This paper presents an integrated control and transactional framework aimed at enhancing the stability, resilience, and automation of decentralized microgrid systems. The proposed approach combines an advanced Sliding Mode Control (SMC) scheme with blockchain-enabled smart contracts to address critical challenges associated with conventional control strategies, including slow convergence rates, susceptibility to disturbances, and limited scalability. The SMC is augmented with a nonlinear disturbance observer to provide fast transient response, robust disturbance rejection, and reduced control chattering under dynamic operating conditions. A closed-loop interaction between the SMC and blockchain layers enables continuous two-way communication. Real-time operational parameters, such as power imbalances, voltage deviations, and frequency fluctuations, are transmitted from the controller to the blockchain layer. In response, smart contracts autonomously trigger control adjustments and Demand Response (DR) actions, which are fed back to the SMC as reference inputs. This dynamic feedback loop enables the system to adapt to fluctuations and uncertainties in both energy generation and consumption, thereby ensuring consistent power quality and system stability. Experimental results confirm the system’s ability to maintain stability and improve power quality under varying operational conditions. Moreover, the system facilitates coordinated energy exchange among interconnected microgrids, thereby supporting the integration of local renewable energy sources and reducing dependence on centralized grid infrastructure.

This research article presents an innovative approach to enhance the performance and robustness of voltage source converters within microgrid systems. Existing controls suffer from slow convergence and unsatisfactory transient performance when dealing with disturbances, especially in the presence of control delay. To discourse these limitations, the proposed sliding mode controller is augmented with a nonlinear disturbance observer to enhance the disturbance elimination capability, attenuates the effect of parameter uncertainties, superior voltage reference tracking and suppressing chattering in the control response. The observer is introduced to estimate sensor values, potentially eliminating the need for dedicated sensors. The proposed control strategy employs a switching Lyapunov function-based mathematical model, addressing dynamic response limitations and switching action incorporating nonlinearities in the proposed model. By incorporating Lyapunov-based stability analysis, the article overcomes limitations and conservatisms associated with traditional linearized techniques, enabling more accurate stability assessments. Online adaptation norms are integrated to estimate and reject external disturbances, aligning closed-loop responses with reference models. Extensive numerical simulations and hardware-in-the-loop experiments validate the improved performance, highlighting the elimination of chattering and enhanced robustness against step and stochastic disturbances.

The integration of renewable energy sources and distributed energy resources (DERs) has driven the evolution of modernized nested microgrids, enhancing resilience and flexibility in power distribution systems. Grid-following (GFL) and grid-forming (GFM) inverters are central to these systems, with GFL units emulating current sources challenged by uncertain grid impedance, and GFM units emulating voltage sources required to adapt to dynamic load variations. Mode transitions introduce instability through multi-loop control interactions. This work presents a comprehensive dynamical stability analysis of GFL and GFM inverters in nested microgrids, supported by advanced control strategies addressing dynamic response limitations, sensor dependencies, filter fluctuations, and controller complexities. An eigenvalue-based framework identifies dominant oscillatory modes, while online adaptation mitigates disturbances to preserve closed-loop performance. Time-evolution modeling of observables enables enhanced real-time monitoring. A blockchain-enabled decentralized framework ensures secure, transparent, and automated stability actions. Hardware-in-the-loop (HIL) experiments on a modified IEEE 123-node test feeder demonstrate a total harmonic distortion (THD) of 1.75% under weak-grid conditions compared with 2.73%, 4.76%, 8.40%, and 2.2% for other approaches and 0.3% under grid-impedance variation and <0.3% under nonlinear loading. The proposed controller achieves 0.06% tracking error dynamics and 0.02% steady-state error, outperforming classical methods (0.32–0.87% and 0.17–0.38%, respectively), with a computational time of 29 ms. The blockchain layer, implemented on the Polygon network, achieved a measured throughput of 1,572 transactions/s, an average block time of 2.3 s, and transaction fees below $0.01 USD, enabling rapid, economical, and scalable peer-to-peer stability service execution.

The integration of distributed energy resources into modernized networked microgrids and the increasing variability in load dynamics present significant instability challenges. This study seeks to provide a comprehensive understanding of the stability of cascaded interactions in nonlinear multi-timescale systems consisting of grid following and grid forming inverters. Furthermore, the proposed controller, aided by the nonlinear Lyapunov function, offers an improved and resilient approach to address the challenges associated with nonlinear plant and line/network dynamics, uncertainties in state variables, and unmodeled system dynamics. Asymptotic spatio-temporal dynamical stability is guaranteed for the closed loop control system through constrained time state convergence to stable equilibrium points while control oscillations are kept to a minimum. The combination of comparative analysis and high-fidelity hardware in loop emulations provided robust evidence of the superior performance of our proposed controller over traditional controllers in diverse dynamic scenarios. Its ability to rapidly reject disturbances, minimize overshoot, converge efficiently, and enhance power quality confirms its effectiveness and highlights its potential for real-world applications.

