Frequency Control Of Islanded 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.

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.

Bio-inspired distributed load frequency control in Islanded Microgrids: A multi-agent deep reinforcement learning approach

To prevent errors in load frequency control (LFC) decision-making in an islanded microgrid and reduce the waste of frequency regulation resources, a fully distributed “starfish” load frequency control method is proposed. For this method, a novel deep reinforcement learning algorithm called the distributed decomposed multirole multiagent deep deterministic policy gradient (DDMR-MADDPG) algorithm is introduced. This algorithm treats each unit as an agent, enabling centralized training of all the agents. Through the coordination and control of multiple agents, a global optimal strategy can be obtained. During online application, the algorithm employs a distributed implementation strategy, enabling each unit to make decisions autonomously. This decision-making strategy is akin to the distributed neural network of a starfish, and it can fundamentally improve the efficiency and correctness of decision-making. In addition, it employs multiple techniques, including value function decomposition, multirole learning, imitation learning, and curriculum learning approaches. The performance of the proposed method compared with 24 existing methods is demonstrated on a simulated LFC model of the Zhuzhou Island microgrid in China.

Event-Triggered Fractional PID-Based Load Frequency Control in Islanded Microgrids Under Cloud-Edge Collaborative Framework

To address the issue of frequency stability in microgrids with limited communication resources and bandwidth, this paper proposed an event-triggered control method based on a cloud-edge collaborative architecture. Firstly, to address the limited communication resources, event-triggered detector is set up at the edge to upload state variables and update control instructions only when the frequency deviation exceeds a pre-set threshold. Secondly, a fractional-order PI–PD controller is designed at the cloud control center, which achieves higher control degrees of freedom and stronger robustness than traditional integer-order PID control schemes. Additionally, to obtain better control parameters, an optimized design method based on an improved Big Bang-Big Crunch method (BB-BC3) is proposed for the fractional-order PI–PD controller. At last, a simulation example is given to prove the validity of the proposed method. Simulation results show that compared to the integer-order PI–PD controller, the fractional-order PI–PD controller based on the BB-BC3 algorithm proposed in this paper reduces the peak value of the response curve by 5%, and shortens the setting time by 0.14 s.

Design and Analysis of Novel Hybrid Grey Wolf and Cuckoo Search-Based Tilt Integral Derivative Controller for Frequency Control in Islanded Microgrid

The research work presents a novel approach for the control of frequency in an islanded AC Microgrid (MG)system. Different renewable energy sources such as Solar energy and wind energy are combined together to form a MG system. Any parametric change in renewable sources creates fluctuations in frequency and power in a Micro grid system. Thus in order to suppress these types of disturbance, a suitable controller has to be designed. Thus in this research work , a Tilt integral derivative (TID) controller has been designed, for MG system, whose parameters are optimized and tuned with the help of a novel hybrid Grey Wolf and Cuckoo Search (HGW-CS) algorithm to overcome the difficulties. The proposed Micro Grid is based on distributed generators like Diesel Engine Generator (DEG), Flywheel Energy Storage System (FESS), Battery Energy Storage System (BESS), Micro Turbine (MT), Fuel cell (FC) and Aqua-electrolyze (AE) are used in MG system to fulfill the load demand. The novel HGW-CS based TID controller is implemented in MG system and the robustness of the controller is investigated at different cases and it gives better performance with working well at any adverse situations. All the simulations are carried out with the help of MATLAB environment. It is depicted from the results that the proposed controller performs better than the conventional controllers

Nonfragile power and frequency control in islanded microgrids under abnormal asynchronous stochastic cyber attacks

In this paper, we focus on the real power sharing and frequency regulation of distributed generators in islanded microgrids with abnormal asynchronous stochastic cyber attacks, which is of great significance to the information security and stable operation of microgrids. Firstly, considering the possible cyber attacks in the communication network, a distributed non-fragile controller with coupled memory delay is proposed according to the nonperiodic sampled-data control. Then, the construction of delay-dependent two-sided looped-functional makes the Lyapunov–Krasovskii functional contain more delay and sampling information and relaxes constraints on free matrices. In addition, based on the enhanced integral inequality technique and the linear convex combination method, a sampling-based consensus protocol is presented to solve the issues of real power sharing and frequency regulation in the islanded microgrids. Finally, to verify the effectiveness and feasibility of the designed control strategy, a modified IEEE 34-bus test system is used for the experiment.

