Medium and Long Term Scenario Generation of Multi-Temporal Renewable Energy Based on GAN

Abstract

With China’s “double carbon” target, the future power system will show A significant percentage of the energy generated comes from renewable sources, specifically wind power, which has obvious randomness, volatility and intermittency, and will have a greater impact on the safety of the power system. How to effectively characterize the randomness and volatility of its power output is a major challenge for power system dispatch. The method we suggest in this paper utilizes generative adversarial networks to produce medium- and long-term scenarios for wind power generation. This approach allows for the creation of multiple sets of wind power generation data that share the same characteristics as the historical data., Our approach takes into account the spatial correlation between wind power generation at wind farms situated in various geographic locations, fully describing the randomness and spatio-temporal correlation of wind power, and providing an important reference for power system planning and scheduling.