Abstract: Under the background of high proportion of renewable energy, wind and photovoltaic power generation have become the key means to cope with climate change and promote energy conservation and emission reduction. However, due to their volatility and randomness, the difficulty of collaborative optimization operation of power system is increased. In this paper, the scene generation and load real-time output is generated by the Latin hypercube sampling, and the sample reduction is carried out according to Euclidean distance, and the uncertainty problem is transformed into the scene analysis problem. On this basis, the scheduling compensation strategy of different user types at different time scales is analyzed, and the complementary mechanism composed of electricity price type and incentive demand response is introduced. Taking the highest economic efficiency of the system operation, the renewable energy consumption rate under different scheduling strategies is compared and analyzed. The example shows that after considering the source-load uncertainty, the proposed strategy can better stabilize the load curve, improve the scenery consumption rate, and realize the collaborative optimization operation of high-proportion renewable energy power system.

Key words: high-proportion renewable energy, uncertainty of generation and load, scenario analysis, multi-scale operation, demand response, photovoltaic and wind accommodation.