Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms

Abstract

In this paper, combined heat and power units are incorporated in dynamic economic dispatch to minimize total production costs considering realistic constraints such as ramp rate and spinning reserve limits effects over a short time span. Four evolutionary approaches, namely particle swarm optimization (PSO), particle swarm optimization with constriction factor (PSOCFA), particle swarm optimization with inertia weight factor (PSOIWA) and particle swarm optimization with both constriction factor and inertia weight factor (PSOCFIWA) are successfully implemented to solve the combined heat and power economic dispatch (CHPED) problem. These approaches have been tested on 12-generation units system with two steam, four gas and six cogeneration units. In addition, the performance tests are applied to measure the actual power output and the fuel consumption in every point tests for achieving different curves such as input/output, incremental heat rate and heat rate curves for the twelve units. The results of the four approaches are compared with those obtained using existing performance testing method. The results show that the particle swarm optimization with improved inertia weight is able to achieve a better solution at less computational time.