Search Results (97)

The increasing penetration of renewable energy has led to a decrease in system inertia, challenging grid stability and frequency regulation. This paper presents a dynamic estimation framework for load-side virtual inertia, supported with high-power-density electrical double-layer supercapacitors (EDLCs). By leveraging the fast response and high power density of EDLCs, the proposed method enables the real-time emulation of demand-side inertial behavior, enhancing frequency support capabilities. A hybrid estimation algorithm has been developed that combines demand forecasting and adaptive filtering to track virtual inertia parameters under varying load conditions. Simulation results, based on a 150 kVA distributed system with 27% renewable penetration and 33% demand variability, demonstrate the effectiveness of the approach in improving transient stability and mitigating frequency deviations within ±0.1 Hz. The integration of ESS-based support offers a scalable and energy-efficient solution for future smart grids, ensuring operational reliability under real-world variability. 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

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

Traditionally, the dynamics of power systems have been governed by synchronous generators and their associated rotating masses. However, with the increasing penetration of renewable generation and power electronic interfaces, the inertia contributed by rotating machines has been gradually displaced. This makes it imperative to study alternative elements capable of mitigating the reduction in inertia in modern power systems. This article addresses the problem of optimal sizing and placement of Battery Energy Storage Systems to enhance frequency response in power grids through the application of optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Several inertia scenarios are analyzed, where the algorithms determine the optimal locations for Battery Energy Storage Systems units while minimizing the total installed Battery Energy Storage Systems capacity. As key contributions, this study models Battery Energy Storage Systems units, which emulate inertial responses based on the system’s Rate of Change of Frequency, and evaluates the impact of Battery Energy Storage Systems on frequency stability by analyzing parameters such as the frequency nadir, zenith, and steady-state frequency according to the installed Battery Energy Storage System’s size and location. A comparative analysis of the optimization scenarios shows that the Particle Swarm Optimization algorithm with 50% rotational inertia is the most efficient, requiring the lowest total installed power (277.11 MW). It is followed by the Particle Swarm Optimization algorithm with 100% rotational inertia (285.79 MW) and Genetic Algorithms with 50% rotational inertia (285.57 MW). In contrast, Genetic Algorithms with 25% rotational inertia demand the highest total installed Battery Energy Storage Systems power (307.44 MW), a result directly associated with a significant reduction in system inertia. Overall, an inverse relationship is observed between the available inertia level and the required Battery Energy Storage Systems capacity: the lower the inertia, the greater the power that the Battery Energy Storage Systems must supply to keep the system frequency within acceptable operational limits. Full article

The increasing integration of renewable energy sources is reshaping power systems from centralized, synchronous generator-based architectures to more inverter-dominated and decentralized architectures. This transition, however, results in a significant reduction in system inertia, posing challenges to frequency stability. To address this issue, various control strategies have been proposed to emulate the inertial response of traditional synchronous generators—commonly known as virtual inertia. This study reviews inverter-based virtual inertia and related control strategies that replicate or extend synchronous generator dynamics, covering five main approaches: droop control, synchronverters, virtual synchronous generators (VSGs), the swing equation-based approach, and data-driven grid-forming (GFM) methods. While all approaches enhance frequency nadir and RoCoF, they differ in complexity, robustness, and adaptability. Droop control offers simplicity but lacks true inertia support, whereas synchronverter and swing equation-based controls provide closer emulation of synchronous behavior for grid-forming or islanded systems. VSG offers a more practical grid-following solution, and data-driven GFM introduces adaptability through learning-based mechanisms. Overall, this study contributes to a comprehensive understanding of how these control strategies can be implemented through inverter control to maintain frequency stability in renewable-dominated power systems. Full article

