Distributed Control and Communication Strategies in Networked Microgrids

This paper deals with optimal scheduling of networked microgrids (NMGs) considering resilience constraints. The proposed scheme attempts to mitigate the damaging impacts of electricity interruptions by effectively exploiting NMG capabilities. A three-stage framework is proposed. In Stage 1, the optimal scheduling of NMGs is studied through determining the power transaction between the NMGs and upstream network, the output power of distributed energy resources (DERs), commitment status of conventional DERs as well as demand-side reserves. In Stage 2, the decisions made at Stage 1 are realized considering uncertainties pertaining to renewable generation, market price, power consumption of loads, and unintentional islanding of NMGs from the upstream network and resynchronization. Stage 3 deals with uncertainties of unintentional islanding of each MG from the rest of islanded NMGs and resynchronization. The problem is formulated as a mixed-integer linear programming problem and its effectiveness is assured by simulation studies.

Networked microgrids (NMGs) are developing as a viable approach for integrating an expanding number of distributed energy resources (DERs) while improving energy system performance. NMGs, as compared to typical power systems, are constructed of many linked microgrids that can function independently or as part of a more extensive network. This allows NMGs to be more flexible, dependable, and efficient. The present study comprehensively investigates architecture, communication, and cybersecurity issues in NMGs. This comprehensive study examines various aspects related to networked microgrids (NMGs). It explores the architecture of NMGs, including control techniques, protection, standards, and the challenges associated with their adoption. Additionally, it investigates communication in NMGs, focusing on the technologies, protocols, and the impact of communication on the functioning of these systems. Furthermore, this study addresses cybersecurity challenges specific to NMGs, such as diverse cyberattack types, detection and mitigation strategies, and the importance of awareness training. The findings of this study offer valuable insights for NMG researchers and practitioners, emphasizing critical aspects that must be considered to ensure the safe and efficient operation of these systems.

In collaborative networked microgrids (NMs), distributed energy resources (DERs) utilize intelligent Internet of Things (IoT)-based controllers to coordinately support various smart community functions. Meanwhile, privacy and security issues occur when IoT-based controllers interact with each other. This article presents a cryptography-based, programmable control (crypto-control) scheme to provably preserve the privacy of DERs while ensuring fast, flexible distributed control in NMs. Specifically, it makes the following contributions: 1) a programmable crypto-control-based NMs (PCNMs) architecture, where crypto-controllers are fully virtualized, is devised; 2) a novel dynamic encrypted weight addition (DEWA) approach, which integrates an enhanced partial homomorphic encryption and a secret sharing scheme, is devised to ensure privacy preserving of distributed controls; 3) the DEWA privacy-preserving property is mathematically analyzed; and 4) a real-time DEWA-based PCNMs testbed is deployed by incorporating DEWA, real software-defined networking switches, and IoT devices. The deployable crypto-control scheme is interfaced with and thoroughly verified in a Real-Time Digital Simulator environment, and the experimental results validate the effectiveness, benefits, and superiority of DEWA-based PCNMs. The inherent resilience of the DEWA-based PCNMs is validated by small signal stability analysis.

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities.

We propose a distributed algorithm for online energy management in networked microgrids with a high penetration of distributed energy resources (DERs). A high penetration of DERs introduces high uncertainty of power generation to the microgrids. In general, the state-of-the-art forecasting for non-dispatchable DERs such as solar energy is not sufficiently accurate, which results in inaccurate energy scheduling. To address the high uncertainty issue in the networked microgrids, we propose an online energy management based on the online alternating direction method of multipliers algorithm with the past power generation information from the DERs. The online algorithm provides less conservative schedule than the robust optimization-based approach. The effectiveness of the proposed algorithm is verified by various numerical examples.

Mixed integer linear programming (MILP)–based distributed energy management for networked microgrids embedded modern distribution systems is proposed. Considering the diverse ownership of microgrids, distributed energy resources (DERs) that interface directly with utilities and responsive loads, an alternating direction method of multipliers–based distributed framework was formulated for the scheduling of networked microgrids embedded modern distribution systems by adjusting nodal price signals iteratively. In addition, to make the formulated optimization problems resolvable through more accessible and popular MILP solvers, different linearisation techniques were employed to transform the nonlinear terms into linear or mixed integer linear formats. The proposed MILP‐based distributed method preserves all participants' autonomy (e.g., microgrids, DERs that interface directly with utilities and responsive loads), while incentivising them to actively participate in the distribution system operation with price signals. The proposed method is validated with results of numerical simulation using a modern distribution system consisting of multiple networked microgrids, DERs that interface directly with utilities, as well as responsive loads.

Networked microgrids are clusters of geographically-close, islanded microgrids that can function as a single, aggregate island. This flexibility enables customer-level resilience and reliability improvements during extreme event outages and also reduces utility costs during normal grid operations. To achieve this cohesive operation, microgrid controllers and external connections (including advanced communication protocols, protocol translators, and/or internet connection) are needed. However, these advancements also increase the vulnerability landscape of networked microgrids, and significant consequences could arise during networked operation, increasing cascading impact. To address these issues, this report seeks to understand the unique components, functions, and communications within networked microgrids and what cybersecurity solutions can be implemented and what solutions need to be developed. A literature review of microgrid cybersecurity research is provided and a gap analysis of what is additionally needed for securing networked microgrids is performed. Relevant cyber hygiene and best practices to implement are provided, as well as ideas on how cybersecurity can be integrated into networked microgrid design. Lastly, future directions of networked microgrid cybersecurity R&D are provided to inform next steps.

A continuation method considering the dynamics of distributed energy resources (DERs) is presented to identify Hopf bifurcations in the networked microgrids induced by the integration of electrical vehicle (EV) charging stations. The dynamic model of networked microgrids is developed for bifurcation analysis. An adaptive predictor-corrector approach is presented for inclusively searching for equilibrium points in the space of bifurcation parameters. Extensive numerical results have demonstrated and validated the prediction of the subcritical Hopf bifurcation, based on which corresponding control strategies can be employed to stabilize the system when necessary. These features make the continuation method an effective tool for determining the system's critical operations and providing early warnings.

This paper develops a novel distributed iterative event-triggered control scheme for a master-slave-organized dc microgrid network with limited communication bandwidth. The proposed scheme can synchronize the voltage of multiple distributed energy resources (DER) to their desired value. Moreover, the optimal load sharing for their economic operation (e.g., minimize the total generation cost) can be achieved through a low bandwidth communication network. The designed controllers are fully distributed and only triggered at their own event time, which effectively reduces the frequency of controller updates compared with continuous-time feedback control. Eventually, each DER only requires the local voltage and current measurement from its own and some nearest (but not all) neighbors at given event-triggered time through limited-bandwidth communication links. The Lyapunov technique is employed to derive the event-triggered conditions that guarantee the stability. Furthermore, the lower bound of the interevent intervals is captured by the proposed iterative algorithm to exclude Zeno behaviors. Different cases in MATLAB/SimPowerSystems are investigated and results demonstrate the effectiveness and the performance of the proposed approach.

This article presents an event-triggered distributed secondary control with predictive compensation based on the model-free predictive control under Denial-of-Service (DoS) attacks in ac/dc-networked microgrids. First, models of ac/dc networked microgrids in both electric network and communication network are established. On this premise, event-triggered distributed secondary control is proposed to solve the problems of strong communication burden and low-power distribution accuracy. Besides, aiming at the impact of DoS attacks on distributed secondary control, a compensation algorithm based on model-free predictive control is designed to estimate the control variables when the DoS attack occurs, which can improve the control performance and maintain the stable operation without a specific system structure system. Then, the convergence of event-triggered distributed secondary control with the condition of whether DoS attacks happen are analyzed. Finally, the effectiveness of the proposed control is verified on the hardware-in-loop (HIL) simulation platform consisting of the RT-LAB simulator, MATLAB/Simulink simulation model, and DSP controller.

At present, the distributed cooperative control method has been widely adopted in the secondary control of the conventional droop-based microgrid (MG). However, its application in the secondary control of Virtual Synchronous Generator (VSG) is relatively lagging. Based on the conventional virtual governor, this paper designs the frequency distributed secondary control strategy of VSG based on the theory of nonlinear heterogeneous multi-agents and presents the distributed active power sharing control strategy. The frequency will converge to the frequency reference value through the consensus protocol, instead of relying on the virtual frequency modulation coefficient. To reduce the communication burden, a control strategy based on event-triggered is adopted in the distributed secondary control. The distributed control is used in frequency strategy, compared with the VSG secondary control proposed previously, the distributed generations (DGs) can collaboratively work. Compared with the existing conventional droop-based distributed secondary control, the proposed scheme retains the characteristics of VSG and can provide virtual inertia to MG. An islanded microgrid test system was established to verify the proposed control strategy. The results show that the proposed control strategy can restore the frequency to the reference value and accurately share the active power, while greatly reducing the communication burden.

Blockchain for decentralized transactive energy management system in networked microgrids

High-impact and low-probability extreme events have occurred more frequently than before because of rapid climate change, which can seriously damage distribution systems. However, conventional distribution management can be dysfunctional after an event, destroying its centralized supervision towards resilience enhancement. In this context, networked microgrids (NMGs) with distributed energy resources provide a viable solution for the resilience enhancement of distribution systems. Existing literature tends to employ model-based optimization approaches for resilient operations of NMGs, which require complete system models and can be time-consuming. To address these challenges, this paper suggests a decentralized framework for resilience-oriented coordination of NMGs and proposes a novel multi-agent reinforcement learning (MARL) method to solve it. Specifically, the proposed MARL method develops an efficient credit assignment scheme for NMGs to learn their contributions to the distribution system resilience via the Shapley Q-value technique with more efficient resilience enhancement. Case studies based on two modified IEEE 15- and 69-bus distribution networks are conducted to validate the effectiveness of the proposed MARL method in enabling effective coordination among NMGs and providing a high resilience level.

This paper focuses on a fully-distributed peer-to-peer (P2P) control scheme for voltage restoration and reactive power sharing of multiple inverter-based distributed energy resources (DRs) in AC microgrids (MG). The proposed distributed gossip control (DGC) strategy is P2P fully distributed enabled through the discrete-time communication; moreover, each DR only requires local voltage and current measurement from its own and some (but not all) nearest neighbors for the voltage and reactive power sharing control by using low-bandwidth communication links. The gossip communication protocol is used to update the control inputs to not only restore the voltage magnitudes of critical load points to a desired value, but also ensures accurate reactive power sharing for each local smart load. Having the proposed distributed controllers implemented on local DRs, no central controller is any longer required bringing about several advantages, e.g., plug-and-play property. Simulation studies in a MG network of MATLAB/SimPowerSystems demonstrate the effectiveness and applicability of the proposed control strategy.

<abstract> <p>This study proposes a centralized control system for an islanded multivariable minigrid to improve its performance, stability and resilience. The integration of renewable energy sources and distributed energy storage systems into microgrid networks is a growing trend, particularly in remote or islanded areas where centralized grid systems are not available. The proposed control system is designed to be implemented at two levels a high-level control system and a low-level control system. Hence, the high-level control system balances energy resources and demand, makes decisions for effective resource utilization and monitors energy transactions within the minigrid. Real-time data from various sources and advanced algorithms are used to optimize energy management and distribution enabling the integration of renewable energy sources and enhancing the resilience of the minigrid against power outages.</p> <p>Moreover, the low-level control system monitors energy parameters such as voltage, current, frequency and mechanical energy. The control system ensures these parameters remain within the specified range, maintaining system stability and ensuring efficient energy distribution. It also protects the minigrid against power outages improving system reliability and security. Finally, the proposed centralized control system offers a promising solution for improving the performance, stability and resilience of microgrid networks. The system provides real-time monitoring, efficient energy management and distribution, and the integration of renewable energy sources. These results have important implications for the development and deployment of microgrid networks in remote or islanded areas.</p> </abstract>