Multi-objective operation optimization analysis based on distributed energy sources

Abstract

In the context of the determination and implementation of the dual carbon goal, it is an important trend of China’s energy strategy to incorporate a considerable proportion of renewable energy into the power grid. Therefore, energy consumption is gradually turning to new energy sources such as wind energy and wind energy. Considering the limitations of random, intermittent and anti-peak regulation of energy forms such as wind and light, a multi-objective optimization model of wind, light, fire and storage combined power generation and heating is built. This model can fine-tune and fill the valley by using the peak regulating capacity of thermal power plants and pumped storage devices, so that the whole system has stable power generation and heating capacity. The model is solved by multi-purpose particle swarm optimization algorithm based on maximum total generation and heating rotation and minimum generation and heating slope fluctuation. The calculation results show that the established model can effectively improve the wind and light consumption rates and reduce the peak set pressure of the thermal device.