Research on Optimal Dispatch of Water-Fire-New Energy Power System

In order to safeguard their own energy security, strengthen environmental protection and prevent climate change, many countries in the world are vigorously developing renewable energy to replace traditional fossil energy. As the largest developing country in the world, China's energy structure and development situation are in a very serious situation, and the energy security problems are threatening the smooth implementation of the national energy strategy. Accelerating the development and utilization of renewable energy has become the best choice to solve the energy crisis in China. Wind and solar energy, as an important part of new energy, are gradually infiltrating into China's power system. However, due to their great randomness, they are bound to bring severe challenges and a series of difficulties to the traditional power system optimal scheduling problem. China is rich in water energy resources, hydropower is the largest renewable energy that can be developed on a large scale at present, vigorously developing hydropower can significantly reduce the consumption of traditional fossil energy, and at the same time it will absorb some negative effects of new energy power generation through controllable adjustment. Therefore, it is necessary to rationally formulate the optimal dispatching strategy of multi-energy complementary power system for the development of China's energy strategy. This paper has carried on a series of discussions and carried out the following work. (1) Comprehensively analyze the reasons for the uncertainty of new energy power generation, study the probability distribution model of prediction error, and discuss the new energy power generation scenario with the scenario analysis method. (2) The traditional combined optimal dispatching model, the combined dispatching model with new energy generation system and the multi-energy complementary power system are calculated respectively, and the optimal dispatching strategy is given through an example analysis. (3) Complex models need efficient algorithms to solve, so this paper proposes a new intelligent optimization algorithm.