Search Results (715)

Amid the global transition toward sustainable energy systems, the hybrid energy storage system (HESS) plays a vital role due to its combined advantages of high energy density and high power density. However, distributed HESSs in islanded microgrids still lack effective management strategies for handling complex and abnormal operating conditions, which may compromise system stability. Therefore, this paper proposes a coordinated control and management strategy for distributed HESSs based on grid-forming (GFM) converters. First, a dynamic following decoupling algorithm based on actual power anchoring is proposed to eliminate the reverse active power regulation phenomenon during the initial transient period while enabling the frequency restoration process and the power transfer process to be completed independently. Second, to address communication interruptions in the multi-agent system, a communication weight update mechanism and a local degraded control strategy are designed to ensure that the system can still operate stably when communication is disconnected. Furthermore, through an information relay mechanism, a faulty converter is redefined as an information relay node to maintain the global communication topology of the multi-agent system under converter fault conditions. Finally, hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed control strategy, demonstrating its ability to enhance microgrid resilience and sustainability. Full article

Microgrids are important technologies for increasing the penetration of renewable energy sources (RESs). Compared with AC microgrids, DC microgrids avoid frequency regulation and reactive-power compensation. Moreover, many RES interfaces and energy storage systems (ESSs) are DC or DC-link based; therefore, they can be integrated into DC buses with fewer conversion stages, reducing conversion losses. Consequently, DC microgrids have attracted increasing attention. This paper reviews DC microgrid topologies, hierarchical control methods, and protection schemes. First, the representative topologies are compared from the perspectives of structural features, control implications, protection requirements, and application scenarios. Next, primary, secondary, and tertiary control strategies are analyzed, with emphasis on droop control, virtual impedance, virtual inertia, fractional-order control, communication delay, and energy management. Protection issues, including fault detection, fault interruption, and ground-fault protection, are then discussed with respect to topology–control interactions. Finally, future research trends and challenges for DC microgrids are summarized. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article

Microgrids are increasingly recognized as transformative and crucial constituents within advanced smart grid systems. This study introduces a decentralized energy management approach for interconnected microgrids that leverage renewable energy sources such as wind and solar, alongside distributed energy generators and storage mechanisms. An energy coalition manager (ECM) plays a key role in facilitating each microgrid’s integration to optimize power exchanges, enhance data communication, and reduce costs. The alternate-direction multiplier method is adapted to address optimization challenges, incorporating modifications to develop a censored version that enhances communication efficacy. This refined approach involves the exchange of information among neighboring entities, evaluated against a preset threshold. Through this precise comparison, ECMs strategically reveal their local variables to ensure convergence towards an optimal solution. A detailed case study was conducted to assess the performance, efficiency, and scalability of both methodologies comprehensively. Full article

Integrating green hydrogen systems into hybrid microgrids introduces nonlinear dynamics that compromise control stability during operational transitions. The performance of the advanced control loops depends on the latency and reliability provided by the communication infrastructure. This paper proposes a Software-Defined Networking (SDN) architecture integrated with an adaptive Quality of Service (AQoS) framework to support time-critical data flows in a hybrid microgrid with green hydrogen integration. An emulated network topology in GNS3, with OpenDaylight as the SDN controller and Open vSwitch as the forwarding plane, reproduces IEC 61850 traffic patterns, including GOOSE, control set-points and MMS. These traffic classes coordinate key microgrid components, including electrolysers, fuel cells and battery storage. Experimental results show that the SDN-AQoS framework reduces latency variance by 60% compared to unmanaged SDN configurations and delivers 49.4% higher throughput than traditional TCP/IP networks under congestion. The SDN-AQoS configuration achieves a median latency of 9.68 ms, keeping 97.5% of the measurements below the 20 ms safety threshold for electrolyser control. This level of reliability represents a substantial improvement over the plain TCP/IP at 90%, unmanaged SDN at 66.7% and static QoS policing at 60%. QoS rules are configured through the RESTCONF interface and remain fixed during each experiment while enabling the future integration of reinforcement learning agents for autonomous QoS adaptation. At the same time, this framework supports the bounded communication delay required to sustain frequency control and electrolyser safety coordination in low-inertia hydrogen microgrids during network congestion. The physical layer impact of these communication improvements remains a subject of future hardware-in-the-loop validation. Full article

Smart cities increasingly depend on electrical grid infrastructures capable of operating under high levels of digitalization, decentralization, and intelligence while maintaining reliability, security, and governance at the city scale. However, conventional power systems, historically designed for centralized generation and passive operation, are poorly aligned with the operational complexity, multi-actor coordination, and cross-sector integration characteristic of urban energy systems. This review develops an architecture-first perspective on smart-city electrical grids, tracing their evolution from digitalized power networks to decentralized and AI-enabled urban energy ecosystems. Rather than focusing on individual technologies, the study evaluates grid architectures using a multi-layer framework that integrates physical grid infrastructure, distributed energy resources and microgrids, communication and data platforms, intelligence placement, cybersecurity exposure, and governance accountability. Smart-city grid architectures are assessed using deployability beyond pilot projects, auditability, and regulatory alignment as primary evaluation criteria alongside conventional technical considerations. Through this perspective, the review explains a recurring pattern observed in the literature: many technically mature smart-grid solutions fail to scale in real urban deployments due to architectural fragmentation and governance constraints. By synthesizing insights from power systems engineering, information and communication technologies, and smart-city research, the paper highlights architectural trade-offs related to decentralization, interoperability, resilience under compound threats, and assisted autonomy. The resulting framework supports researchers, system designers, and policymakers in the coordinated development of resilient, secure, and governable electrical grids for future smart-city energy systems. Full article

Microgrids (MGs) have lately received significant attention from researchers as a contemporary solution to better employ the high penetration of renewable energy sources (RESs) to enhance energy sustainability. They can improve the reliability, resilience, and security of distribution systems. However, a distributed energy management framework is required for the optimal operation of distribution systems with multiple microgrids, given the limited communication between the distribution system operator (DSO) and the microgrid operators. Moreover, distribution systems are unbalanced in nature due to the unbalanced connected loads. Thus, modeling the unbalanced power flow in distributed energy management is essential to ensuring the feasibility of operational decisions. This paper proposes a distributed algorithm based on the alternating direction method of multipliers (ADMM) for optimal operation of distribution systems with multi-microgrids, accounting for uncertainty in demand, RESs, and MG operation modes, as well as unbalanced power flow. A modified IEEE 34-bus distribution system with six microgrids is used to validate the effectiveness of the proposed method. The proposed distributed energy management framework can achieve high solution accuracy with limited information shared among operators, as demonstrated in the case study, providing results comparable to those of the centralized energy management approach, with an insignificant 0.24% error in total operating cost. Moreover, numerical results show that compared with the distribution system and microgrids with forecasted loads and PV outputs under normal operation, the proposed stochastic model yields a 0.56% higher total expected operating cost due to uncertainty in load and PV power outputs. When probabilistic MG islanding operation is considered, the total expected operating cost of the distribution system decreases by 1.03% compared with the stochastic solution under normal operation due to the microgrids’ disconnection from the distribution system during islanding in a few scenarios, hence relieving the distribution system of excessive load. Full article

The transition to decentralized, clean energy systems is essential for sustainable development, particularly in rural South African communities where grid extension costs can exceed R300,000 per km. This paper presents a comprehensive analysis of Artificial Intelligence (AI) integration into hybrid solar-battery systems to address challenges of intermittency, load variability, and unreliable demand. We propose a model incorporating Long Short-Term Memory (LSTM) networks for energy forecasting and Reinforcement Learning (RL) for real-time optimization. Mathematical formulations for photovoltaic (PV) generation, battery state-of-charge dynamics, and a multi-objective cost function minimizing Levelized Cost of Energy (LCOE), carbon emissions, and reliability loss are derived with appropriate citations. A fairness metric is introduced as an operational constraint to mitigate algorithmic bias in energy allocation. Simulation results, calibrated with South African data, demonstrate a 20% improvement in forecasting accuracy (RMSE), a 30% reduction in diesel generator use, and a decrease in LCOE from R7.80 to R5.50/kWh. Furthermore, our fairness-constrained optimization reduced the Gini coefficient for load shedding from 0.38 to 0.19, ensuring more equitable access across households. This study concludes that AI-driven microgrids are technically viable, environmentally beneficial, and ethically sound for advancing equitable rural electrification in South Africa. Full article

Smart microgrids depend on continuous communication between controllers, sensors, and actuators over industrial protocols like Modbus TCP, message queuing telemetry transport (MQTT), and distributed network protocol 3 (DNP3), which were designed without built-in security mechanisms. The gateway that aggregates this traffic represents a single point of failure and is vulnerable to distributed denial-of-service (DDoS) attacks. Most existing detection methods require labeled attack data for training, a condition rarely met in operational technology (OT) environments. This paper presents an unsupervised convolutional neural network–long short-term memory (CNN-LSTM) model trained exclusively on normal microgrid gateway traffic to predict the next traffic window; anomalies are flagged when the prediction error exceeds a threshold derived from the training distribution. A dual-branch architecture processes metric time-series through LSTM layers and flow aggregate features through CNN layers, fusing both representations for prediction. The model is evaluated against three protocol-specific DDoS attack scenarios—Modbus supervisory control and data acquisition (SCADA) flooding, MQTT publish storm, and DNP3 response flooding—none of which are seen during training. Compared against an isolation forest baseline and an autoencoder baseline under identical unsupervised conditions, the CNN-LSTM achieves higher precision and recall on all attack types. The framework is deployed within a web-based monitoring platform that supports real-time detection and anomaly logging. Full article

This paper presents a practical and communication-independent energy management system (EMS) for a DC microgrid supply within the firezone of a cruise ship. The proposed approach prioritizes operational reliability and fault tolerance under emergency conditions, where communication availability and control complexity should be minimized. The proposed DC microgrid integrates photovoltaic systems (PVs), fuel cell systems (FCs), and lithium-iron-phosphate (LFP) battery energy storage systems (BESSs), coordinated through a rule-based EMS combined with droop-controlled converters. The electrical topology considered in this study is a collaborative development of the project consortium of the publicly funded project Sustainable DC Systems (SuSy), featuring a novel configuration with two independent horizontal busbars for the Cabin Area Distribution (CAD) and Technical Area Distribution (TAD). The EMS can manage two operational scenarios: (i) regular operation, with two decentralized droop controls where power generation is distributed among all generators based on their respective capacities, and a power curtailment strategy is applied to prevent overcharging of BESSs; and (ii) irregular operation, where a fault on one of the vertical busbars triggers the use of reserved battery storage capacity on both sides of the ship and activates load-shedding to ensure continued operation of critical loads and sustain grid functionality. The effectiveness of the proposed architecture is validated through detailed MATLAB/Simulink simulations. Under regular conditions, the EMS achieves stable voltage regulation, balanced power sharing, and efficient energy curtailment. During fault conditions, the battery storage on both sides successfully supports the critical loads. The fuel cells are operated in power-controlled mode effectively up to their full rated 6$\mathrm{k}$$\mathrm{W}$ capacity while the DC bus voltage stabilization is ensured by the battery energy storage systems. These results validate the proposed EMS as a robust and low-complexity solution for maritime DC microgrids, offering stable voltage regulation, effective load prioritization, and resilient operation of critical loads. Full article

AC microgrids with high penetration of inverter-based distributed energy resources (IBDERs) introduce major protection challenges due to reduced fault current levels, bidirectional power flows, and control-dependent fault behavior. Under these conditions, short-circuit current calculation and relay protection coordination become tightly coupled, since inaccurate fault modeling directly degrades relay sensitivity and selectivity. This review presents a protection-oriented assessment of state-of-the-art short-circuit calculation and relay protection strategies for AC microgrids. The analysis shows that conventional IEC-based fault models and static overcurrent protection schemes are insufficient for inverter-dominated networks. Generalized Δ-circuit–based modeling framework is identified as the most suitable foundation for microgrid fault analysis, as they enable inverter-aware phasor-domain representation and support both grid-connected and islanded operation. In addition, adaptive relay coordination approaches that incorporate time-varying IBDER participation and fault ride-through behavior demonstrate improved coordination robustness compared to conventional fixed settings, although their practical deployment remains constrained by network topology and communication requirements. Simulation results obtained on a representative microgrid case study confirm that the combined application of protection-oriented short-circuit modeling and adaptive relay coordination significantly improves fault detection reliability and coordination performance. The findings highlight the necessity of jointly addressing fault modeling and protection design to ensure reliable operation of inverter-dominated AC microgrids. Full article

A microgrid is a localized distribution network composed of electricity users who have access to local renewable and other energy sources. While the utility grid plays a critical role in the nation’s economy, security, and the well-being of its residents, connecting microgrids to the wider network via utility substations can introduce significant cybersecurity risks. Unlike most existing studies that rely on simulation, this research designs and implements a physical microgrid testbed to examine cybersecurity vulnerabilities in microgrid systems. We examine the impact of various cyberattacks—including denial of service (DoS) and communication hijacking—on microgrid operations, with a particular focus on system stability and communication networks. The findings reveal critical weaknesses within the existing communication infrastructure, providing valuable insights for designing more resilient and secure microgrids. This work offers a practical framework for addressing cybersecurity challenges in real-world industrial utility networks. Full article

