Scalable Predictive Control for District Heating Networks

Abstract

THE transition to sustainable energy requires cleaner and more efficient ways to consume heat.We are used to our homes and offices being warm, yet behind this expectation lies infrastructure that must continuously balance supply and demand: heat must be produced, transported, and delivered exactly when and where it is needed. District heating networks accomplish this by circulating hot water through underground pipelines, connecting heat sources to thousands of buildings across a city. Operating such a network efficiently is far from trivial. Decisions made now about how much heat to produce, at what temperature, and through which routes have consequences that unfold over hours as hot water slowly travels through kilometers of pipe. Getting these decisions right can mean significant cost savings; getting them wrong leads to wasted energy or discomfort.
This challenge is fundamentally one of planning under complexity. The operator must anticipate future demand, account for heat losses during transport, respect physical limitations of pumps and pipes, and respond to fluctuating energy prices, all while the state of the network is only partially observable through a limited set of sensors. Traditional approaches rely on simple rules and operator experience, which suffice for conventional high-temperature networks but fall short as renewable sources have lower temperatures, requiring more advanced anticipative control strategies.
This thesis develops computational methods that enable district heating networks to be operated efficiently in real time. The underlying physics lead to mathematical optimization problems that are, in their original form, too complex to solve within practical time limits. This thesis introduces techniques that reformulate these problems into more tractable forms, decompose them across time to reduce computational burden, and learn efficient representations from operational data.We also address the question of where to place sensors to best reconstruct the network state, and establish theoretical conditions that guarantee stable closed-loop operation.
These methods are validated on realistic network models and compared against conventional rule-based strategies. The results show that predictive control can meaningfully reduce operating costs while improving constraint satisfaction, that our sensor placement method provides a practical tool for selecting informative measurement locations under tight instrumentation budgets, and that computational decomposition and data-driven surrogate models each bring solve times within real-time limits without sacrificing performance. We further find evidence of inherent stability properties of the closed-loop system, even in the absence of terminal constraints. Together, these findings demonstrate that the fundamental barriers to deploying advanced control in district heating networks can be systematically addressed.