In this study, the authors demonstrate the development and evaluation of an optimal frequency control controller for an interlinked two-area power system that incorporates Renewable Energy Sources (RESs). In decentralized power grids, the Load Frequency Control (LFC) system allows scheduled tie-line power as well as system frequency to be reimposed to their nominal values. Designing an advanced controller might enhance the functionality of the LFC mechanism. This article illustrates the possible impacts of converter capacitors using the new High-Voltage Direct Current (HVDC) tie-line model as well as the Inertia Emulation Technique (IET). This paper suggests a new adaptive control procedure for the expected LFC mechanism: an ANN-based (PI^λ + PI^λf) controller. The authors evaluate which control parameters are most effective using a modified version of the Quasi-Opposition-learning-based Reptile Search Algorithm (QORSA) method. Software called MATLAB/Simulink-2015 is used to create this arrangement. The use of established techniques for handling step as well as random load disturbances has enabled an evaluation of the suggested LFC architecture’s efficacy. An IET-based HVDC tie-line reduces overshoot by 100% in Areas 1 and 2 (Area 1 frequency deviation, i.e., ∆f₁, as well as Area 2 frequency deviation, i.e., ∆f₂). When considering SLD, the suggested controller outperforms the most widely used alternative settings. The IEEE-39 bus system has been changed by the addition of RESs. The IEEE-39 bus system is composed of three control areas. It is confirmed how the IEEE-39 bus system reacts to changes in frequency in Areas 1, 2, and 3. It is illustrated how to use the suggested controller in the modified IEEE-39 bus system, accompanied by real-time load variations. Recent research indicates that the suggested control method is better and more efficient due to its 100% decrease in overshoot in Areas 1 and 2 and quick response time. Full article

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article

The displacement of synchronous machines by inverter-based resources raises critical concerns regarding the stability of future low-inertia power systems. Grid-forming (GFM) inverters offer a pathway to address these challenges by autonomously establishing voltage and frequency while emulating inertia and damping. This paper investigates the feasibility of operating a transmission-scale network with 100% GFM penetration by fully replacing all synchronous generators in the IEEE 39-bus system with a heterogeneous mix of droop, virtual synchronous machine (VSM), and synchronverter controls. System stability is assessed under a severe fault-initiated separation, focusing on frequency and voltage metrics defined through center-of-inertia formulations and standard acceptance envelopes. A systematic parameter sweep of virtual inertia (H) and damping ($D_p$) reveals their distinct and complementary roles: inertia primarily shapes the Rate of Change in Frequency and excursion depth, while damping governs convergence speed and steady-state accuracy. All tested parameter combinations remain within established stability limitations, confirming the robust operability of a fully inverter-dominated grid. These findings demonstrate that properly tuned GFM inverters can enable secure and reliable operation of future power systems without reliance on synchronous machines. Full article

The growing penetration of renewable energy has reduced system inertia and damping, threatening grid stability. This paper proposes a novel control strategy that seamlessly integrates virtual synchronous generator (VSG) emulation with low-voltage ride-through (LVRT) capability for direct-drive permanent magnet synchronous generators (PMSGs). The unified control framework enables simultaneous inertia support during frequency disturbances and compliant reactive current injection during voltage sags—eliminating mode switching. Furthermore, the proposed strategy has been validated through both a single-machine model and actual wind farm topology. Results demonstrate that the strategy successfully achieves VSG control functionality while simultaneously meeting LVRT requirements. Full article

The widespread integration of renewable energy sources (RESs) into the grid through inertia-less power converters is reducing the overall system inertia leading to large frequency variations. To mitigate this issue, grid-forming (GFM) control strategies in bidirectional battery chargers have emerged as a promising solution, since the inertial response of synchronous generators (SGs) can be emulated by power converters. However, unlike SGs, which can withstand currents above their rated values, the output current of a power converter is limited to its nominal design value. Therefore, the estimation of the power delivered by the GFM power converter during frequency events, called Virtual Inertia (VI) support, is essential to prevent exceeding the rated current. This article analyzes the VI response of GFM power converters, classifying the dynamic behavior as underdamped, critically damped, or overdamped according to the selected inertia constant and damping coefficient, parameters of the GFM control strategy. Subsequently, the transient power response under step-shaped and ramp-shaped frequency deviations is quantified. The proposed analysis is validated using a 1.2 KW single-phase power converter. The simulation and experimental results confirm that the overdamped response under a ramp-shaped frequency event shows higher fidelity to the theorical model. Full article

