Networked microgrids have been instrumental in facilitating seamless integration of distributed energy resources (DERs) into the power grid. Conversely, managing such a large-scale cyber-physical system of DERs in a distributed environment presents significant challenges in terms of scalability and convergence time of algorithm. This highlights the need to have a scalable and computationally efficient distributed energy management framework. This article presents a hierarchical distributed consensus-based framework to address energy management problem in networked microgrids. The proposed approach utilizes a hierarchical structure to implement phasewise consensus, enabling parallel consensus that accelerates the convergence of algorithm. The effectiveness of the proposed method is affirmed by benchmarking against centralized solution, demonstrating its ability to converge to the optimal solution. Furthermore, simulations across different network topologies validate the approach, showcasing faster convergence and reduced communication overhead compared to the conventional distributed consensus method. Finally, Monte Carlo simulations demonstrate the scalability of the proposed approach, highlighting the feasibility in real-time applications.

The growing integration of distributed energy resources (DERs) is transforming energy management systems (EMS) framework, driving a shift from centralized to distributed and further to hierarchical distributed systems. To ensure the effective implementation of hierarchical EMS, DERs must be clustered to form an optimal partition of the network that ensures fast convergence. This article presents a centrality-based clustering framework to organize the DERs to form a hierarchical distributed structure in networked microgrids. The proposed framework incorporates centrality measures to identify the network leaders, coupled with a distance-based partitioning algorithm to form the optimal partition. Furthermore, an ensemble-based method is used to determine the optimal number of clusters for the most efficient partitioning. Simulation results demonstrate that the proposed clustering framework significantly improves convergence speed in a hierarchical distributed system. The resulting optimal partition reduces the number of iterations required to converge by 52% compared to distributed framework and by 29.6% compared to suboptimal partition. Furthermore, Monte Carlo simulations showcase the logarithmic improvement in convergence performance achieved through optimal partition.

To accommodate the exponential integration of distributed energy resources (DERs) into the power grid, there is a pressing need for a scalable and computationally efficient networked microgrid energy management framework. In this context, a Hierarchical Distributed Consensus (HDC) based approach has emerged as a promising solution. A key prerequisite to ensure an effective implementation of the HDC framework is to obtain an optimal hierarchical partition from the network. This paper presents a framework for determining the optimal number of clusters using the ensemble method to form a hierarchical structured networked microgrid. The proposed algorithm incorporates the consensus matrix and the elbow method to analyze and pinpoint the best possible network configuration. The simulation results highlight the importance of obtaining the optimal number of clusters and interdependence of cluster configuration to speed of consensus convergence in the network.

Power restoration is a vital task in post-disaster scenarios. Microgrid-based strategies offers a promising approach that can ensure fast and timely restoration of resources. However, in a distributed microgrid environment, the overall performance is highly dependent on the efficiency and reliability of the communication layer. Identifying critical links can better coordinate the limited communication resources available post disaster. Connectivity Rank Index (CRI) has been proven to be an effective edge centrality metric for evaluating communication links in distributed energy management systems. In this paper, a variant of CRI called Local-CRI, is proposed specifically tailored for hierarchical distributed network structures. Numerical simulations shows that the CRI effectively reflects the importance of communication links in relation to consensus performance, outperforming existing methods. Additionally, the Local-CRI proves effective in quantifying the impact of communication link failures on consensus convergence, particularly within hierarchical distributed network.

The increasing demand for energy has necessitated the expansion of Distributed Energy Resources (DERs), highlighting the critical need for an effective and reliable Energy Management System (EMS). This paper introduces a novel approach to solving the economic dispatch problem in asynchronous networked microgrid systems through a hierarchical distributed gossip consensus-based method. The proposed approach combines the strengths of gossip algorithms with hierarchical consensus strategies, specifically designed to enhance efficiency in asynchronous settings. The effectiveness of this method is validated through extensive simulations, which demonstrate faster convergence times and improved system performance compared to standard distributed gossip consensus configurations. Furthermore, the Monte-Carlo simulations showcases the scalability of the proposed approach, illustrating its suitability in real-time applications.

The fast adoption of distributed energy resources (DERs) to meet the ever-growing energy demand has created a need for an efficient and reliable energy management system (EMS). This paper proposes a weighted consensus based method for solving the economic dispatch problem in hierarchical distributed networked microgrid system. The combination of cluster size estimation and weighted consensus enable us to obtain an optimally operating energy management framework for our system. The proposed approach is validated through extensive simulations, demonstrating its effectiveness over existing methods in terms of convergence time and system performance optimization in networked microgrids.