Degradation-Aware Pumping Control of Variable-Speed Pumped Storage via Residual Reinforcement Learning
For operators of variable-speed pumped storage hydropower plants, this work provides a control method that simultaneously improves tracking accuracy and reduces component degradation, addressing a persistent trade-off.
Variable-speed pumped storage hydropower faces a conflict between tracking short-block dispatch commitments and limiting operational degradation. The proposed two-layer control architecture with residual reinforcement learning reduces best-efficiency-point tracking error by ~96% and cuts total degradation by up to ~56% compared to fixed-speed baselines.
Variable-speed pumped storage hydropower (VS-PSH) must honor short-block dispatch commitments while limiting the operational degradation that intensified regulation duty inflicts on its components. When a single controller pursues both aims at once, every tracking gain is paid for in degradation, a conflict that persists even under full model knowledge and look-ahead. This paper proposes a two-layer control architecture that separates the guaranteed commitment from the bounded learning. A deterministic feedforward-PI gate controller, auditable and certifiable for grid-connected operation, secures average power delivery over each five-minute block, while a residual reinforcement learning policy adjusts only the rotor speed within a fixed bound the gate loop can always absorb, so the worst-case command is bounded by construction. The speed policy tracks a demand-dependent best-efficiency-point reference and is trained against an operation-degradation index that combines off-best-efficiency hydraulic loss with power and actuation variation into one physically interpretable signal. Across normal and stressed dispatch, the proposed policy lowers best-efficiency-point tracking error by roughly 96\% relative to a fixed-speed baseline and cuts total degradation by up to about 56\% under the most demanding dispatch. It matches or slightly exceeds a full-information model-based optimizer in efficiency while preserving substantially tighter block tracking.