Scenario-Driven Marginal-Efficiency Graph Search for Supervisory Control of Heterogeneous Multi-Unit Cooling Plants

Supervisory control of heterogeneous multi-unit cooling plants should coordinate unit staging, load allocation, switching stability, and supply-water temperature reliability. Conventional rule-based control is easy to implement, but threshold-triggered staging and fixed allocation rules limit coordination under fast load variation. This study proposes a scenario-driven marginal-efficiency graph search method (S-ME-GS) that reformulates supervisory control as a receding-horizon unit-combination path-selection problem. A TCN forecaster generates short-term load trajectories from operating history, residual quantile bands represent forecast uncertainty, and a four-scenario classifier adjusts path-cost preferences. Marginal-efficiency allocation evaluates each candidate active-unit set, while layered dynamic-programming graph search compares feasible paths using energy, reserve, and transition costs. A scenario-dependent activation margin screens the selected path before only the first action is executed. During a 62-day TRNSYS-Python co-simulation, S-ME-GS reduced electricity consumption by 9.61% and 6.59% relative to RBC-A and RBC-B, respectively, while maintaining zero supply-temperature exceedance. Compared with ME-only, it reduced switching from 29.2 to 10.5 events per day with only 0.50% higher energy use. The planner solved each control step in 85.3 ms on average, supporting real-time deployment. These results show that S-ME-GS coordinates energy efficiency, reserve adequacy, and staging stability in an interpretable, computationally tractable framework.