Search Results (663)

This paper proposes an emergency power supply restoration strategy for a distribution network that considers the operational risk of an islanded microgrid in response to the issues of voltage exceeding limits and power imbalance faced during their operation. Firstly, a distribution network emergency power supply restoration model supported by a generalized dynamic islanded microgrid is constructed. By equating the alternate tie line with a virtual distributed generator (DG), the integrated power supply restoration problem of distribution network is transformed into a generalized island power distribution network division problem based on DGs. Then, the risk of islanded microgrid operation is considered and restricted by chance constraints. Finally, simulation results based on the improved IEEE-33 node distribution network show that, compared to the generalized island partitioning strategy which ignores operational risks, the proposed strategy increases the power supply restoration rate from 83.4% to 97.8% while successfully ensuring the stability of all islanded microgrids under the specified confidence level for operational risk. Full article

As renewable energy integration accelerates, the displacement of synchronous generators by inverter-based resources (IBRs) necessitates advanced grid-forming (GFM) control strategies to maintain system stability. While techniques such as Droop control, Virtual Synchronous Generator (VSG), and Dispatchable Virtual Oscillator Control (dVOC) are well-established, their comparative performance under coordinated cyber-physical stress remains underexplored. This paper presents a comprehensive Controller Hardware-in-the-Loop (CHIL) assessment of these three GFM strategies within a networked microgrid environment. Utilizing a co-simulation framework that integrates an OPAL-RT real-time simulator with the EXata CPS network emulator, we evaluate the dynamic resilience of each controller under islanded, parallel, and fault-induced reconfiguration scenarios. Experimental results demonstrate that the VSG strategy offers superior transient performance, characterized by faster settling times and enhanced fault-ride-through capabilities compared to the Droop and dVOC strategies. Furthermore, recognizing the vulnerability of connected microgrids to cyber threats, this study investigates the impact of False Data Injection (FDI) attacks on the control layer. To address this, a model-reference resilience layer is proposed and validated on a TI C2000 DSP. The results confirm that this protection mechanism effectively detects and mitigates attacks on control references and feedback measurements, ensuring stable operation despite cyber-physical disturbances. Full article

The growing incorporation of distributed energy resources (DER) in power distribution grids, although pivotal to the energy transition, increases operational variability and amplifies the exposure to disturbances that can compromise resilience and the continuity of service during contingencies. Addressing these challenges requires both a shift toward flexible distribution architectures and the adoption of advanced power electronics interfacing systems. In this setting, this paper proposes a resilience-oriented strategy for medium-voltage (MV) distribution systems and clustered hybrid AC/DC microgrids interfaced through solid-state transformers (SSTs). When a fault occurs along an MV feeder segment, the affected microgrids naturally transition to islanded operation. However, once their local generation and storage become insufficient to sustain autonomous operation, the proposed framework reconfigures the power routing within the cluster by activating an emergency low-voltage DC (LVDC) power path that bypasses the faulted MV section. This mechanism enables controlled power sharing between microgrids during prolonged MV outages, ensuring the supply of priority loads without oversizing SSTs or reinforcing existing infrastructure. Experimental validation on a reduced-scale SST prototype demonstrates stable grid-forming and grid-following operation. The reliability of the proposed scheme is supported by both steady-state and transient experimental results, confirming accurate voltage regulation, balanced sinusoidal waveforms, and low current tracking errors. All tests were conducted at a switching frequency of 50 kHz, highlighting the robustness of the proposed architecture under dynamic operation. Full article

Voltage control is problematic in an islanded microgrid, as small and mismatched feeder impedances lead to inaccurate reactive power sharing among grid-forming inverters and potential instability under conventional droop control. Existing adaptive virtual impedance solutions often depend on communication links, creating a system vulnerability. This study introduces an autonomous control strategy to enhance reactive power sharing without requiring communication. The proposed method utilizes an artificial neural network (ANN) consisting of an offline and online phase to determine the optimal virtual impedance locally at each grid-forming inverter. During an offline phase, a physics-aware recursive least-squares (RLS) algorithm is used to generate a training data set. In online operation, the trained ANN is a lightweight model that uses only local measurements to calculate the required voltage compensation. This ANN-based virtual impedance is a practical and adaptable solution for autonomous voltage and reactive power control. By eliminating communication dependency, this strategy enhances microgrid stability, reliability, and scalability, offering a significant improvement over communication-based methods in terms of cybersecurity. MATLAB/SIMULINK simulations validate the approach, showing that the controller achieves precise reactive power sharing under varying loads and eliminates steady-state errors. Significantly, it maintains robust performance during communication failures and seamlessly adapts to the grid changes. Full article

