  Model predictive control (MPC) strategies can be applied to the coordination
of energy hubs to reduce their energy consumption. Despite the effectiveness of
these techniques, their potential for energy savings are potentially
underutilized due to the fact that energy demands are often assumed to be fixed
quantities rather than controlled dynamic variables. The joint optimization of
energy hubs and buildings' energy management systems can result in higher
energy savings. This paper investigates how different MPC strategies perform on
energy management systems in buildings and energy hubs. We first discuss two
MPC approaches; centralized and decentralized. While the centralized control
strategy offers optimal performance, its implementation is computationally
prohibitive and raises privacy concerns. On the other hand, the decentralized
control approach, which offers ease of implementation, displays significantly
lower performance. We propose a third strategy, distributed control based on
dual decomposition, which has the advantages of both approaches. Numerical case
studies and comparisons demonstrate that the performance of distributed control
is close to the performance of the centralized case, while maintaining a
significantly lower computational burden, especially in large-scale scenarios
with many agents. Finally, we validate and verify the reliability of the
proposed method through an experiment on a full-scale energy hub system in the
NEST demonstrator in D\"{u}bendorf, Switzerland.
