Search Results (162)

As the global energy transition accelerates, distribution systems are integrating increasing shares of inverter-interfaced renewables, making reliable voltage support a key operational requirement. In grid-connected microgrids, especially weak radial feeders in rural and remote areas, voltage-reactive power (Volt/Var) control must coordinate multiple inverters under uncertainty from photovoltaic (PV) intermittency, load volatility, and point-of-common-coupling (PCC) disturbances. Existing droop, model-based optimization, and non-graph reinforcement learning (RL) approaches often rely on fixed rules or do not explicitly exploit electrical topology, which limits adaptive coordination. To address this gap, we propose a topology-aware graph reinforcement learning framework for voltage-reactive power control in grid-connected microgrids under uncertainty. The method encodes node states with a graph convolutional network (GCN) and learns coordinated PV/storage reactive-power actions via proximal policy optimization (PPO) with a multi-objective reward balancing voltage quality, control effort, and action smoothness. In a controlled comparison against a multilayer perceptron (MLP)-PPO baseline with identical action space, reward, and PPO objective, our method reduces voltage violation rate (VVR) from 0.0316 ± 0.0086 to 0.0048 ± 0.0019. Additional validation on a modified IEEE 33-bus feeder further reduces VVR from 0.00726 for MLP-PPO and 0.02999 for Droop control to 0.00095, supporting the effectiveness of topology-aware state representation on a larger radial benchmark feeder. Full article

With the integration of high-penetration distributed renewable energy sources and grid-forming inverters, AC microgrids face significant challenges in maintaining autonomous voltage and frequency stability. While traditional droop control can achieve autonomous power allocation, it introduces inherent steady-state deviations when load change. To address this, this paper proposes a distributed secondary control strategy for AC microgrids based on a consensus algorithm, aiming to achieve high-precision coordinated correction of voltage and frequency and improve power quality. In the proposed strategy, each grid-forming inverter autonomously generates dynamic secondary compensation signals based solely on local measurements and limited information exchange with neighboring nodes, eliminating the need for a central controller and enhancing robustness, scalability, and fault tolerance. Stability is proven via Lyapunov function construction. Simulation results show that the strategy effectively eliminates steady-state errors, with frequency deviations within ±0.01 Hz and voltage deviations below 0.5% of the rated value. Rapid and precise regulation is achieved under various load disturbances and network conditions, validating its effectiveness and application potential. Full article

The increasing penetration of renewable energy sources in inverter-dominated microgrids introduces significant challenges for maintaining voltage and frequency stability under weak-grid and dynamically varying operating conditions. Conventional inverter control strategies, including droop control and virtual synchronous machine (VSM) methods, often exhibit limited adaptability and degraded transient performance under renewable intermittency and uncertain load variations. This paper proposes a physics-informed neural-network (PINN)-based supervisory framework for real-time grid-forming inverter control. The proposed approach embeds swing-equation dynamics, Kirchhoff-based electrical constraints, and stability-aware objectives directly into the neural-network optimization process to improve physical consistency, robustness, and operational reliability. The controller is trained offline and deployed for low-latency online inference on an NVIDIA Jetson AGX Xavier embedded platform. Simulation and hardware-in-the-loop validation results demonstrate improved transient stability, reduced frequency deviation, enhanced voltage regulation, and superior robustness compared with conventional droop, VSM, and purely data-driven neural-network controllers. The proposed framework achieved an average inference latency of approximately 0.7 ms while maintaining stable operation under renewable intermittency, load disturbances, and weak-grid conditions. The results demonstrate the potential of physics-informed machine learning for supervisory real-time control of inverter-dominated microgrids and intelligent renewable energy systems. Full article

This paper presents a capability-aware frequency control strategy for microgrids comprising a ramp-rate-limited synchronous generator (SG) and a bounded inverter-based resource (IBR). In contrast to conventional droop and virtual inertia methods, the proposed design activates IBR support according to whether the required power-rate exceeds the ramp-rate capability of synchronous generation. A smooth activation mechanism detects when the required power-ramp demand exceeds the SG ramp-rate limit. The IBR is then engaged to supply the excess ramping requirement while providing additional damping through frequency-deviation feedback. A two-timescale model is formulated, where the IBR power-tracking dynamics evolve on a fast boundary-layer timescale. In contrast, the SG regulation loop evolves on a slow electromechanical timescale. Using singular perturbation theory combined with Lyapunov and input-to-state stability (ISS) analysis, local practical stability of the closed-loop system is established for sufficiently fast IBR dynamics. The proposed framework yields a physically interpretable coordination mechanism that exploits the fast response of IBR without introducing artificial inertia or frequency-domain disturbance splitting. Full article

The rapid integration of photovoltaic (PV) generation into active distribution networks (ADNs) creates a fundamental tension between maintaining tight voltage regulation and accommodating high distributed energy resource (DER) penetration levels. Conventional voltage control methods such as the droop control operate locally without coordination, while centralized optimal power flow requires full network observability and reliable real-time communication. Multi-agent deep reinforcement learning (MADRL) methods provide adaptive coordination but suffer from long training times and algorithmic complexity that prevent direct deployment on embedded inverter hardware. This paper proposes the Optimal Historical Selection and Forecasting (OHSF) scheme: a physics-informed long short-term memory (LSTM) network combined with an online sensitivity-based correction loop for medium-voltage ADNs. A composite loss function incorporating data-driven regression, an inter-PV voltage sensitivity penalty, and an inverter capability constraint produces reactive power setpoints that are inherently aware of physical limits, while the correction loop refines the predictions using real-time AC power flow feedback. The OHSF scheme supports a centralized full-network mode and a decentralized fallback mode in which the trained weights run locally on each inverter. Simulations under worst-case PV placement and network reconfiguration on the modified IEEE 33-bus and 69-bus test systems achieve an average voltage deviation across all PV buses of $0.701\%$ and $0.601\%$ at $172\%$ DER penetration on the 33-bus system, and $0.804\%$ and $0.806\%$ at $242\%$ DER penetration on the 69-bus system, while training 32 to 49 times faster than state-of-the-art MADRL methods. Full article

The increased penetration of renewable energy resources with low inertia poses a risk to the frequency and voltage stability of modern power systems. Therefore, it is important to investigate grid-support functions from inverter-interfaced technologies. While conventional software simulations provide valuable insights into system behavior, they fail to capture physical interactions and hardware dynamics. This paper presents a power-hardware-in-the-loop (PHIL) platform used to evaluate inverter grid-support functions in a physical microgrid supplied by two synchronous generators connected to a load bus. The inverter is implemented in Simulink, executed on a real-time simulator and interfaced to the physical load bus through a power amplifier. The inverter controller uses droop control to inject power in response to frequency and voltage deviations. Experimental results demonstrate that the PHIL platform captures dynamic interactions between virtual and physical components. The paper concludes with practical guidelines and key considerations for the reliable application of PHIL in validating inverter control strategies in small-scale microgrids. Full article

The proliferation of vehicle-to-grid (V2G) charging stations in distribution networks introduces both voltage regulation challenges and untapped reactive power resources. This paper proposes a reactive–active power coordination control strategy for grid-forming (GFM) V2G charging stations to achieve voltage regulation in radial distribution networks. First, a voltage–reactive power sensitivity matrix is analytically derived from the linearized DistFlow equations, quantifying the voltage influence of each V2G station. The sensitivity matrix is computed from the network topology and line parameters, and its accuracy under varying operating conditions is validated against nonlinear power flow solutions. Second, a dynamic residual reactive capacity model exploits the inverter apparent power margin without curtailing active power, and a sensitivity-weighted proportional allocation distributes the reactive power references among stations. Third, a two-timescale hierarchical control architecture is designed: the upper layer solves a quadratic programming problem every 60 s to determine optimal set-points, while the lower layer employs GFM droop control with a 1 ms response to track references and provide inertia support. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method reduces the maximum voltage deviation by 62% compared with active-power-only control, while maintaining a frequency nadir of 49.73 Hz, confirming negligible frequency performance degradation. Extended simulations covering a 24 h period with stochastic EV arrival and departure patterns as well as varying load conditions further confirm the robustness of the proposed strategy. Full article

