Data-driven online control for real-time optimal economic dispatch and temperature regulation in district heating systems

TL;DR

This paper introduces a data-driven online control method for district heating systems that achieves near-optimal operation without relying on disturbance forecasts, using a novel DeePO-based controller with convergence guarantees.

Contribution

It develops a new data-driven control framework embedding economic optimality into temperature dynamics, with a DeePO-based controller and theoretical performance guarantees.

Findings

Achieves stable near-optimal operation in simulations.

Demonstrates robustness to model mismatch and disturbances.

Provides convergence and performance guarantees for the control system.

Abstract

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.