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The increasing penetration of renewable energy resources has transformed theenergy system from traditional hierarchical energy delivery paradigm to adistributed structure. Such development is accompanied with continuousliberalization in the energy sector, giving rise to possible energy tradingamong networked local energy hub. Joint operation of such hubs can improveenergy efficiency and support the integration of renewable energy resource.Acknowledging peer-to-peer trading between hubs, their optimal operation withinthe network can maximize consumption of locally produced energy. However, forsuch complex systems involving multiple stakeholders, both computationaltractability and privacy concerns need to be accounted for. We investigate bothdecentralized and centralized model predictive control (MPC) approaches for anetwork of energy hubs. While the centralized control strategy offers superiorperformance to the decentralized method, its implementation is computationallyprohibitive and raises privacy concerns, as the information of each hub has tobe shared extensively. On the other hand, a classical decentralized controlapproach can ease the implementation at the expense of sub-optimal performanceof the overall system. In this work, a distributed scheme based on a consensusalternating direction method of multipliers (ADMM) algorithm is proposed. Itcombines the performance of the centralized approach with the privacypreservation of decentralized approach. A novel multi-horizon MPC framework isalso introduced to increase the prediction horizon without compromising thetime discretization or making the problem computationally intractable. Abenchmark three-hub network is used to compare the performance of the mentionedmethods. The results show superior performance in terms of total cost,computational time, robustness to demand and prices variations.

Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs