Frequency Control Of Microgrid Research Articles

Optimal Load Frequency Control of a Hybrid Electric Shipboard Microgrid Using Jellyfish Search Optimization Algorithm

This paper examines the critical topic of load frequency control (LFC) in shipboard microgrids (SMGs), which face challenges due to low system inertia and the intermittent power injection of renewable energy sources. To maintain a constant frequency (even under system uncertainties), a robust and well-tuned controller is required. In this paper, a study was conducted first by examining the performance of three different controller architectures, in order to determine which is the most-appropriate for the multi-energy SMG system. The time delays that occur due to communication links between the sensors and the controller were also considered in the analysis. The controllers were tuned using a very recent bio-inspired optimization algorithm called the jellyfish search optimizer (JSO), which has not been used until recently in LFC problems. To assess the tuning efficiency of the proposed optimization algorithm, the SMG’s frequency response results were comprehensively compared to the results obtained with other bio-inspired optimization algorithms. The results showed that the controllers with gains provided by the JSO outperformed those tuned with other bio-inspired optimization counterparts, with improvements in performance ranging from 19.13% to 93.49%. Furthermore, the robustness of the selected controller was evaluated under various SMG operational scenarios. The obtained results clearly demonstrated that the controller’s gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation.

Centralized Model Predictive Control for Transient Frequency Control in Islanded Inverter-Based Microgrids

Centralized Model Predictive Control for Transient Frequency Control in Islanded Inverter-Based Microgrids

Load frequency control for two-area hybrid microgrids using model predictive control optimized by grey wolf-pattern search algorithm

Load frequency control for two-area hybrid microgrids using model predictive control optimized by grey wolf-pattern search algorithm

Adaptive nonlinear controllers based approach to improve the frequency control of multi islanded interconnected microgrids

Islanded microgrids (MGs) are now widely used to electrify rural areas at a lesser cost and with greater efficiency. To maintain system balance and guarantee stability when exposed to various disturbances, MGs should be equipped with efficient controllers. Traditional controllers (like the PI controller) are linear and offer the best performance at a certain operating point, but the performance may degrade when the operating situation changes. To mitigate the drawbacks of fixed parameters controller, the paper suggested a fuzzy PI controller-based model reference adaptive control (FPI-MRAC) optimized by an advanced meta-heuristic optimization technique coronavirus herd immunity optimizer (CHIO) for enhancing the dynamic performance of several interconnected MGs. The proposed controller is non-linear adaptive controller that can improve the system performance over a wide range of operating conditions. The effectiveness of FPI-MRAC is assessed by subjecting the system to various disturbances, such as generation variation, load change, changing in uncertain system parameters and occurrence short circuit faults. Additionally, it investigated how quick reaction supercapacitors can improve the dynamic performance of the system. The acquired results show that, for all applied scenarios, the FPI-MRAC offers a much superior dynamic response than PI controller. Using super-capacitors also improves the system frequency when there are disruptions.

Virtual synchronous generator based superconducting magnetic energy storage unit for load frequency control of micro-grid using African vulture optimization algorithm

An isolated microgrid has significant frequency stability issues due to the erratic nature of renewable energy sources, stochastic load behaviour, and low system inertia. Consequently, this paper presents a Virtual Synchronous Generator (VSG) based Superconducting Magnetic Energy Storage (SMES) unit for frequency stability enhancement of microgrid. A newly developed metaheuristic algorithm named African Vulture Optimization Algorithm (AVOA) is used to optimize the proportional-integral controller parameters with the help of the Integral Time Absolute Error criterion. The proposed Load Frequency Control (LFC) model of an isolated microgrid is evaluated against numerous scenarios and well-known algorithms like Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization, and Harris Hawks Optimization. The MATLAB simulation results of the proposed system demonstrate a significant improvement in the dynamic response pertaining to maximum overshoot, settling duration, and frequency oscillations when contrasted to the other algorithms. Also, observed that the LFC system with VSG-based SMES unit improves the frequency response of 77.37 % and 86.56 % in peak overshoot and settling time respectively as compared to the system without energy storage units. Finally, real-time simulations are carried out in OPAL-RT (OP-4500) to validate the proposed system.

Adaptive frequency control in smart microgrid using controlled loads supported by real-time implementation.

