Hydrogen-containing energy storage microgrid optimization operation method based on deep reinforcement learning

Abstract

The invention provides a hydrogen-containing energy storage microgrid optimization operation method based on deep reinforcement learning, and the method comprises the steps: building an electrolytic cell efficiency characteristic model through a linear interpolation method, combining the electrolytic cell efficiency characteristic model with a gas turbine model, an electrochemical energy storage model, a hydrogen storage tank model, a fuel cell model and the like, and taking the microgrid operation cost as a target, constructing an optimized operation model of the hydrogen-containing energy storage micro-grid. And finally, solving a sequence decision problem of micro-grid optimization operation by adopting a depth deterministic strategy gradient algorithm. According to the method, the efficiency characteristic of the electrolytic cell is considered, the hydrogen energy storage capacity can be fully utilized, the optimization problem is solved according to the deep reinforcement learning principle, the operation cost of the hydrogen-containing energy storage microgrid is reduced, and good generalization is achieved.

Description

technical field [0001] The invention belongs to the technical field of power system optimization operation and scheduling, and in particular relates to a hydrogen-containing energy storage microgrid optimization operation method based on deep reinforcement learning. Background technique [0002] With the goal of "strive to reach the peak of carbon dioxide emissions before 2030 and strive to achieve carbon neutrality before 2060", how to improve the utilization rate of renewable energy and reduce carbon emissions has become a current research hotspot. However, a large amount of renewable energy in the microgrid is intermittent and random, which brings great challenges to the scheduling and operation of the microgrid. [0003] At present, the microgrid economic scheduling problem is usually solved using traditional planning algorithms or heuristic algorithms. However, it is difficult for traditional planning algorithms to avoid local optimal solutions for nonlinear and non-co...

AI Technical Summary

Problems solved by technology

However, it is difficult for traditional planning algorithms to avoid local optimal solutions for nonlinear and non-convex problems. Heuristic algorithms can solve nonlinear and non-convex problems, but there are problems such as slow convergence speed and weak generalization

Moreover, the above algorithms often rely on the accurate prediction of the uncertainty of renewable energy output and load fluctuations, and cannot adapt to dynamic changes in source loads.

Images