A Multiagent Quantum Deep Reinforcement Learning Method for Distributed Frequency Control of Islanded Microgrids

This article proposes a data-driven method for distributed frequency control of islanded microgrids based on multiagent quantum deep reinforcement learning (DRL). The proposed method combines the conventional DRL framework with quantum machine learning, and can adaptively obtain the optimal cooperative control strategy. The microgrid secondary frequency control is organized in a distributed manner in which each agent performs the control action only based on the local and neighboring information. To solve the DRL problem, the deep deterministic policy gradient algorithm is derived to tune the agents’ parameters. Simulation tests are performed on an islanded microgrid with four distributed generators and a 13-bus microgrid. The results demonstrate that the proposed method can effectively regulate the frequency with better time-delay tolerance, and displays the quantum advantage in parameter reduction.

Designing optimized PID controller using improved bacterial foraging optimization algorithm for robust frequency control of islanded microgrid

Designing optimized PID controller using improved bacterial foraging optimization algorithm for robust frequency control of islanded microgrid

Intelligent voltage and frequency control of islanded micro-grids based on power fluctuations and communication system uncertainty

Control of critical micro-grid factors such as voltage/frequency is complex and challenging in power distribution networks that include micro-grids. Therefore, providing an efficient and optimal control framework to maintain stability and control the critical factors of the network significantly affects the development of micro-grids goals. The present study proposed the intelligent voltage/frequency control scheme in primary and secondary micro-grids. Micro-grid voltage/frequency restoration was planned based on the PSO-Fuzzy control algorithm in the primary control. In addition to optimizing the members of the fuzzy controller, the PSO optimization algorithm was also used to calculate the voltage/ frequency deviation based on the voltage error factors and the violation of the deviation from the frequency reference. The PSO-Fuzzy structure was provided to optimize hybrid energy storage units' charge and discharge thresholds. Moreover, an intelligent artificial neural network algorithm was presented based on Lyapunov stability equations and a communication system platform to compensate for the voltage/frequency deviation in the secondary control. The proposed algorithm had optimal and efficient performance in different scenarios, such as the effect of load dynamic and its type, power fluctuations, and communication system delays.

Investigations of Decoupled Trigonometric Saturated and Fuzzy Logic Techniques for the Automatic Frequency Control of Islanded Microgrid

This paper involves the investigation of new techniques for the automatic load frequency control of islanded Microgrids. Microgrids are being established as a part of smart grid environment. In modern power systems, smart grid represents the solution for many of traditional power system problems such as frequency fluctuations. Frequency fluctuations have negative consequences in terms of electrical equipment life, production cost and production losses relative to costumers and electricity producers. So, the frequency of power system must be kept in acceptable range. In order to operate a power system with fixed frequency, it is necessary to always maintain a balance between the generation and the consumption of active power. The frequency droop control methods are widely used to control active power and frequency of the parallel synchronous generators in the traditional power grid. At present, this method has also been applied to the control of parallel inverters to share the load demand in proportion to their ratings. In order to improve the frequency control of traditional droop control technique applied in islanded microgrid, It is proposed to investigate the Decoupled Trigonometric Saturated (DTS) and fuzzy droop control techniques on islanded microgrids which uses meshed parallel inverter systems. To verify the performance of the proposed ALFC based on Decupled trigonometric saturated controller, fuzzy PI controller the MATLAB/SIMULINK environment is used.

Investigations of Decoupled Trigonometric Saturated and Fuzzy Logic Techniques for the Automatic Frequency Control of Islanded Microgrid

This paper involves the investigation of new techniques for the automatic load frequency control of islanded Microgrids. Microgrids are being established as a part of smart grid environment. In modern power systems, smart grid represents the solution for many of traditional power system problems such as frequency fluctuations. Frequency fluctuations have negative consequences in terms of electrical equipment life, production cost and production losses relative to costumers and electricity producers. So, the frequency of power system must be kept in acceptable range. In order to operate a power system with fixed frequency, it is necessary to always maintain a balance between the generation and the consumption of active power. The frequency droop control methods are widely used to control active power and frequency of the parallel synchronous generators in the traditional power grid. At present, this method has also been applied to the control of parallel inverters to share the load demand in proportion to their ratings. In order to improve the frequency control of traditional droop control technique applied in islanded microgrid, It is proposed to investigate the Decoupled Trigonometric Saturated (DTS) and fuzzy droop control techniques on islanded microgrids which uses meshed parallel inverter systems. To verify the performance of the proposed ALFC based on Decupled trigonometric saturated controller, fuzzy PI controller the MATLAB/SIMULINK environment is used.