The operation of the system's frequency can be strongly impacted by load change, solar irradiation, wind disturbance, and system parametric uncertainty. In this paper, the application of an adaptive controller based on a hybrid Jaya-Balloon optimizer (JBO) for frequency oscillation mitigation in a single area smart μG system is studied. The proposed adaptive control approach is applied to control the flexible loads such as HPs and EVs by using the JBO which efficiently controls the system frequency. The suggested technique uses the power balance equation to provide a dynamic output feedback controller. The main target is to regulate the frequency and power of an islanded single area μG powered by a PV and a diesel generator with integrations of smart bidirectional loads (HPs and EVs) that are controlled by the proposed adaptive controller in presence of electrical random loads. Moreover, the JBO is designed to minimize the effect of the system load disturbance and parameter variations. For a better assessment, the proposed controller using JBO technique is compared with two other methods which are the coefficient diagram method (CDM) and adaptive one using classical the Jaya technique. In the obtained results, the frequency deviation is found as 0.0015 Hz, which is fully acceptable and in the range of the IEEE standards. The MATLAB simulation results reveal that the suggested technique has a substantial advantage over other techniques in terms of frequency stability in the face of concurrent disturbances and parameter uncertainties. The real-time simulation tests are presented using a dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card and confirmed the MATLAB simulation results.

An innovative LFC scheme for multi-area microgrid incorporating with hydrogen-based demand response mechanism

Due to the unpredictability of renewable energy sources (RESs) in Microgrids (MG), the Load Frequency Control (LFC) mechanism is crucial for frequency management of these systems. Frequency control in MGs can be made more cost-effective with the help of demand-side management by temporarily boosting or decreasing power usage, in addition to Automatic Generation Control (AGC) techniques. Hydrogen can provide considerable benefits to MGs since it can be used in two ways: as a fuel cell for electricity generation or as an electrolyzer for power consumption, satisfying the needs of AGC and demand response (DR) simultaneously utilizing Hydrogen Energy Storage (HES) system. This research proposes an MG structure that integrates appropriate energy storage technologies, is able to provide both energy and waste management, and enables use of a variety of RES (including solar, wind, wastewater, agricultural, and residential wastes). With the newly announced Improved-Salp Swarm Algorithm (ISSA), this research aims to design a novel double-input interval type-2 fuzzy fractional order proportional-integral-derivative PI + TID (DIT2-FOPI + TID) cascade controller for LFC of multi-area MGs considering DR with a broad range of Distributed energy resources (DER). In all case studies, the proposed controller demonstrates outstanding effectiveness within the setting of LFC of MGs and, especially by employing DRM approaches, boosts frequency response performance by up to 65%. The results indicate that the dynamic frequency response of the MG is significantly enhanced by the addition of DRM capability with the HES system.

A Coordinated Frequency Control in Microgrid Based on Asymmetric Synthetic Inertia Considering Rotational Speed of Wind Turbine

AbstractFrequency regulation is an important issue in isolated microgrids with inertia‐less variable renewable energy sources such as wind turbines (WTs) or photovoltaics (PVs) due to the small inertia of the overall system. Synthetic inertia control (SI) using the kinetic energy of WT can provide the microgrid with virtual inertia to mitigate frequency fluctuations. In order to improve the performance of SI, the authors have proposed an asymmetric synthetic inertia control (ASI) in which the virtual moment of inertia is alternating depending on the system frequency and the rate of change of frequency. By increasing the control gain only when the system frequency is moving away from the nominal value, it is expected that ASI contributes to the faster convergence of frequency control. However, another issue is interference between ASI and the rotational speed control for maximum power point tracking of WT. Hence, in this study, a coordinated frequency and rotational speed control strategy was proposed. The rotational speed control can be activated based on a mode‐switching logic when the interference does not occur. The effectiveness of the proposed control strategy was verified by numerical simulations using a microgrid model consisting of diesel engine generators, WTs, a photovoltaic, and a load. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Affine projection‐like optimal control of UIPC in networked microgrids with controllable loads