High penetration and output volatility of island wind and photovoltaics (PV) pose challenges to energy consumption and supply–demand balance, and cost-effective energy storage configuration. A coupled dispatch model for a wind–PV–storage system is proposed, which treats multiple canal units as virtual ‘loads’ that switch between generation and pumping under constraints of power balance and available water head model. Considering the variable reservoir–irrigation feature, a multi-objective model framework is developed to minimize both economic cost and storage capacity required. An augmented Lagrangian–Nash product enhanced NSGA-II (AL-NP-NSGA-II) algorithm enforces constraints of irrigation shortfall and overflow via an augmented Lagrangian term and allocates fair benefits across canal units through a Nash product reward. Moreover, updates of Lagrange multipliers and reward weights maintain power balance and accelerate convergence. Finally, a case simulation (3.7 MW wind, 7.1 MW PV, and 24 h rural load) is performed, where 440.98 kWh storage eliminates shortfall/overflow and yields 1.5172 × 10⁴ CNY. Monte Carlo uncertainty analysis (±10% perturbations in load, wind, and PV) shows that increasing storage to 680 kWh can stabilize reliability above 98% and raise economic benefit to 1.5195 × 10⁴ CNY. The dispatch framework delivers coordination of irrigation and power balance in island microgrids, providing a systematic configuration solution. Full article

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized by vulnerable grid infrastructure. A multi-objective optimization framework is developed to jointly minimize operational costs, load-not-served, and environmental impacts under both normal and abnormal operating conditions. The proposed strategy employs the Multi-objective JAYA (MJAYA) algorithm to coordinate photovoltaic generation, diesel generators, battery energy storage systems, and inter-microgrid power exchanges within a 20 kV distribution network. Using real load, generation, and electricity price data, we evaluate the NMG’s performance under five representative fault scenarios that emulate ND-induced outages, including grid disconnection and loss of inter-microgrid links. Results show that the interconnected NMG structure significantly enhances system resilience, reducing load-not-served from 366.3 kWh in fully isolated operation to only 31.7 kWh when interconnections remain intact. These findings highlight the critical role of cooperative microgrid networks in strengthening community-level energy resilience in vulnerable regions. The proposed framework offers a practical decision-support tool for planners and governments seeking to enhance energy security and advance sustainable development in disaster-affected areas. Full article

The increasing demand for energy combined with depleting conventional energy sources has led to the evolution of distributed generation using renewable energy sources. Integrating these distributed generations with the existing grid is a complicated task, as it risks the stability and synchronisation of the system. Microgrids (MG) have evolved as a concrete solution for integrating these DGs into the existing system with the ability to operate in either grid-connected or islanded modes, thereby improving reliability and increasing grid functionality. However, owing to the intermittent nature of renewable energy sources, managing the energy balance and its coordination with the grid is a strenuous task. The hierarchical control structure paves the way for managing the dynamic performance of MGs, including economic aspects. However, this structure lacks the ability to provide effective solutions because of the increased complexity and system dynamics. The incorporation of artificial intelligence techniques for the control of MG has been gaining attention for the past decade to enhance its functionality and operation. Therefore, this paper presents a critical review of various artificial intelligence (AI) techniques that have been implemented for the hierarchical control of MGs and their significance, along with the basic control strategy. Full article

