A Study of Multi-Objective Load Optimal Dispatch in Thermal Power Unit Based on Improved Particle Swarm Optimization Algorithm

Abstract

In order to fit in with the demands of the development of electricity market in China, a multi-objective optimization mathematical model is presented to dispatch load within the units, taking economy, environmental protection and quick responsiveness to dispatching commands into consideration at the same time. And take the minimal whole plants power-supply coal cost rate, the minimal pollutant emissions and the minimal load adjusting time as these three objective functions respectively. The four constraint conditions are unit power balance constraint, load bound constraint, ramping constraint and pollution discharge standards constraint. An improved particle swarm optimization algorithm is used to get the Pareto solution set. The optimal solution was obtained by using the method of multi-attribute decision making, through sequencing the solution set by comprehensive evaluation. A case study based on a power plant with 4×600MW units was carried out. The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.

Optimization of Valve Flow Characteristics Based on Improved Particle Swarm Algorithm

“…At present, the algorithm has been widely used in the study of PFR of thermal power units. Wang Ruoyu adopted the particle swarm algorithm for identifying unit characteristics and predicting the primary FM margin [6] . Tao Qian et al analyzed the unit economics during primary FM and optimized the FM parameters by particle swarm algorithm to obtain the optimal coal consumption, so that the primary FM performance reaches the best level.…”