This scoping review analyzes the role of inertia in converter-dominated microgrids, with an emphasis on hybrid AC/DC architectures. Following the PRISMA-ScR methodology, 54 studies published between 2015 and 2025 were identified, screened, and synthesized. The review addresses two key aspects, inertia estimation methods and control strategies for emulating inertia via power converters, emphasizing the role of the interlinking converter (ILC) as a bidirectional interface for inertia support between the AC and DC subsystems. This work addresses several limitations of prior reviews: their narrow scope, often overlooking advanced data-driven approaches such as machine learning; the lack of systematic classifications, hindering a comprehensive overview of existing methods; and the absence of practical guidance on selecting appropriate techniques for specific conditions. The findings show that conventional estimation methods are insufficient for low-inertia grids, necessitating adaptive and data-driven approaches. Virtual inertia emulation strategies—such as Virtual Synchronous Machines, Virtual Synchronous Generators, Synchronverters, and ILC-based controls—offer strong potential to enhance frequency stability but remain challenged by scalability, adaptability, and robustness. The review highlights critical research gaps and future directions to guide the development of resilient hybrid microgrid control strategies. Full article

High photovoltaic (PV) penetration challenges grid frequency stability due to reduced system inertia. Virtual Synchronous Generators (VSGs), particularly when paired with Battery Energy Storage Systems (BESSs), can mitigate this by emulating synchronous machine dynamics. This study focuses on improving frequency response during PV power reductions through the adaptive tuning of an extensive set of VSG parameters. A double-phase Supervisory Controller is developed: in the first phase, Particle Swarm Optimization (PSO) computes multiple sets of optimal VSG parameters for various PV curtailment and load demand change scenarios; in the second phase, the system determines the most appropriate parameters based on current operating conditions to minimize frequency deviations, using the first phase as a foundation for adaptive decision making. The proposed Supervisory Controller reduced the Integral of the Absolute Error (IAE) of 151.55% in the case of a 65% irradiance drop. At 55%, the IAE decreased from 0.4605 to 0.2227, and at 25% from 0.0791 to 0.0546. In the low-disturbance scenario at a 25% drop, the IAE was maintained below 0.06. Supervisory Controller performance led to a reduced settling time and improved frequency recovery. These results demonstrate that the Supervisory Controller improves frequency regulation in both mild and severe irradiance reduction events. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

The virtual synchronous generator (VSG) technique plays a crucial role in power systems with high penetration of power electronics, as it can provide virtual inertia and damping performance by emulating the swing characteristics of a synchronous generator (SG). However, the VSG faces challenges due to its inherent limitations, such as vulnerability to disturbances and instability in strong grid conditions. To address these issues, this article proposes an exchanged VSG (eVSG) control strategy. In this approach, the phase information (θ) is derived from reactive power (Q), while the voltage information (E) is derived from active power (P). Furthermore, a Magnitude-Phase Motion Equation (MPME) is introduced to analyze the eVSG system from a physical perspective. Additionally, this article is the first to illustrate the oscillation propagation effect between P and frequency (f) in both VSG and eVSG systems. Finally, the advantages of the eVSG strategy are comprehensively demonstrated through three aspects: (1) comparing the motion trajectory of f using the MPME model, (2) evaluating the oscillation propagation effect between VSG and eVSG systems, and (3) conducting simulations and experiments. Full article

The shift to renewable energy generation increases risks of frequency and voltage instability. This transition can cause significant voltage and frequency fluctuations during load changes, generation interruptions, and grid faults. One potential solution is the deployment of synchronous condensers to mitigate these issues; however, this approach may also increase operational and maintenance costs. To address this limitation, this paper proposes a method called the virtual synchronous condenser (VSCon) that enables renewable energy systems such as PV-solar energy systems or wind farms to emulate the behavior of synchronous condensers. Unlike traditional VSGs with simplified models, VSCon uses the mathematical equivalent circuit of a real synchronous condenser. This enables sub-transient and inertial behavior. Voltage support improves by adjusting sub-transient reactance, and frequency support enhances by tuning inertia and damping coefficients, thereby enhancing the local voltage and frequency stability. The proposed approach has been validated through case studies, demonstrating both its effectiveness and practicality. Full article