Real-Time Operation Scheduling Optimization of Electric-Hydrogen Coupling System Under Multiple Time Scales

[Objective] With the rapid development of new power systems, the electrc-hydrogen coupling system gradually presents the characteristics of multiple time scales, multiple uncertainties, high-dimensional multiple levels, nonlinearity, etc., which puts forward higher requirements for operation scheduling decision-making. Therefore, it is very important to ensure the quality of optimal scheduling results while dealing with the renewable energy output disturbance, load changes and equipment dynamic characteristics. [Method] In this study, real-time operation scheduling optimization of the electric-hydrogen coupling system under multi-time scales was carried out. By establishing a day-ahead-intraday optimization decision-making model based on model predictive control and combining the equipment operation characteristics with the grid interaction constraints, a multi-time scale scheduling optimization framework with day-ahead planning and intraday rolling adjustment was formed to provide a closed-loop optimization control strategy under multi-time scales. This realized real-time scheduling control that considered flexible resources in the system. [Result] The results show that the day-ahead planning, based on large time scale, low prediction accuracy data, insufficiently estimated the fluctuation of uncertain variables, leading to the idealization and homogenization of the results. At the same time, there is the risk of short-term supply and demand imbalance, which is quite different from the scheduling results under intraday conditions. Under intraday correction, grid electricity purchase and energy storage, as flexible resources, have become key equipment for regulation. The maximum variation range of the average and peak values can reach 23.3%. Moreover, under intraday correction, timely responses can be made to sudden changes in wind and solar power. [Conclusion] The established multi-time scale optimization scheduling model, based on the results of day-ahead optimization scheduling, accurately predicts the renewable energy output and user energy demand in intraday rolling optimization and adjusts the unit output. It effectively suppresses the power fluctuation caused by the day-ahead and intraday deviations and can also make the operation scheduling process more flexible and mobile. It avoids the problem of supply and demand imbalance that may be caused by scheduling results based on a single fixed time scale and improves the operational stability of the system.