Intelligent power flow control of AC/DC hybrid transmission corridors using safe reinforcement learning agents

As renewable generation progressively displaces conventional generators, power flow through geographically constrained transmission corridors increasingly approaches or violates thermal and stability limits, exposing the grid to congestion-induced renewable curtailment and cascading-failure risks. Traditional real-time dispatch practices, which rely on precomputed look-up tables and operator heuristics, prove inadequate when faced with rapidly growing uncertainties arising from high penetrations of wind and photovoltaic generation. This paper presents a safe reinforcement learning (SRL)-driven coordinated control framework that simultaneously regulates embedded HVDC links and dispatchable generators to enhance the transfer capability of AC/DC hybrid transmission corridors. A perturbation-based sensitivity approach distills the generator fleet into a compact subset whose output variations most strongly affect the transmission corridor power flow, effectively compressing the decision dimensionality. The sequential decision task is formulated as a Markov Decision Process model, where SRL agents are trained to govern HVDC flow and generator redispatch, under a maximum-entropy actor-critic framework, yielding policies that are simultaneously exploratory, reward-seeking, and constraint-respecting. Extensive simulation experiments and commissioning on the Yangtze River-crossing transmission corridor confirm that the SRL agent's policies elevate the mean aggregate transfer by 629 MW and raise the delivery ceiling by 807 MW, peaking at 2761 MW in heavily stressed scenarios.