The integration of distributed energy resources into modernized networked microgrids, combined with the increasing variability in load dynamics, presents significant stability challenges. This research offers a comprehensive analysis of the stability of cascaded interactions in nonlinear multi-timescale systems, including grid-following and grid-forming inverters. The proposed grid-forming controller, integrated with energy storage systems and a nonlinear Lyapunov function, facilitates seamless control and stabilization of these inverters. This approach addresses challenges related to nonlinear plant and network dynamics, uncertainties in state variables, and unmodeled system dynamics. The closed-loop control system ensures asymptotic spatio-temporal dynamical stability through constrained time state convergence to stable equilibrium points while minimizing control oscillations. Comparative analysis and high-fidelity hardware-in-loop emulations demonstrate the superior performance of the proposed controller over traditional ones in diverse dynamic scenarios. Its ability to rapidly reject disturbances, minimize overshoot, converge efficiently, and enhance power quality confirms its effectiveness and highlights its potential for real-world applications.

The stability of grid-connected active front-end converters (AFEs) is often compromised by the intricate interplay between inherent converter nonlinearities and grid dynamics. This article presents a novel approach to dynamic model predictive current control (MPC) by leveraging recursive least squares (RLS) to accurately estimate the properties of a physical model. This estimation mitigates stability issues, setting the stage for improved control. Unlike conventional methods, our proposed RLS-based MPC, enriched with an auto-tuning feature, empowers controller development without the upfront need for precise external dynamics. This not only ensures high disturbance rejection but also maintains a high-fidelity control performance, rendering the approach versatile for applications where obtaining or predicting precise external dynamics is challenging. At each sampling interval, a cost function is applied to predicted variables to discern optimal switching states. Through extensive simulation studies covering diverse grid impedance changes and system nonlinearities, we evaluate the controller's effectiveness. To underscore its superiority over traditional controls, simulation results are validated on a laboratory hardware platform equipped with Typhoon HIL and dSPACE real-time emulators, further attesting to the robustness and practical viability of our proposed approach.

The pursuit of seamless formation and robust control of inverters in power electronic-dominated grids face challenges arising from uncertainties in grid impedance, dynamic load variations, and transitional phase jump scenarios, leading to elevated instability during mode transitions. These challenges necessitate a nuanced approach to voltage regulation and stability analysis to manage the stochastic nonlinearities and inherent dynamics effectively. This paper introduces a novel Unified Koopman-based Model Predictive Control (K-MPC) method that synergizes a modified MPC framework with an ensemble approach and a saturation-like automatic adaptation function for seamless inverter transitions. It facilitates precise power-sharing among inverters in isolated microgrids by adjusting output impedances without the need for communication lines, thereby addressing the limitations in dynamic response, sensor requirements, filter fluctuations, and controller complexity. The K-MPC method enhances system performance and fidelity by incorporating online adaptation norms for external disturbance rejection, aligning closely with a predefined reference model. Quantitative validation, conducted through frequency response analysis and hardware-in-the-loop (HIL) experiments on an IEEE 123-node test system, underscores the method’s effectiveness. The K-MPC approach notably reduces total harmonic distortion (THD) by up to 30% relative to conventional control strategies and improves power-sharing precision among inverters by 25% under dynamic loading conditions. Furthermore, it exhibits a 40% faster response in adapting to external disturbances, ensuring voltage and frequency remain within target thresholds.

AbstractThe operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios.

The classical current control techniques for grid connected LCL type active front-end converters (AFEs) are usually interacts with the phase locked loop (PLL) nonlinearities and grid impedance, thus deteriorates the stability of the converter which leads to system instability. In order to avoid the stability issues, this manuscript provides a simpler and much better recursive least square based dynamic model predictive current control technique to approximate the attributes of physical model to their true value. In this control structure the predicted variables are evaluated using the cost function at each sample interval and the most efficient switching state with the lowest cost function value is chosen. According to the estimated switching states, the gate pulses are generated for the AFEs switches. The performance of the proposed control structure is tested using simulation platform under a widespread range of variations in grid impendence and system's nonlinearities. Under these conditions, the suggested method which considers the system's nonlinearities provides outstanding resilience in high and medium frequency grid dynamics. Furthermore, the simulations results have been varied through hardware platform using Typhoon HIL to show the advantages of proposed control over classical controls.

