Paper detail

Economic zone data-enabled predictive control for connected open water systems

The real-time operation of open water systems is essential for ensuring operational safety, satisfying operational requirements, and optimizing energy usage. However, existing rule-based control strategies rely heavily on human experience, while model-based approaches depend on accurate hydrodynamic models, which limit their applicability to water systems with complex dynamics and uncertain disturbances. In this work, we develop a fully data-driven, zone-based control framework with adaptive control target zone selection for safe and energy-efficient operation of connected open water systems. Specifically, we propose a mixed-integer economic zone data-enabled predictive control (DeePC) approach that aims to maintain the water levels of the branches within the desired water-level zone while reducing real-time operational energy consumption. The DeePC-based approach enables direct use of input-output data for predictive control, eliminating the need for explicit dynamic modeling. To handle multiple control objectives with different priorities, we employ lexicographic optimization and reformulate the traditional DeePC cost function to incorporate zone tracking and energy consumption minimization objectives. Additionally, Bayesian optimization is utilized to determine the control target zone, which enables an effective trade-off between zone tracking and energy consumption in the presence of external disturbances. Comprehensive simulations and comparative analyses demonstrate the effectiveness of the proposed method. The proposed method maintains water levels within the desired water-level zone for 97.04% of the operating time, with an average energy consumption of 33.5 kWh per 0.5 hour. Compared to rule-based control method, the proposed method lowers zone-violation frequency by 74.96% and the average energy consumption by 22.44%.