Frequency Control Of Microgrid Research Articles

Distributed robust fixed-time secondary frequency and voltage control of islanded microgrids with external disturbances

This article primarily examines the fixed-time secondary voltage and frequency control of islanded microgrids with external disturbances. First, a distributed observer is introduced to ensure that each distributed generator (DG) can accurately estimate the reference voltage level. Subsequently, a distributed integral sliding mode is constructed through the super-twisting algorithm, and the continuous super-twisting part is used to compensate for the disturbances and eliminate chattering phenomena. Furthermore, a distributed secondary control strategy is designed using the backstepping method, which can achieve fixed-time regulation of voltage and frequency in a microgrid with unknown parameters. Finally, the validity of the method is proved in MATLAB ® /SimPowerSystems.

Dynamic event‐triggered‐based adaptive frequency control of microgrids under cyber‐attacks via adaptive dynamic programming

AbstractThe increasing penetration of renewable energy sources (RES) and the development of the cyber‐physical microgrids (CPMs) make greater demands for frequency control of microgrids. The common approach for frequency control is controlling the micro‐turbine to compensate for frequency deviations, with energy storage systems serving as an auxiliary approach. This article proposes an online adaptive frequency control method to control the governor and energy storage to realize the frequency recovery of microgrid, subject to the external unknown disturbances caused by wind turbine, power load and false data injection (FDI) attacks. First, the non‐zero sum (NZS) games of the considered system are modeled in this work, where the unknown disturbances are also taken into account. For the sake of estimating the unknown disturbances, a disturbance observer (DOB) for the microgrid system is introduced. Then, on the basis of the estimated results of the applied DOB, the disturbance compensation input is derived to offset the interference of the unknown disturbance. Meanwhile, the adaptive dynamic programming (ADP) approach is employed to derive the adaptive optimal control input for the NZS games of microgrid system. Besides, the dynamic event‐triggered (DET) control is introduced, reducing the occupation of computing resources. By utilizing the Lyapunov's method, the stability of the closed‐loop system, the convergence of the estimation weight, the estimation disturbances and the system state are guaranteed. The effectiveness of the proposed method is ultimately verified by the simulation results.

Chaos-based optimization for load frequency control in Islanded airport microgrids with hydrogen energy and electric aircraft

Chaos-based optimization for load frequency control in Islanded airport microgrids with hydrogen energy and electric aircraft

Delay-Dependent Stability Evaluation for Temperature Control Load Participating in Load Frequency Control of Microgrid

Delay-Dependent Stability Evaluation for Temperature Control Load Participating in Load Frequency Control of Microgrid

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80–90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

A maiden application of an MGO-tuned cascade controller for the secondary frequency control in biogas-powered marine shipboard microgrid

The use of renewable energy sources in marine microgrids has become crucial to limiting the emissions from the shipping industry. Hence, sources like biogas, solar photo-voltaic, sea wave energy, etc. are being integrated rapidly into marine microgrids. In this study, a novel implementation of cascaded controllers like 1 + PI-TID and 1 + PD-PI is modelled for the secondary load frequency control of the shipboard system. The proposed controllers are then tuned with a newly developed metaheuristic algorithm i.e. mountain gazelle optimisation. Moreover, a comparative study is done with other newly developed algorithms. The performance of these controllers is checked in diverse scenarios. The proposed method gives a 23.79% and 54.07% increase stability for the conventional PI controllers. Furthermore, for studying cyber-attacks in the proposed system, various durations of communication delay are introduced and its stability is evaluated. Finally, sensitivity and uncertainty analysis is performed for the validation of the effectiveness of the proposed controller.

Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters

Smart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.

Voltage and Frequency Control of Microgrids Considering State Constraints and Attacks

Voltage and Frequency Control of Microgrids Considering State Constraints and Attacks

Privacy-preserving distributed frequency control for AC microgrids with constrained communication

Distributed control of AC microgrids is one of the most popular methods in the islanded operation mode. Whereas, most of the existing studies either do not consider the potential threat of privacy security or rely on the assumption of ideal communication networks. To this end, this paper presents a novel privacy-preserving distributed secondary frequency control strategy for the privacy protection problem of an islanded AC microgrid with constrained communication. The key contributions of this paper are threefold. (1) Different from the existing privacy-preserving approaches used in AC microgrids, a time-varying function is introduced to mask interactive information such that the frequency cannot be reconstructed by malicious attackers. (2) An event-triggered communication scheme is employed to cope with the constrained communication environment. (3) A privacy-preserving distributed event-triggered control strategy with communication delay is developed such that the frequency restoration and active power sharing of the microgrid are guaranteed. Moreover, the maximum communication delay that the proposed control can withstand is analyzed. Simulation results show the properties of the privacy preservation, the decrease of communication load, and the bounded communication delay allowed in the proposed control strategy.

