Data-driven online control for real-time optimal economic dispatch and temperature regulation in district heating systems
This addresses operational inefficiencies in district heating systems, which are critical for energy management, though it appears incremental as it builds on existing control methods with data-driven enhancements.
The paper tackled the problem of coordinated economic dispatch and temperature regulation in district heating systems under uncertain conditions by developing a data-driven online control framework that converges to the economically optimal operating point without relying on disturbance forecasts, achieving stable near-optimal operation and strong empirical robustness in simulations on an industrial-park system in Northern China.
District heating systems (DHSs) require coordinated economic dispatch and temperature regulation under uncertain operating conditions. Existing DHS operation strategies often rely on disturbance forecasts and nominal models, so their economic and thermal performance may degrade when predictive information or model knowledge is inaccurate. This paper develops a data-driven online control framework for DHS operation by embedding steady-state economic optimality conditions into the temperature dynamics, so that the closed-loop system converges to the economically optimal operating point without relying on disturbance forecasts. Based on this formulation, we develop a Data-Enabled Policy Optimization (DeePO)-based online learning controller and incorporate Adaptive Moment Estimation (ADAM) to improve closed-loop performance. We further establish convergence and performance guarantees for the resulting closed-loop system. Simulations on an industrial-park DHS in Northern China show that the proposed method achieves stable near-optimal operation and strong empirical robustness to both static and time-varying model mismatch under practical disturbance conditions.