Ramp Rate Constrained Unit Commitment by Improved Priority List and Enhanced Particle Swarm Optimization

Abstract

This paper proposes a new approach to solve ramp rate constrained unit commitment (RUC) problem by improving the method of particle swarm optimization, namely improved priority list and enhanced particle swarm optimization (IPL-EPSO). The IPL-EPSO proposed in this paper is a combination of improved priority list (IPL) and enhanced particle swarm optimization (EPSO), which decomposes UC problem into two sub-optimization problems and solves them respectively. The IPL is applied to solve unit scheduling problem, considering power balance constraint, system reverse constraint, start-up/shut-down ramp rate constraint, operation ramp rate constraint and minimum up/down-time constraint, and hence the EPSO is used to solve ramp rate constrained economic dispatch (RED) problem, in order to provide specific solutions satisfying power balance constraint and ramp rate constraint. Such cooperation fully presents not only the advantage of intelligence algorithm for addressing NP-hard problem, but also the guiding effects of human knowledge, reducing the complexity of computation. Problem formulation, representation, parameter testing and the final simulation results for 10, 20, 40 generator-scheduling problems are represented. Results clearly show that IPL-EPSO is very competent in solving the UC problem in comparison to other existing methods using PSO or IPL.