Intrusion detection system (IDS) research relies on accurately labeled network traffic datasets; however, label quality in IDS datasets is seldom audited prior to modeling. Many publicly available IDS datasets assign ground-truth labels based on capture filenames or temporal session windows rather than per-flow inspection, a practice referred to as file-level labeling. This study identifies and corrects a systematic mislabeling instance in SDN-MG25, a CICFlowMeter-based dataset for software-defined networking (SDN)-enabled microgrid intrusion detection. IP composition analysis, which cross-references each attack-labeled flow with the documented attacker IP address, reveals that the BackgroundAttackTraffic (BAT) class, comprising 3167 flows (79.5% of all attack labels), contains no attacker-originated traffic. All BAT flows involve legitimate microgrid hosts communicating with external services during the attack capture window. Correcting this labeling error increases binary detection F1 from 0.578 to $0.956\pm 0.005$, an improvement of $+0.378$ that is 4.2 times greater than the best single modeling improvement (threshold tuning, $+0.090$). Furthermore, Confident Learning, a state-of-the-art automated label-noise detector, recovers only 8.4% of mislabeled BAT flows (recall $=0.084$, precision $=0.247$), indicating that domain-knowledge audits are essential for detecting systematic, class-level mislabeling that statistical methods cannot identify. The end-to-end pipeline Macro F1 improves from 0.749 to 0.862 after label correction. IP composition analysis is proposed as a mandatory prerequisite for IDS dataset evaluation, and a reproducible two-stage pipeline with feature-tier ablation for session confound diagnosis is provided. Full article

The growing integration of solar, wind and battery energy storage (BES) of the microgrids (MGs) has increased the necessity of real-time energy management, especially in the multi-microgrid (multi-MG) setting, where the generation and the load change stochastically. This paper presents a Java Agent DEvelopment (JADE)-based Multi-Agent System (MAS) for real-time energy management of a low-voltage hybrid multi-MG system incorporating solar photovoltaic (PV), wind generation, and battery energy storage (BES). The proposed framework’s novelty lies in its physical campus-scale hardware deployment—validated across four operating scenarios (single MG off-grid, single MG on-grid, dual MG off-grid, and dual MG on-grid)—combined with autonomous inter-MG power sharing, which distinguishes it from existing simulation-only MAS-based microgrid studies. The suggested framework facilitates decentralized communication between interconnected MGs and the utility AC grid to facilitate the proper management of power flow, its exchange, and the reliability of the system. The intelligent agents are used to coordinate solar, wind, BES, and load changes in order to adjust to changing demand conditions. The system is physically implemented on a campus rooftop with two 1 kW solar PV arrays and two 1.5 kW wind turbine generators, each paired with a 24 V, 150 Ah battery bank, operating on a 24 V DC bus. Results across 24 h real operational profiles demonstrate effective power balance maintenance, renewable energy maximization, and constraint-compliant battery operation (SOC is bounded within 20–90%). A direct comparison with a conventional centralized JavaScript-based EMS confirms equivalent dispatch accuracy while demonstrating superior scalability, fault tolerance, and modularity of the proposed JADE MAS architecture. Full article

Because smart microgrids can flexibly integrate distributed energy resources and support grid-connected and islanded operation modes, they enhance power supply reliability and promote the efficient utilization of renewable energy. However, the open communication environment and physically exposed infrastructure introduce critical security challenges, including risks of physical hijacking and data leakage. Many existing authentication protocols either fail to address these threats or rely on heavyweight cryptographic operations such as bilinear pairings and modular exponentiation, resulting in high computational and communicational overhead. To address these issues, a lightweight authentication and key agreement (AKA) protocol for smart microgrids is proposed. The protocol symmetrically integrates Physical Unclonable Functions (PUFs) into the smart meter (SM) and smart microgrid control center (SMC) to protect stored secret information against capture attacks. Meanwhile, the SM and SMC register with the data center (DC) in a symmetric manner. During the AKA phase, the DC only assists in authenticating the identities of the SM and SMC online in a symmetric way, without participating in session key computation, thereby reducing the trust burden and computational load on the smart meters and control center. Formal security proof and informal security analysis demonstrate that the proposed protocol can resist known attacks such as physical hijacking and data leakage. Compared with existing smart microgrid authentication protocols, the proposed protocol has performance advantages and the lowest computational cost, confirming its suitability for resource-constrained microgrid environments. Full article