AbstractThe upcoming generation of power systems would be outlined as a system of systems in which many AC and DC microgrids are interconnected to exchange power in a controlled manner. In this work, a new platform for the interconnection of multiple microgrids using a unified interphase power controller (UIPC) is presented. The networked microgrids operate in an islanded mode where voltage control in DC microgrids, and voltage and frequency control in AC microgrids are demanding. Therefore, we develop a new structure for the UIPC according to a current‐controlled four‐leg power converter configuration which can satisfy voltage and frequency support at the AC side. Due to many power and voltage oscillations, UIPC control is challenging in this heterogeneous environment. Therefore, the UIPC is equipped with a new affine projection‐like optimal control (APLOC) strategy which enables the microgrids to safely exchange power when necessary. Further, each AC or DC microgrid contains a new controllable load model which is commanded by the UIPC according to the power management paradigm. Simulation results proved the sufficiency of the proposed networked microgrids model in both controlling the exchanged power among microgrids and improving power quality problems.

Real time frequency stabilization of islanded multi-microgrid

The paper represents a hybrid power system consisting of solar, wind, and Battery sources. The intermittent characteristics of the power system manage the power balance among the generations and load demands. Under these conditions, the system faces high instability. It addresses well-structured PID controllers for the load frequency control in a standalone hybrid microgrid for this problem. The proposed PID controllers offer superior stability. Each Microgrid incorporates the self Maximum Power Point Tracking (MPPT) algorithm to validate the existing microgrids. The test bed has been validated in real-time in Software in Loop (SIL) depending on OPAL-RT 4500 tool.

A linear active disturbance rejection control technique for frequency control of networked microgrids

A linear active disturbance rejection control technique for frequency control of networked microgrids

Energy-Based Cooperative Distributed MPC for Frequency Control in Microgrids

Energy-Based Cooperative Distributed MPC for Frequency Control in Microgrids

Resilient Load Frequency Control of Islanded AC Microgrids Under Concurrent False Data Injection and Denial-of-Service Attacks

Due to malicious cyber attacks, the frequency regulation of an islanded microgrid (MG) with load changes and wind/solar power fluctuations may not be guaranteed and the overall system may even be destabilized. The MG frequency control thus faces new challenges. In response to these challenges, this paper addresses a resilient load frequency control (LFC) problem for islanded AC-MGs under simultaneous false data injection (FDI) attacks and denial-of-service (DoS) attacks. Toward this aim, a new piecewise observer is constructed to provide the real-time estimates of the unavailable system state and the unknown FDI attack signal. Furthermore, a resilient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{\infty }$ </tex-math></inline-formula> LFC scheme is developed to suppress the attack impacts. The novelty of this study lies in the development of an attack-parameter-dependent time-varying Lyapunov function approach to achieve stability analysis and resilient observer/controller design against concurrent FDI attacks and intermittent DoS attacks. Specifically, a tractable observer design criterion is first derived such that the estimation error is exponentially stable under a specified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{\infty }$ </tex-math></inline-formula> performance level. Then a design criterion on the existence of the resilient controller is presented to guarantee the exponential stability of the resulting closed-loop system in the presence of the attacks, while preserving the anticipated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{\infty }$ </tex-math></inline-formula> performance level. Finally, comparative simulation studies in various attack scenarios and different parameter settings are presented to verify the efficiency of the obtained theoretical results.

Comparison between PID and PSO-PID controllers in analysing the load frequency control in interconnected microgrids in a deregulated environment

Comparison between PID and PSO-PID controllers in analysing the load frequency control in interconnected microgrids in a deregulated environment

Robust trajectory-constrained frequency control for microgrids considering model linearization error

Grid supportive modes integrated within inverter-based resources can improve the frequency response of renewable-rich microgrids. The synthesis of grid supportive modes to guarantee frequency trajectory constraints under a predefined disturbance set is challenging but essential. To tackle this challenge, a numerical optimal control (NOC)-based control synthesis methodology is proposed. Without loss of generality, a wind-diesel fed microgrid is studied, where we aim to design grid supportive functions in the wind turbine. In the control design, linearized models are used, and the linearization-induced errors are quantitatively analyzed by reachability and interval arithmetics and represented in the form of interval uncertainties. Then, the NOC problem can be formulated into a robust mixed-integer linear program. The control structure is strategically configured into two levels to realize online deployment. The proposed control is verified on the modified 33-node microgrid with a full-order three-phase nonlinear model in Simulink. The simulation results show the effectiveness of the proposed control paradigm and the necessity of considering linearization-induced uncertainty.