Microgrid (MG) system functionality is limited by low inertia, nonlinearity, dynamical operation regions and system and network dynamics. The proposed unique model-based method analyzes nonlinear dynamic behavior and parameter uncertainty propagation in inverter-based resource studies using Koopman mode-based eigen analysis and extending it to koopman-based predictive control (K-PC). The state-space (SS) model is then expanded by redefining uncertain components as pseudo-state variables. The modified system's dynamics can further be investigated across time using Koopman eigenfunctions, eigenvalues, and modes. Finally, frequency domain and a hardware-in-the-loop (HIL) based time domain analysis on a networked microgrid test system with grid-forming (GFM) and grid following (GFL) inverters confirms the proposed methodology.

The preordained time delay in digital controlled power electronic converters has a substantial impact on the overall system's stability as it will vary the phase-frequency characteristic, further accounting for unstable system dynamics. The stability of a 3-phase grid-interfaced converter with an LCL filter is analyzed in frequency domain considering the time delay of the digitally controlled system, inner current and voltage, and outer power control loops. The indication of a low-phase margin in the minor loop gain of the Nyquist plot reveals the existence of harmonics in the system. Delay compensation and stabilization is also carried out using a Third Order Generalized Integrator (TOGI). Simulation results verify the efficacy of the proposed analysis.

In a distributed generation system, the nonlinear dynamics of a grid-interfaced inverter (GII) cause dynamic instability and power quality issues. Because traditional linear approaches rely on parametric fluctuations in controller parameters, adequate tuning is required. Some of the foregoing issues can be addressed using feedback and feedforward damping methods, but they require extra sensors and are ineffective in maintaining resilience against grid impedance variations and system parametric changes. Due to the digital control delay, they also add negative damping in a given frequency band, resulting in non-minimum phase behavior. The suggested control solution adds an observer-based adaptive control architecture to the existing inverter control. The concept of an energy function is used to depict grid dynamics at the GII terminals. On this dynamic energy signal, the nonlinear function will act to derive an ideal control parameter for the enhanced control algorithm. The proposed algorithm, which considers the system’s nonlinearities, provides more robustness amid high- and medium-frequency grid dynamics, as demonstrated by simulation and experimental studies.

The non-linear dynamics of a grid-interfaced inverter (GII) in a distributed generation system results in dynamic instability and power quality issues. The conventional linear methods depend on parametric variations in controller parameters hence, tuning should be proper. Also, linearization of dynamical states around steady-state operating point works only if the controllers operate around a fixed operating point thus, only ensuring local stability. The feedback and feedforward damping methods can address some of the above challenges but requires additional sensors and also incapable of maintaining robustness against grid impedance variations and system parametric changes. To address aforementioned issues, this journal presents a non-linear Lyapunov Krasovskii Passivity (LK-PBC) based dynamic controller for Grid-interfaced Voltage Source Converters (VSCs). A generalized admittance model is derived which is used to calculate Non-passive regions of the VSC where the output admittance will have a negative real part. The damping provided by the proposed controller also eliminates the resonance instability issues in the concerned frequency scale regardless of the admittance seen at the inverter terminals. It also takes care of time delay and norm-bounded state uncertainties. The evaluation of the theoretical studies and efficacy of the proposed LK-PBC controller is confirmed by hardware in loop analysis on a laboratory experimental setup.

A Grid-Forming Converter (GFMC) is a critical part for proper operation of an isolated microgrid (MG). Its aim is to generate voltage reference for rest of the inverter- based resources (IBRs) in the MG just like a traditional slack bus generator. To operate a GFMC in a d-q reference frame, single or dual loop proportional-integral (PI) controller is typically used. However, under system and grid parametric fluctuations, the above controller performs poorly. The voltage source converter (VSC) should operate for grid-tied (GT) as well as stand-alone (SA) mode to function as a grid following converter (GFLC) for power delivery to local loads where each mode has its own control loop. It should be seamless in providing an uninterruptible supply of power to the local load. Hence, for the addressable challenges, a non-linear model of both GFMC and GFLC that covers all system and grid parametric variables is devised along with stochastic eigen analysis. The suggested technique has an accurate model for the converter system which can cope with LC filter resonance and uncertainties more effectively where the need of any passive or active dampening methods is unessential. Simulations and tests were carried out to validate the suggested methodology. The simulation and experimental findings indicate that the suggested control strategy may be utilized to accomplish autonomous and smooth mode transitions even in the presence of norm-bound state uncertainty, as well as to offer resilience against grid impedance, system, and grid parameter fluctuations.

The ever increasing population has posted a serious mismatch between supply and demand of electrical energy. The whole power system network needs to be restructured and new units must be deployed to mitigate this problem. A new effective technique known as Demand Side Management (DSM) can efficiently optimize resource utilization without the further need of adding up new units resulting in unnecessary capital cost. In this paper efficient DSM strategies for the deregulated market environments are put forth. Also a game theoretic model has been proposed to optimize the demand response in the smart grid. Our simulation results show that there is a decrease of around 20% in user's electricity bill when they schedule their appliances according to our proposed concept.