  The increasing penetration of renewable energy resources has transformed the
energy system from traditional hierarchical energy delivery paradigm to a
distributed structure. Such development is accompanied with continuous
liberalization in the energy sector, giving rise to possible energy trading
among networked local energy hub. Joint operation of such hubs can improve
energy efficiency and support the integration of renewable energy resource.
Acknowledging peer-to-peer trading between hubs, their optimal operation within
the network can maximize consumption of locally produced energy. However, for
such complex systems involving multiple stakeholders, both computational
tractability and privacy concerns need to be accounted for. We investigate both
decentralized and centralized model predictive control (MPC) approaches for a
network of energy hubs. While the centralized control strategy offers superior
performance to the decentralized method, its implementation is computationally
prohibitive and raises privacy concerns, as the information of each hub has to
be shared extensively. On the other hand, a classical decentralized control
approach can ease the implementation at the expense of sub-optimal performance
of the overall system. In this work, a distributed scheme based on a consensus
alternating direction method of multipliers (ADMM) algorithm is proposed. It
combines the performance of the centralized approach with the privacy
preservation of decentralized approach. A novel multi-horizon MPC framework is
also introduced to increase the prediction horizon without compromising the
time discretization or making the problem computationally intractable. A
benchmark three-hub network is used to compare the performance of the mentioned
methods. The results show superior performance in terms of total cost,
computational time, robustness to demand and prices variations.
