A decomposition-coordination approach for distributed real-time control of urban drainage systems

Area of Science:

• Environmental Engineering
• Water Resource Management
• Control Systems

Background:

• Real-time control is crucial for urban drainage system (UDS) efficiency.
• Centralized control methods struggle with complexity, reliability, and scalability.
• Existing distributed control relies on expert knowledge or historical data, limiting adaptability.

Purpose of the Study:

• To propose a novel decomposition-coordination-based distributed model predictive control (DC-DMPC) approach for UDS.
• To enable global coordination in UDS without prior knowledge or centralized optimization.
• To enhance the operational resilience and adaptability of UDS.

Main Methods:

• Decomposition-coordination-based distributed model predictive control (DC-DMPC).
• Decomposition of UDS into independent subsystems.
• Alternating-direction optimization for inter-subsystem flow coordination.

Main Results:

• DC-DMPC significantly reduced flood and combined sewer overflow (CSO) volumes compared to rule-based control (RBC) and centralized model predictive control (CMPC).
• Under homogeneous rainfall, DC-DMPC reduced flood and CSO volumes by 82.5% and 23.1% over RBC, and 9.4% and 12.3% over CMPC.
• DC-DMPC demonstrated superior adaptability to inhomogeneous rainfall and maintained robustness during communication failures, showing 9.4% less resilience loss than CMPC.

Conclusions:

• The proposed DC-DMPC approach offers a prior-knowledge-free and scalable framework for UDS.
• DC-DMPC enhances operational efficiency by reducing optimization dimensionality while ensuring robustness.
• This method significantly improves the resilience of urban drainage systems against various operational challenges.