Virtual Synchronous Generator (VSG) has become a prominent candidate to control grid-tied power electronic inverters for its ability to provide inertial support and improve power system frequency stability. However, under disturbances, VSG exhibits significant oscillations in its output frequency and power. Meanwhile, existing oscillation suppression methods rely on somewhat complex modeling and cumbersome parameter tuning. To address this issue, this paper proposes a straightforward approach to improving the transient performance of VSG based on the equivalent circuit model of the VSG active power loop. First, it is shown that the parameters in the VSG active power loop have a one-to-one correspondence with the elements of a RLC circuit. Based on the equivalent circuit model of VSG control, it is demonstrated that under the constraints of ROCOF and power–frequency droop limitation, oscillation suppression cannot be effectively achieved only by parameter tuning. Thus, an additional damping resistance branch is introduced into the VSG equivalent circuit model. The quantitative parameter design method of this damping branch is further introduced. Finally, high-power experiments demonstrate that the proposed method effectively suppresses power oscillations and enhances the transient performance of VSGs. Full article

The transition from conventional synchronous generators to inverter-based power systems has introduced significant challenges in stability, reliability, and protection coordination. Grid-forming inverters (GFMs) have emerged as a promising solution by emulating inertia and voltage regulation functions while enabling grid-supportive operation in weak or islanded networks. This study presents a structured qualitative review of the recent literature on GFM technologies. The selection process focused on control strategies, advanced semiconductor materials, protection frameworks, and cyber–physical security considerations. A thematic synthesis and comparative analysis were conducted to identify emerging trends and technical gaps. Among established approaches, virtual synchronous machine (VSM) and droop control remain widely adopted. More advanced strategies, including virtual oscillator control (VOC) and model predictive control (MPC), demonstrate improved dynamic performance in weak-grid conditions. Advances in semiconductor technologies, particularly Silicon Carbide (SiC) and Gallium Nitride (GaN), enable faster switching, higher efficiency, and enhanced thermal performance. The findings indicate a growing shift toward decentralized control architectures, fault-resilient converter topologies, and integrated protection–control co-design. Emerging solutions include grid-forming synchronization techniques that replace conventional phase-locked loop (PLL) structures, intrusion-tolerant inverter firmware with embedded anomaly detection, and predictive fault-clearing schemes tailored for low-inertia networks. Despite these advancements, several research gaps remain. These include limited large-scale validation of VOC and MPC strategies under high renewable penetration, insufficient interoperability metrics for legacy system integration, and a lack of standardized cybersecurity benchmarks across platforms. Future research should prioritize real-time experimental validation, robust protection co-design methodologies, and the development of regulatory and dynamic performance standards tailored to inverter-dominated grids. Strengthening protection coordination and interoperability frameworks will be essential to ensure the secure and stable deployment of GFMs in modern power systems. Full article

Grid-forming inverters (GFIs) are emerging as a key enabling technology for maintaining stability in renewable-dominated power systems, where conventional synchronous generation is progressively displaced by inverter-based resources. This paper presents a comprehensive technical review of GFI control strategies applied to photovoltaic (PV) systems, with focused attention on small-signal stability, transient dynamic performance, and overcurrent-limiting capabilities. In contrast to grid-following inverters (GFLIs), which rely on phase-locked-loop synchronization, GFIs operate as voltage sources capable of forming and regulating grid voltage and frequency. The reviewed control approaches, including droop control, virtual synchronous generator (VSG), synchronverter, matching control, virtual oscillator control (VOC), model predictive control (MPC), and intelligent techniques such as fuzzy logic control (FLC), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs), are systematically compared based on dynamic response characteristics, robustness under weak-grid conditions, control complexity, and practical implementation challenges. The paper synthesizes recent findings on stability margins, inertia emulation, transient current response, and protection requirements, highlighting remaining research gaps related to large-disturbance ride-through capability, coordination of multiple GFIs, and protection integration. These insights aim to support future deployments of reliable grid-forming photovoltaic systems in resilient inverter-dominated power networks. Full article

