Optimal Coordination of District-Scale Multi-Energy Systems

A major challenge in the transition to a net-zero energy system revolves around the decarbonization of energy use for heating, cooling and transport via electrification, whilst simultaneously ensuring the security of a power system with high penetration of renewable energy generation. One possible way to address this challenge is to adopt a multi-energy systems approach, in which traditionally separate energy systems for the delivery of electricity, gas, heating and cooling are co-optimized as an integrated entity. A major benefit of this approach is that flexible distributed energy resources in non-electrical systems can be utilized to support the power grid. This chapter presents a novel multi-energy system optimization modeling framework, capable of rapidly formulating large-scale optimization problems. These problems can be readily integrated within model predictive control (MPC) schemes, providing a method for online energy management of a continuously evolving system. Such schemes can optimally manage distributed energy resources at a community or district scale, ensuring that all energy demands are met, networks are operated within acceptable safety limits and cost savings are delivered to both customers and network operators. Given the potentially large size of the resulting control problem, a multi-agent control architecture and associated coordination algorithms are also presented. These ensure that near-optimal, feasible control actions can be determined within timescales that are suitable for online energy management. In an exemplary case study, considering a 15 min sampling interval for a district comprising 84 buildings and multiple energy supply networks, a maximum computation time of approximately 55 min for a single controller is reduced to just over 1 s using the novel multi-agent MPC scheme, demonstrating the substantial benefit of the proposed approach.