Enabling smart control by optimally managing the State of Charge of district heating networks

Digitalization and smart control of district heating networks are emerging as key features to make these systems flexible and optimal. However, since effective and scalable methods for large-scale systems are currently unavailable, the implementation of smart controllers can be challenging and time-consuming. This is addressed herein by proposing a novel approach to include the thermal capacity of the connected buildings in the optimal control of large-scale heating networks. A reduced-order model of the aggregated communities supplied by a large-scale network is used to define their State of Charge, which is exploited to store or retrieve energy when convenient, while maintaining indoor comfort. This concept is included in a Model Predictive Controller that optimizes power plant management and heat distribution. The results show that the controller successfully shaves heat supply peaks to different regions up to 16% and reduces the difference between distribution and soil temperature up to 20%. At the same time, the return temperature is kept close to the set-point of 35 degrees C, which is lower than the historical operation and further reduces distribution heat losses. The procedure can be easily replicated to optimize systems of different sizes and to support their transition to efficient, smart district heating networks.

Automated summary

This study introduces a novel approach to incorporate the thermal capacity of connected buildings into the optimal control of large-scale heating networks, aiming to enhance system flexibility and performance. The method, implemented through a Model Predictive Controller, successfully reduces heat supply peaks, decreases temperature differences, and minimizes energy losses, providing a promising solution for efficient and smart district heating networks.