The increasing penetration of inverter-interfaced photovoltaic (PV) generation in active distribution networks (ADNs) intensifies fast voltage violations and makes real-time Volt-VAR control (VVC) challenging, especially when each inverter has only partial and noisy measurements and communication is limited. Existing local droop-type strategies lack coordination, while fully centralized optimization/learning is often impractical for online deployment. To address these gaps, an attention-enhanced multi-agent deep reinforcement learning (MADRL) framework is developed for inverter-based VVC under the centralized training and decentralized execution (CTDE) paradigm. First, the voltage regulation problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP) to explicitly account for system stochasticity and temporal variability under partial observability. To solve this complex game, an attention-enhanced MADRL architecture is employed, where an agent-level attention mechanism is integrated into the centralized critic. Unlike traditional methods that treat all neighbor information equally, the proposed mechanism enables each inverter agent to dynamically prioritize and selectively focus on the most influential states from other agents, effectively capturing complex intercorrelations while enhancing training stability and learning efficiency. Operating under the CTDE paradigm, the framework realizes coordinated reactive power support using only local measurements, ensuring high scalability and practical implementability in communication-constrained environments. Simulations on the IEEE 33-bus system with six PV inverters show that the proposed method reduces the average voltage deviation on the test set from 0.0117 p.u. (droop control) and 0.0112 p.u. (MADDPG) to 0.0074 p.u., while maintaining millisecond-level execution time comparable to other MADRL baselines. Scalability tests with up to 12 agents further demonstrate robust performance of the proposed method under higher PV penetration. Full article

The increasing penetration of inverter-based resources (IBRs) has been reducing system inertia and intensifying frequency stability challenges. Hence, various grid demands have been imposed on grid-connected systems, e.g., requiring the provision of an auxiliary service to the grid. In this context, this paper investigates the provision of synthesized inertia from the DC-link capacitors in grid-connected photovoltaic (PV) systems. For this configuration, the PV converter adopts a frequency–voltage droop control (FVDC) strategy, while a virtual synchronous generator (VSG) is employed on the grid side to emulate a synchronous generator, to enable the DC-link energy to contribute to primary frequency support. To quantify the virtual inertia and evaluate the closed-loop stability, a small-signal model of the inverter system is established. An eigenvalue analysis reveals that while increasing the DC-link voltage or capacitance enhances the achievable virtual inertia, it simultaneously narrows the stability margin. As such, comparative stability assessments under different parameter settings are performed, highlighting the distinct impacts of the DC-link voltages and capacitances on the emulated inertia and stability margins. The study provides insights into the maximum virtual inertia achievable via DC-link capacitors and offers practical guidelines for coordinating the controller and DC-link design to enhance frequency robustness in low-inertia power systems. Real-time hardware-in-the-loop (RT-HIL) tests validate the analytical findings. Full article

Parallel inverter systems constitute the fundamental units of AC microgrids and distributed renewable energy generation systems, wherein accurate power sharing among units represents a critical challenge for stable operation. Conventional droop control fails to share the harmonic power in proportionality to the capacity of inverters due to disparities on line impedance, leading to circulating currents, degraded power quality, and reduced system load capability. To address these issues, this paper proposes a harmonic power-sharing control strategy based on perturbative virtual impedance injection. Under the premise that fundamental power sharing according to capacity ratios has been ensured, the strategy first converts the harmonic power information of each inverter into a small-signal perturbation, which is injected into the virtual impedance of its fundamental control loop. Subsequently, by detecting the resulting variations in fundamental power coefficients induced by this perturbation, a closed-loop feedback is constructed to adaptively adjust the virtual impedance value of each inverter at harmonic frequencies. This adjustment enables the automatic matching of the harmonic power distribution ratio to the inverter capacity ratio, ultimately achieving precise harmonic power sharing. The proposed strategy operates without requiring inter-unit communication links or sampling the voltage at the common coupling point, relying solely on local information, thereby enhancing system reliability. Finally, the effectiveness of the proposed control strategy in achieving harmonic power sharing under conditions of line impedance mismatch is validated through an RT-LAB hardware-in-the-loop platform. Full article

Droop control is an effective power regulation method for islanded microgrids to cope with fluctuations in renewable energy and loads. However, its power coordination performance is easily affected by the line impedance. When virtual impedance is introduced to enhance impedance matching, fixed values struggle to adapt flexibly to varying grid conditions. To address this specific limitation, this paper proposes a novel power coordination control strategy based on real-time line impedance identification. The method first analyzes the power distribution principle and equilibrium conditions under droop control. Crucially, it then establishes a dynamic virtual impedance regulation mechanism. By continuously identifying the actual line impedance, the proposed strategy dynamically adjusts the virtual impedance, thereby reshaping the inverter’s output impedance in real-time to match the grid conditions. This approach directly enhances the inverter’s adaptability to impedance variations, which is the core challenge in robust power coordination. Simulation results demonstrate that, compared to methods using fixed virtual impedance, the proposed strategy significantly improves power-sharing accuracy and system robustness under uncertainties such as fluctuating line impedance and load changes. 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