Distributed predictive control design to achieve economically optimal power flow for DC microgrid clusters

Abstract

DC microgrid clusters are a collection of interconnected microgrids that allow for flexible power flow, leading to economic benefits and improved resilience from distributed generation. However, managing power flow among interconnected microgrids with different components such as photovoltaic, wind turbine, and battery energy storage systems, as well as various dynamic operation scenarios, presents a significant challenge for proportional-integral (PI)-based controllers. To address this challenge, this paper proposes a distributed predictive control design in the hierarchical control paradigm that aims to achieve economically optimal power flow (EOPF) for DC microgrid clusters. The predictive controller considers multiple objectives optimisation, including generation cost models, converter losses, and transmission losses over both local lines and tie-lines, and design and implementation of the two-layer tertiary control for the EOPF of DCMGCs are presented. Hardware-in-the loop (HIL) experimental results demonstrate the effectiveness of the controller design.