A Hierarchically Coordinated Operation and Control Scheme for DC Microgrid Clusters Under Uncertainty

In the existing works of microgrid clusters, operation and real-time control are normally designed separately in a hierarchical architecture, with the real-time control in the primary and secondary levels, and operation in the tertiary level. This article proposes a hierarchically coordinated control scheme for DC MG clusters under uncertainty. In each MG, the tertiary level controller optimizes the operating cost in the MG by taking into account the real-time uncertainties of renewable generations and loads deviated from the forecasting data; and the primary controller responds to the real-time power fluctuations through an optimised droop curve. The hierarchically coordinated optimization problem is formulated to optimize the power set points and droop curve coefficients simultaneously under uncertainties using an adjustable robust optimization model. For the MG cluster, the energy sharing of each MG in the cluster is optimized to minimize the total operating cost and the transmission loss. The overall optimization problem is solved in a distributed manner by alternating direction method of multipliers (ADMM) where each MG entity only exchanges boundary information (i.e. the power exchange of MG entity with the MG cluster), thus information privacy and plug-and-play feature of each MG are guaranteed. The proposed approach optimally coordinates the operation and real-time control layers of a DC MG cluster with uncertainties; it achieves decentralized power sharing at the real-time control layer and distributed optimization at the operation layer, featuring high scalability, reliability and economy. Case studies of a DC MG cluster are conducted in Matlab/Simulink in order to demonstrate the effectiveness of the proposed approach.

Automated summary

This article proposes a hierarchically coordinated control scheme for DC MG clusters under uncertainty, optimizing power set points and droop curve coefficients simultaneously to minimize operating costs and transmission loss. The approach ensures information privacy and plug-and-play feature, featuring decentralized power sharing and distributed optimization.