Frequency Stabilization Based on a TFOID-Accelerated Fractional Controller for Intelligent Electrical Vehicles Integration in Low-Inertia Microgrid Systems

Microgrid systems face challenges in preserving frequency stability due to the fluctuating nature of renewable energy sources (RESs), underscoring the importance of advanced frequency stabilization strategies. To ensure power system stability in situations where renewable energy significantly contributes to the energy mix, it is essential to implement load frequency controllers (LFCs). Moreover, with the widespread use of electric vehicles (EVs), leveraging battery storage from EVs for microgrid frequency control is becoming increasingly crucial. This integration enhances grid stability and offers a sustainable solution by utilizing renewable energy more efficiently and reducing dependency on traditional power sources. Therefore, this paper proposes an innovative approach to LFCs, using fractional-order control techniques to boost the resilience of the interconnected microgrid systems. The approach centers on a centralized control scheme with a tilt fractional-order integral-derivative featuring an accelerated derivative (TFOID-Accelerated) controller. The accelerated derivative component of this controller is tailored to mitigate high-frequency disturbances, while its tilt feature and fractional integration effectively handle disturbances at lower frequencies. As a result, the proposed controller is expected to efficiently counteract disturbances caused by variability in RESs and/or load changes, achieving a high level of disturbance rejection. Additionally, this paper employs the recent growth optimizer (GO) method for the optimal design of the controller’s parameter set, avoiding the need for complex control theories, elaborate disturbance observers, filters, and precise power system modeling. The GO algorithm enhances fractional-order capabilities, offering a robust solution to the challenges of renewable energy variability and demand fluctuations. This is accomplished by optimizing parameters and simplifying the control system design across different microgrid scenarios. The proposed TFOID-Accelerated LFC demonstrates superior performance in enhancing frequency stability and minimizing oscillations compared to existing controllers, including traditional proportional-integral-derivative (PID), PID-Accelerated (PIDA), and tilt-integral-derivative (TID) controllers.

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Suppression of low‐frequency oscillations in hybrid/multi microgrid systems with an improved model predictive controller

AbstractGrid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method.

Adaptive Load Frequency Control in Microgrids Considering PV Sources and EVs Impacts: Applications of Hybrid Sine Cosine Optimizer and Balloon Effect Identifier Algorithms

The negative impacts of microgrids (µGs) on the load frequency highlight the importance of implementing a robust, efficient, and adaptable controller to ensure stability. This work introduces an adaptive load frequency control (LFC) for an isolated µG that includes a PV system and electric vehicles (EVs), which have a significant impact on frequency. This control utilizes a combination of sine cosine optimization (SCO) and balloon effect identifier (BEI) algorithms. The controller presented in this work transforms the LFC process into an optimization problem that is highly compatible with various random situations encountered in the control process. The suggested control method is a novel approach by utilizing SCO+BEI for adaptive LFC application, resulting in a highly efficient response. The effectiveness of the proposed adaptive controller is assessed under the conditions of 17 MW variable load, system parameters uncertainties, and installed PV systems of 6 MW. MATLAB / Simulink package is rummage-sale as a digital test environment. According to simulation results, the proposed adaptive controller succeeds in regulating the frequency and power of an islanded µG. To measure the efficiency of the proposed control scheme, a comparison between other control techniques (such as adaptive controller using Jaya+BEI and classical integral controller) is done. The findings of the studied scenarios assured that the not compulsory control method using (SCO+BEI) has an obvious superiority over other control methods in terms of frequency solidity in case of random load instabilities and parameter uncertainties. Finally, it can be said that the proposed controller can better ensure the safe operation of the µGs.

Improved frequency control in microgrids dominated by renewables and grid-forming energy storage

Improved frequency control in microgrids dominated by renewables and grid-forming energy storage

Voltage and frequency control of microgrid in presence of micro-turbine interfaced to matrix converter

The active and reactive load changes have a significant impact on voltage and frequency. In this paper, in order to stabilize the microgrid (MG) against load variations in islanding mode, the active and reactive power of all distributed generators (DGs), including energy storage (battery), diesel generator, and micro-turbine, are controlled. The micro-turbine generator is connected to MG through a three-phase to three-phase matrix converter, and the droop control method is applied for controlling the voltage and frequency of MG. In addition, a method is introduced for voltage and frequency control of micro-turbines in the transition state from grid-connected mode to islanding mode. A novel switching strategy of the matrix converter is used for converting the high-frequency output voltage of the micro-turbine to the grid-side frequency of the utility system. Moreover, using the switching strategy, the low-order harmonics in the output current and voltage are not produced, and consequently, the size of the output filter would be reduced. In fact, the suggested control strategy is load-independent and has no frequency conversion restrictions. The proposed approach for voltage and frequency regulation demonstrates exceptional performance and favorable response across various load alteration scenarios. The suggested strategy is examined in several scenarios in the MG test systems, and the simulation results are addressed.