Game theoretic vulnerability management for secondary frequency control of islanded microgrids against false data injection attacks

Game theoretic vulnerability management for secondary frequency control of islanded microgrids against false data injection attacks

Distributed Finite-Time Secondary Frequency Control of Islanded Microgrids With Enhanced Operational Flexibility

Consensus-based control has been widely applied in the frequency/voltage restoration and power sharing of microgrids. Considering the existence of plug-and-play devices and accidental communication failures in microgrids, the physical and cyber structure of a microgrid may be time-varying, where the performance of traditional consensus-based control methods under ideal cases may not be guaranteed. This article proposed a novel finite-time consensus-based secondary frequency control strategy, i.e., a control strategy with finite-time convergence performance. The convergence time of the proposed control strategy is independent of both the structure and operation states of the microgrid, which guarantees the effectiveness of the control strategy under flexible operating conditions. Moreover, a detailed performance analysis of the finite-time control strategy is presented, and an optimal design method of the control parameters is developed, which significantly improves the performance of the finite-time control strategy. The flexibility of the proposed control strategy, as well as the effectiveness of the optimal design method, is verified by both simulation and experimental results.

Stochastic Secondary Frequency Control of Islanded Microgrid Under Uncertainties

As the penetration of renewable energy, generally, uncertainties, increase in microgrids, a more dynamic and complex system is emerging that makes the load frequency control (LFC) of islanded microgrids more challenging due to their stochastic dynamic nature. This stochastic nature can create oscillatory frequency response, which eventually leads to deteriorating control function and instability even under primary and traditional secondary controllers. Due to the uncertainties, the measurements and microgrid states are continually perturbed; hence, they cannot be directly utilized for feedback control. Therefore, it is required to utilize an observer-based stochastic feedback control that correctly recognizes these uncertainties and sieves incorrect states, frequency deviation here, and can properly maintain the balance between the energy sources and the demand. This article develops an optimal observer-based secondary control for LFC, framed in two layers that are the stochastic unknown input observer (SUIO) and linear quadratic regulator. The proposed SUIO not only can address the uncertainties, e.g., renewable energy, load, and measurement noise, with efficient control effort but also performs robust against parameter changes of the microgrid. The simulation results confirm the efficacy of this proposed control as compared to the traditional secondary control method.

Research on Frequency Control of Islanded Microgrid with Multiple Distributed Power Sources

At present, some achievements have been made in the research on the energy management of microgrid operation. However, the research is mainly on the operation of grid-connected microgrid, while the research on the energy management of islanded microgrid is still relatively few. Frequency is one of the characteristics that affects the reliability and power quality of the microgrid. The essence of controlling frequency stability is to maintain source-load balance and redistribution of active power. Therefore, this paper proposes a frequency control strategy based on dynamically cutting machine to reduce load by analyzing the use priority of different distributed power supply and the division of load importance degree, and combining the influence degree of different frequency variation range on microgrid. To coordinate and control distributed power supply, energy storage device, and load in different frequency change areas, this paper proposes different control strategies. The seed strategies of the control strategy are discussed one by one. Experimental results show that the frequency control strategy can significantly improve the frequency stability of the power supply system and reduce the operating cost of islanded microgrid.

Online generalized droop-based demand response for frequency control in islanded microgrids

Frequency stability, as one of the most important issues in the modern power grids, requires more efficient control methods due to the increasing complexity of the power system, high penetration of distributed generation sources as well as high electrical energy consumption. The challenges become more critical in the case of islanded microgrids (MGs), due to existing no traditional ancillary services of the upstream electric power network. Thus, the modern power grids, such as MGs, need advanced regulation methods to keep the generation-consumption balancing. Demand response (DR) is the recently introduced control approach which guarantees continuous contribution of controllable loads in the system frequency control. In this paper, a new online droop-based DR, generalized droop control (GDC), is introduced to apply in islanded MGs frequency control. An artificial neural network is used for online tuning of droop coefficients in the presented GDC framework. The proposed control approach changes controllable active and reactive loads, using a set of equations based on satisfying dynamics. To evaluate the effectiveness of the proposed control method, several scenarios are simulated in which changes of the system frequency and voltage are studied. Results show significant damping of power–frequency fluctuation and a desirable performance of the closed-loop system.

