO-SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid SA-PSO-SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. SA is used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the SA method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO-SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.