Modified Tilt-integral active disturbance rejection controller with inertia emulated direct current link for frequency control of marine microgrid

The increasing integration of intermittent renewable sources (RSs) poses a dynamic frequency stability challenge for modern marine vessel microgrids. To address this issue, this paper proposes a novel control approach, specifically targeting frequency and tie-line power stabilization in a diverse source marine microgrid (MμG) with two intertied areas featuring renewable (wind-wave) sources. The suggested approach introduces a modified tilt-integral active disturbance rejection (TI-ADRC) controller designed to ensure effective damping of power frequency oscillations. As the control scheme depends on the optimal setting of the proposed controller, a recently developed marine predator technique (MPT) has been adopted. The performance of the proposed controller is compared with other recent controllers viz. PID, tilt-integral derivative (TID), two-degree-of-freedom (2DOF)-PID, fuzzy-PI, and ADRC to validate its superiority. To further enhance the system dynamics, a precise modeling of inertia emulated direct current (IEPDC) tie link is incorporated in microgrid system. The impact assessments, considering time delays with pre/post IEPDC link, demonstrate a substantial 57.79% and 81.53% reduction in peak frequency overshoot compared to DC link (conventional model) and AC link, respectively. The analysis of the eigen plot confirms the stability of the control system. Sensitivity assessments of the controller against ± 30% parametric variations and load fluctuations are conducted, affirming its robustness. Finally, the result from OPAL-RT confirm the practicality of the proposed method. It is asserted that the suggested controller is reliable and functions well in n the presence of diverse disruptions and parametric variations.

Quantum-inspired deep reinforcement learning for adaptive frequency control of low carbon park island microgrid considering renewable energy sources

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning’s experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost.

Optimizing load frequency control in microgrid with vehicle-to-grid integration in Australia: Based on an enhanced control approach

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency.

Enhanced harris hawks optimization based load frequency control of multi area microgrid based water treatment plant with consideration of 3DOF-(FO-PIDN)/(TIDN) controller

Municipal water supply system (WSS) consist of different pumping stages viz. intake, water treatment plant (WTP) and intermediate pumping station (IPS). Usually, the power supply for WSS is obtained through public power tapping sources. However, this often leads to load shedding and disruption of the water supply. This paper focuses on the concept, considering WSS as a multi-source multi-area microgrid scheme, this includes renewable energy sources (RES) such as solar, wind, etc. Moreover, the study incorporates a Battery Energy Storage System (BESS) and a Diesel Engine Generator (DEG) to provide power supply during peak demand at each pumping station. Frequency control is essential for optimizing system performance. This paper proposes Enhanced Harris Hawks Optimization Algorithm (EHHO) based PID controller for regulating the frequency in the multi-microgrid-based water supply system. The proposed controller is implemented in MATLAB simulation software, and its response is compared with other optimization methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Moreover, implementation and comparison of higher degree order controller such as 3DOF-FOPIDN controller and 3DOF-TIDN controllers are tested under PSO method to observe the performance as well as robustness of the controller. The results indicate that the proposed controller provides better performance in controlling the load frequency deviation, thus improving the efficiency and reliability of the multi-microgrid system for consideration of municipal water supply.

Deep reinforcement learning for adaptive frequency control of island microgrid considering control performance and economy

To achieve frequency stability and economic efficiency in isolated microgrids, grid operators face a trade-off between multiple performance indicators. This paper introduces a data-driven adaptive load frequency control (DD-ALFC) approach, where the load frequency controller is modeled as an agent that can balance different objectives autonomously. The paper also proposes a priority replay soft actor critic (PR-SAC) algorithm to implement the DD-ALFC method. The PR-SAC algorithm enhances the policy randomness by using entropy regularization and maximization, and improves the learning adaptability and generalization by using priority experience replay. The proposed DD-ALFC method based on the PR-SAC algorithm can achieve higher adaptability and robustness in complex microgrid environments with multiple performance indicators, and improve both the frequency control and the economic efficiency. The paper validates the effectiveness of the proposed method in the Zhuzhou Island microgrid.