A Variable-Dimension Optimization Approach to Unit Commitment Problem

This paper proposes a variable-dimension optimization approach to address the high dimensionality issues in solving the unit commitment problem. This method introduces the concept of adaptive search space dimension. The proposed approach is implemented in particle swarm optimization algorithm. The optimization process starts with an arbitrary problem dimension, adapts with respect to the swarm progress and finally selects the optimal dimensional space. The efficiency of this method is tested on a ten-unit test system. The results are compared with binary programming and fixed duty cycle approaches. The simulation results show that the proposed method results in considerable reduction of problem dimension, faster convergence and improved quality of the final solution.

I. Introduction

The objective of the unit commitment problem (UCP) is to determine the optimal scheduling of the generating units along with their generation levels at minimum operating costs while satisfying the system and unit constraints. The decision variables include the binary unit commitment (UC) variables and real-valued economic dispatch (ED) variables. The UC variables describe the ON/OFF status while the ED variables indicate the generation levels of the generators at each hour of the planning period. The dimension of the problem increases rapidly with longer planning time and increased size of the power system. Solving this high dimensional problem is a challenging task.