Model predictive control for a university heat prosumer with data centre waste heat and thermal energy storage

Data centres (DCs) are energy-intensive facilities that convert most of their energy use into waste heat. Given the rapidly increasing energy and environmental impacts of DCs, and the need to optimize regional energy structures, there is an increasing effort to recover DC waste heat for district heating (DH) systems. However, previous research mainly focused on exploring the possibilities and proposing technical solutions for capturing DC waste heat for DH systems. They rarely investigated solutions on optimal control of the DH system after recovering DC waste heat, particularly for a DC waste heat-based heat prosumer with thermal energy storage (TES). Therefore, this study applied a model predictive control (MPC) scheme for a university heat prosumer with DC waste heat and water tank TES by simulation. In the framework, the objective function minimized the overall energy cost considering the dynamic heating and electricity prices simultaneously, and the incorporated model described system dynamics including DC waste heat recovery units, TES, and campus DH system. The MPC framework was demonstrated to be more effective than a traditional rule-based control approach in terms of 1) providing more stable chilled water for the DC cooling system and 2) cutting monthly energy costs by up to 3.2%.