Survey on microgrids frequency regulation: Modeling and control systems

• A comprehensive review of load frequency control in microgrids. • Classification of various microgrid components, models, and benchmarks. • Survey on the merits and drawbacks of model-based and model-free control strategies. • Highlight of future trends for frequency deviation in the presence of renewable energy resources. The traditional power system structure is constantly changing due to the application of renewable energy sources (RESs) and microgrids (MGs) into the power system network. Due to the variations in demand and renewable generation profiles, load frequency control (LFC) is one of the most important issues in MGs. As a consequence of the relatively low inertia of these systems, LFC is more challenging compared to absence of DERs and MGs. A comprehensive review of the LFC problem in MGs is the primary objective of this paper. The review process for this paper is undertaken from two perspectives. As a first step, different models for MG components and benchmarks for LFC studies have been described, which can ease the researchers' task of finding suitable models. The second step of this review is a discussion of various control strategies, considering the merits and limitations of each. It is helpful to researchers that this comprehensive literature review gives them an overview of previous well-researched papers in this respect and enables them to connect the dots between recent developments, challenges, and future trends in load frequency strategies for MGs.

Retired electric vehicle battery to reduce the load frequency control oscillation in the micro grid system

The potential of a retired electric vehicle battery (REVB) is its capacity to provide backup power supply to the power system grid. This paper proposed energy storage system (ESS) based on REVB called retired battery energy storage system (retired BESS) to tackle the intermittent of renewable energy source such as wind turbine and dynamic load change. To examine the efficacy of the proposed technique, the load frequency control (LFC) of microgrid (MG) system is utilized in this study and the proposed technique is compared to conventional LFC controller, PI controllers, superconducting magnetic energy storage (SMES), and a new electric vehicle battery. The kind of retired BESS cell used in this study is Li-ion nickel manganese cobalt oxide (NMC) type with a state of charge as of 70%. The capacity of each cell for retired BESS is 38 Ah. From the simulation result, the use of retired BESS can reduce frequency oscillation, compress the settling time to reach steady state, and maintain the robustness of the MG system. A retired BESS has a minimum error performance index value compared to conventional LFC, proportional integral (PI) controller, and SMES.

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.

A load frequency coordinated control strategy for multimicrogrids with V2G based on improved MA-DDPG

The capacity of a single microgrid is limited, so the multimicrogrid system (MMS) has been widely used to improve the reliability and stability. And the mobile energy storage unit electric vehicles (EVs) with vehicle-to-grid (V2G) capability can improve the flexibility of the microgrid frequency control. Considering the complex structure of the MMS, a load frequency control (LFC) strategy for multimicrogrids with V2G based on improved multiagent deep deterministic policy gradient (MA-DDPG) is proposed in this paper. Firstly, a multimicrogrid LFC model including distributed power generation, micro gas turbine (MT), EVs which consider the random behavior of EV users and the mobility of EVs as dynamic limitations is constructed, the contact relationship between the submicrogrid controllers is added. Secondly, the MA-DDPG algorithm is improved to significantly reduce transmission cost and computational complexity. Additionally, the state, action space and reward function of the multiagent controller is designed. Finally, the simulation results show that, compared with PID, Fuzzy and traditional MA-DDPG control, the improved MA-DDPG can ensure the overall stability of MMS through cooperative control in the face of various complex operating scenarios. And under the premise of ensuring the control effect, the loss caused by transmission is significantly reduced.

Power Sharing and Frequency Control in Inverter-based Microgrids

In the recent period, developed countries have resorted to using smart grids more widely as a solution to the problems of shortage of electric power, because it is the most environmentally friendly due to its reliance on renewable energy sources and does not require expensive maintenance due to the proximity of its stations to the feeding areas. However, these networks faced problems in sharing the required power between the inverters of the user stations, as well as regulating their frequency when connected to the main network to fill the shortage of electrical energy. This paper aims to achieve the actual sharing of power (Active and Reactive) supply to the varying load between the smart grid inverters by designing a PID (Proportional, Integral, Derivative) controller. PID controller gain tuning by PSO (particle swarm optimization) algorithm which is based on PWM (Pulse Width Modulation) scaler that feeds the Microgrid inverter switches. Frequency control can be achieved when connecting Microgrid with the main grid by using PLL (Phase Locked Loop) to generate a controlled reference signal for the PID controller.