The progressive replacement of synchronous machines by inverter-based resources (IBRs) reduces system inertia and short-circuit strength, making power systems more vulnerable to frequency and voltage instabilities. Grid-forming (GFM) inverters can mitigate these issues by establishing voltage and frequency references, emulating inertia and enabling autonomous operation during islanding, while grid-following (GFL) inverters mainly contribute to reactive power support. This paper evaluates the capability of GFM inverters to provide grid support under both grid-connected and islanded conditions at the distribution level. Electromagnetic transient (EMT) simulations in MATLAB/Simulink R2022b were performed on a 20 kV radial microgrid comprising GFM and GFL inverters and aggregated load. Small disturbances, including phase-angle jumps and voltage steps at the point of common coupling, were introduced while varying the GFM share and virtual inertia constants. Also, local variables were assessed during islanded operation and separation process. Results indicate that maintaining a GFM share above approximately 30–40% with inertia constants exceeding 2 s significantly enhances frequency stability, supports successful transitions to islanded operation, and improves overall resilience. The study highlights the complementary roles of GFM and GFL in enabling the stable and resilient operation of converter-dominated distribution systems. Full article

The increasing integration of inverter-based resources in smart grid systems has deepened the necessity to understand the difference between grid-following and grid-forming inverters’ operational performance, particularly under abnormal conditions such as unintentional islanding events. This work provides a comparative assessment, showing that while grid-following inverters perform well under strong grids, their stability degrades under weak grids due to their dependence on the grid reference voltage. On the other hand, grid-forming inverters improve the system stability under weak grids, as they operate as an independent voltage source. However, the widespread misconception in academia and industry that grid-forming inverters are always good and grid-following inverters are generally bad is challenged by this work’s results. Despite the stability advantages of grid-forming inverters, they significantly increase the size of non-detected zones and extend the detection time of unintentional islanding events, with various cases failing to meet standards, while grid-following inverters offer quicker and more expectable responses. A Random Forest-based islanding detection scheme is proposed to address the protection difficulties allied with both inverter types. The findings prove that this model can reduce the size of the non-detected zone and the detection time, emphasizing the necessity of intelligent protection schemes in future microgrid applications and the significance of performance-based inverter selection. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

Remote islands face persistent challenges in achieving secure, sustainable and affordable energy supply due to their geographic isolation, fragile ecosystems and dependence on imported fossil fuels. Hybrid renewable energy systems (HRES)—typically combining photovoltaics (PV), wind turbines and battery energy storage systems (BESS)—have emerged as the dominant off-grid solution, demonstrating their potential to reduce fossil fuel dependence and greenhouse gas emissions. Yet, empirical case studies from Zanzibar, Thailand, Malaysia, the Galápagos, the Azores and Greece confirm that current systems remain transitional, relying on oversized storage and fossil backup during low-resource periods. Comparative analysis highlights both technical advances and persistent limitations, including seasonal variability, socio-economic barriers and governance gaps. Future directions for PV—wind-based (non-dispatchable) island microgrids point toward long-term hydrogen storage, artificial intelligence (AI)-driven predictive energy management and sector coupling—alongside participatory planning frameworks that enhance social acceptance and community ownership. By synthesizing technical, economic and social perspectives, this study provides a roadmap for advancing resilient, autonomous and socially embedded hybrid off-grid systems for remote islands. Full article

This paper presents a comprehensive framework for real-time energy management in microgrids integrating distributed renewable energy sources and demand response (DR) programs. To address the inherent uncertainties in key operational variables—such as load demand, wind speed, solar irradiance, and electricity market prices—this study employs a probabilistic modeling approach. A two-stage stochastic optimization method, combining mixed-integer linear programming and optimal power flow (OPF), is developed to minimize operational costs while ensuring efficient system operation. Real-time dynamic pricing mechanisms are incorporated to incentivize consumer load shifting and promote energy-efficient consumption patterns. Three microgrid scenarios are analyzed using one year of real historical data: (i) a grid-connected microgrid without DR, (ii) a grid-connected microgrid with 10% and 20% DR-based load shifting, and (iii) an islanded microgrid operating under incentive-based DR contracts. Results demonstrate that incorporating DR strategies significantly reduces both operating costs and reliance on grid imports, especially during peak demand periods. The islanded scenario, while autonomous, incurs higher costs and highlights the challenges of self-sufficiency under uncertainty. Overall, the proposed model illustrates how the integration of real-time pricing with stochastic optimization enhances the flexibility, resilience, and cost-effectiveness of smart microgrid operations, offering actionable insights for the development of future grid-interactive energy systems. Full article

