The independent operation of hydropower

photovoltaic (PV) and energy storage stations is constrained by transmission channel capacity

leading to frequent curtailment of water and PV power

which limits the clean energy absorption capacity of power grids. To address such an issue

this paper describes an optimal scheduling method for hydro-PV-storage complementary systems based on the Asynchronous Advantage Actor-Critic (A3C) algorithm

which is applicable to large-scale hydro-PV-storage coordinated operation scenarios. First

an operational scenario of hydro-PV-storage stations is constructed

and an optimal scheduling model is built based on the short-term versus medium- and long-term complementary guidance mechanism. Then

for a hydro-PV-storage complementary system

we transform its optimal scheduling problem into a Markov decision process

and achieve efficient strategy exploration and learning via deep reinforcement learning algorithms. Finally

we validate the method through application to such a system in the Yarkant River basin

Xinjiang. Results show the A3C algorithm stably converges to high reward values and improves system-absorbed electricity significantly with its computational cost notably lower than other algorithms

demonstrating its promising practical application value.