Distributed Model Predictive Control of Buildings and Energy Hubs

TL;DR

This paper compares centralized, decentralized, and distributed model predictive control strategies for energy hubs and buildings, demonstrating that distributed control achieves near-centralized performance with lower computational costs through dual decomposition.

Contribution

It introduces a distributed MPC approach based on dual decomposition for energy management, balancing performance and computational efficiency.

Findings

Distributed control performs close to centralized control.

Distributed control reduces computational burden significantly.

Experimental validation confirms the method's reliability.

Abstract

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.