Frequency control of islanded microgrid using fuzzy-PI and autotuned controllers

<p>Any mismatch between generation and demand causes frequency to deviate from nominal value which affects the microgrid operation and reliability of power flow. The load frequency changes abnormally, which is fuzzy in nature, due to low system inertia and unpredictable variation in wind and solar irradiance level. So a frequency controller is needed to solve this problem meeting generation and demand of an islanded microgrid system considering the fuzziness in frequency fluctuation. This paper presents a case study of a hybrid microgrid system consisting of PV system, wind turbine generator set, diesel generator set along with storage facility and equipped with a proposed fuzzy-PI controller for frequency control under islanded condition. This controller shows satisfactory steady-state response. Further, performance of the proposed fuzzy-PI controller is verified with that of an autotuned PI controller to get faster response. The change in frequency is found minimum in case of autotuned PI controller as compared to fuzzy-PI controller. The proposed fuzzy-PI controller is validated based on ITAE (4-7%) which is higher than that attained form autotuned-PI controller. The developed model is simulated in Matlab/Simulink environment in this case study.</p>

Multi-Agent System with Plug and Play Feature for Distributed Secondary Control in Microgrid—Controller and Power Hardware-in-the-Loop Implementation

Distributed control and optimization strategies are a promising alternative approach to centralized control within microgrids. In this paper, a multi-agent system is developed to deal with the distributed secondary control of islanded microgrids. Two main challenges are identified in the coordination of a microgrid: (i) interoperability among equipment from different vendors; and (ii) online re-configuration of the network in the case of alteration of topology. To cope with these challenges, the agents are designed to communicate with physical devices via the industrial standard IEC 61850 and incorporate a plug and play feature. This allows interoperability within a microgrid at agent layer as well as allows for online re-configuration upon topology alteration. A test case of distributed frequency control of islanded microgrid with various scenarios was conducted to validate the operation of proposed approach under controller and power hardware-in-the-loop environment, comprising prototypical hardware agent systems and realistic communications network.

Feedback Linearization Based Distributed Model Predictive Control for Secondary Control of Islanded Microgrid

AbstractIn this paper, a distributed Model Predictive Control (DMPC) is proposed for the secondary voltage and frequency control of islanded microgrid, where each distributed generator (DG) is controlled by a Model Predictive Control (MPC) in the secondary control layer, individually. With considering the nonlinear dynamics of DG with primary control, input‐output feedback schemes are developed for voltage and frequency control separately. Then, all MPCs use the local and neighboring nodes information to solve the optimization problem instead of communicating with a central controller. In this way, the control of the whole system is fully distributed, which allows for a plug‐and‐play. The convergence and stability analysis of the overall closed‐loop system are provided. The simulation result shows the effectiveness of the proposed method.

Distributed Robust Finite-Time Secondary Voltage and Frequency Control of Islanded Microgrids

This paper presents a distributed, robust, finite-time secondary control for both voltage and frequency restoration of an islanded microgrid with droop-controlled inverter-based distributed generators (DGs). The distributed cooperative secondary control is fully distributed (i.e., uses only the information of neighboring DGs that can communicate with one another through a sparse communication network). In contrast to existing distributed methods that require a detailed model of the system (such as line impedances, loads, other DG units parameters, and even the microgrid configuration, which are practically unknown), the proposed protocols are synthesized by considering the unmodeled dynamics, unknown disturbances, and uncertainties in their models. The other novel idea in this paper is that the consensus-based distributed controllers restore the islanded microgrid's voltage magnitudes and frequency to their reference values for all DGs within finite time, irrespective of parametric uncertainties, unmodeled dynamics, and disturbances, while providing accurate real-power sharing. Moreover, the proposed method considers the coupling between the frequency and voltage of the islanded microgrid. Unlike conventional distributed controllers, the proposed approach quickly reaches consensus and exhibits a more accurate robust performance. Finally, we verify the proposed control strategy's performance using the MATLAB/SimPowerSystems toolbox.