Optimal Design of Islanded Microgrids Considering Distributed Dynamic State Estimation

This article proposes an optimal zone clustering algorithm of islanded microgrids (IMG) based on supply adequacy taking into account the dynamic performance of distributed state estimation units. The IMG is partitioned into several localized, yet coupled zones, where each zone is responsible for its local state estimate and performs data fusion to reach consensus for shared state variables between zones. The technique proposes a novel algorithm to optimally define the placement of the virtual boundaries of the zones by minimizing the potential power transfer between adjacent zones. The proposed algorithm adopts the distributed particle filter (DPF) technique for the state estimation process. The proposed algorithm also has the ability to come up with one optimal configuration considering different events and scenarios that might occur in the IMG. Monte Carlo simulations demonstrate the efficacy of the proposed technique in the presence of severely corrupted measurements and state values as well as displaying tolerance to major load changes within the IMG. The DPF shows similar performance when compared to its centralized implementation while also providing computational savings by a factor of the number of zones.

Automated summary

This article introduces an optimal zone clustering algorithm for islanded microgrids (IMG) based on supply adequacy, utilizing distributed particle filter (DPF) technique for state estimation and optimizing the virtual boundaries of zones. The proposed algorithm considers various events and scenarios to come up with the best configuration, showing efficacy in Monte Carlo simulations even under severely corrupted measurements. The DPF performs similarly to its centralized implementation with computational savings based on the number of zones.