A proactive power compensation strategy applicable to achieving transient ride-through of ship microgrid (SM) under pulsed load is presented in this paper. The essence of this strategy can be summarized as the generator enters a transient process when a large portion of the pulsed load is connected to the islanded microgrid. Next, the pulsed load power is calculated and predicted over a 20 ms time scale based on the changes in stator current, stator voltage, excitation current and excitation voltage during the process. As a result, the predicted power is used as the control desired value of the compensation device to ensure that the microgrid recovers the power balance and achieves transient ride-through. Finally, the proposed control strategy not only replaces the one machine infinite bus (OMIB) with the transient model of the SG but also utilizes the energy storage device to actively guide the generator to output the differential power in the microgrid. The power response time of the compensation system is in the range of 6–20 ms, which is able to realize the transient ride-through of the SG within one cycle. Full article

This work proposes an intelligent strategy for the coordinated management of active and reactive power in Battery Energy Storage Systems (BESSs) within AC microgrids operating under both grid-connected (GCM) and islanded (IM) modes to minimize daily operational costs. The problem is formulated as a mixed-variable optimization model that explicitly leverages the control capabilities of BESS power converters. To solve it, a Parallel Particle Swarm Optimization (PPSO) algorithm is employed, coupled with a Successive Approximation (SA) power flow solver. The proposed approach was benchmarked against parallel implementations of the Crow Search Algorithm (PCSA) and the JAYA algorithm (PJAYA), both in parallel, using a realistic 33-node AC microgrid test system based on real demand and photovoltaic generation profiles from Medellín, Colombia. The strategy was evaluated under both deterministic conditions (average daily profiles) and stochastic scenarios (100 daily profiles with uncertainty). The proposed framework is evaluated on a 33-bus AC microgrid that operates in both grid-connected and islanded modes, with a battery energy storage system dispatched at both active and reactive power levels subject to network, state-of-charge, and power-rating constraints. Three population-based optimization algorithms are used to coordinate BESS schedules, and their performance is compared based on daily operating cost, BESS cycling, and voltage profile quality. Quantitatively, the PPSO strategy achieved cost reductions of 2.39% in GCM and 1.62% in IM under deterministic conditions, with a standard deviation of only 0.0200% in GCM and 0.2962% in IM. In stochastic scenarios with 100 uncertainty profiles, PPSO maintained its robustness, reaching average reductions of 2.77% in GCM and 1.53% in IM. PPSO exhibited consistent robustness and efficient performance, reaching the highest average cost reductions with low variability and short execution times in both operating modes. These findings indicate that the method is well-suited for real-time implementation and contributes to improving economic outcomes and operational reliability in grid-connected and islanded microgrid configurations. The case study results show that the different strategies yield distinct trade-offs between economic performance and computational effort, while all solutions satisfy the technical limits of the microgrid. Full article

Under the environment of globalized energy restructuring and achieving the goal of “peak carbon and carbon neutrality”, this paper proposes an optimal scheduling method based on the improved cosine algorithm for island microgrids, which relies on diesel generators, resulting in high carbon emissions and high operating costs. First, an optimal scheduling model for island microgrids is established with the objective of minimizing the system operating cost, which comprehensively considers the carbon emission penalty, power balance constraints, equipment operation constraints, and the volatility of renewable energy sources. Secondly, the traditional sine–cosine algorithm is improved by introducing an adaptive adjustment factor, elite retention strategy and chaotic mapping initialization population in order to solve its shortcomings of falling into local optimums and insufficient convergence accuracy when solving high-dimensional complex problems. Finally, the effectiveness of the proposed method is verified by simulation experiments. The results show that the method in this paper reduces the total system cost to 2994.2 yuan (6.5% lower than the baseline scenario), reduces the carbon emission to 968.8 kg (55.1% lower), and improves the wind and light consumption rate to 98.84%, which is an obvious advantage and provides a theoretical basis and technical support for the realization of the low-carbon and economic operation of island microgrids. Full article