Model predictive control in heating and cooling networks : A case study of an urban district in Stockholm

Sammanfattning: This work presents a model predictive control system for heating and cooling supply planning in an urban heating and cooling network. The control approach addresses the need for strategic operation of distributed production technologies and thermal energy storage in increasingly complex heating and cooling networks. Predictive optimization handles this complexity with an optimization strategy taking future demand, prices, and energy source availability into consideration. The model predictive control is integrated in a model built in a co-simulation approach. The co-simulation approach allows for models to run in their own simulation environments, preserving their levels of detail.  The model is adapted to a case study of an urban district under construction in Stockholm. Yearly simulations of the network and comparisons of the outcome when operated by the model predictive controller and by a reference rule-based controller are performed. The results show performance improvements in the form of reduced operational costs of 9.7 % and 18.8 % reduced carbon emissions, depending on how the objective function of the model predictive controller is formulated. An objective function aiming to minimize district heating imports is also formulated. While that objective function decreases the imports compared to the other objective functions, it increases the imports compared with the reference scenario, albeit from an already low share in the total energy supply of 0.2 %. A sensitivity analysis is performed to investigate the robustness of the control system. The sensitivity analysis shows that the reference controller is not robustly programmed for variations in parameters compared with the model predictive controller, which performs consistently better with both increases and decreases of the parameter sizes.  Future work could include detailed modelling in other simulation tools integrated in the co-simulation platform. Another possibility is developing a closed-loop system approach which would include, for example, feedback from the buildings’ indoor temperatures. This would allow for the utilisation of the buildings’ thermal mass as thermal energy storage. Lastly, more detailed economic and environmental calculations, such as life-cycle analysis or investment calculations, would further emphasize the real-world applicability of the findings.