Distributed Stochastic Model Predictive Control with Temporal Aggregation for the Joint Dispatch of Cascaded Hydropower and Renewables

This paper addresses the real-time energy dispatch of a hybrid system comprising cascaded hydropower plants, wind, and solar units, jointly participating in the day-ahead energy market under inflow, renewable generation, and price uncertainties. Traditional scenario-based stochastic model predictive control (MPC) faces severe computational bottlenecks due to the complexity arising from the temporal, asset, and scenario dimensions of this control problem. To address this, we propose a novel control scheme that combines time series aggregation (TSA) with distributed stochastic MPC. TSA is applied exclusively to the tail of the MPC prediction horizon to preserve real-time accuracy, while distributed optimization enables decomposition across assets and scenarios. Notably, the controller offers a formal performance guarantee through theoretically validated bounds on its approximation error. Simulations on a real-world case study confirm the controller's effectiveness, achieving a 42% reduction in execution time compared to centralized full-scale MPC.