Decentralised Supervisory Control of Networked Multi-Energy Buildings

A major challenge in the transition to a net-zero energy system is how to decarbonise energy use for heating and cooling via electrification, whilst ensuring the security of a power system with a high penetration of renewable energy generation. One possible way to simultaneously address these disparate objectives is to use model predictive control (MPC) to manage multi-vector energy consumption and storage in individual buildings, so that operational constraints in connecting networks are not violated. However, control of a large number of building energy assets and their multiple shared networks using standard MPC requires solution of optimisation problems which are large, non-convex and therefore intractable. In this paper, a novel MPC scheme is proposed in which the overall control problem is decomposed and solved in parallel by decentralised control agents. Since the uncoordinated actions of decentralised agents could cause congestion in connecting electricity and district heating and cooling networks, an energy flow coordinator is also introduced. This coordinator checks agent actions by solving optimal energy flow problems for each network and uses price signals to direct the search for a globally feasible solution. To improve computational efficiency when determining optimal energy flows, the coordinator utilises a novel model reformulation of a district heating and cooling network. An exemplary case study of a multi-energy district demonstrates that the control scheme ensures near-optimal economic performance, when compared with an equivalent centralised benchmark – in this case reducing the maximum computation time from over 55 minutes to just over 1 second. The approach is suitable for on-line management of buildings within a district, to both minimise costs to end users and to maintain secure, reliable operation of the connecting networks.