Research On Optimized Dispatching Of Solar Thermal- Gas Turbine Power Generation System

With the economic development, the electricity consumption in society has significant increase every year, which requires corresponding increase in generating capacity to meet social needs. However, with energy consumption and environmental pollution, the traditional thermal power will be replaced by clean and renewable energy in some form in future. New powers including wind power, solar power, tidal power, etc, have advantages of invested savings, flexible power generation, and less pollution. Despite that these new power generations have outstanding advantages, but it will cause a lot of problems when connected to the grid. The solar thermal power, as a very popular new energy in the international community recent years, has a series of advantages as excellent power quality, energy storage peaking. And after the addition of gas turbine combined to cycle power generation, more can be done around the clock continuous power.This paper studies the solar thermal-gas turbine combined cycle power generation system optimization and scheduling scheme chosen to establish a scheduling model. The steps are as follows:first, the operating mechanism, structure parameters and the physical characteristics of optical thermal power generation systems, consider the gas engine exhaust heat for storage, gas turbines and high temperature thermal storage system will be established after a thorough study of the physical model. Second, constructed on the basis of the physical model, taking into account the interaction and impact of combined cycle power generation system and the grid, post-physical model has been simplified mathematical scheduling model we need. Thirdly, due to scheduling model recently forecast data need to be intermittent energy sources, and thereforethe establishment of a gray neural network prediction model solar thermal power generation system, and the use of modified Drosophila model algorithm has been optimized, resulting in a typical day in solar thermal power generation forecast data. Finally, the use of predictive data and scheduling model, using a multi-objective particle swarm optimization scheduling model is to strike at satisfying various constraints, the system taking into account the operating costs and environmental benefits of comprehensive benefits to achieve optimal when each generation output portion. And a numerical example, the validity of the model and algorithm.