Frequency Regulation Control Research Articles

Improved LSTM-Based Load Forecasting Embedded 3DOF (FOPI)-FOPD Controller for Proactive Frequency Regulation in Power System

Improved LSTM-Based Load Forecasting Embedded 3DOF (FOPI)-FOPD Controller for Proactive Frequency Regulation in Power System

A Coordinated Voltage-Frequency Support Method for VSC-MTDC Integrated Offshore Wind Farms System

Voltage and frequency stability is one of the major concerns with the continuously increasing penetration of offshore wind farms (OWFs) integrated into onshore grid via VSC-MTDC system, and it is a challenge to coordinate its low voltage ride-through (LVRT) and frequency regulation (FR) control when a local fault or large disturbance occurs in onshore grid. To address this challenge, a coordinated voltage-frequency support (CVFS) method for the VSC-MTDC integrated OWFs system is proposed in three parts: 1) an adaptive maximum power point tracking (MPPT) strategy is developed for wind turbines (WTs) to satisfy the power demands for LVRT and FR; 2) an active and reactive power quantification strategy, which firstly considers the static load characteristics of nodes nearest the grid side VSC (GSVSC) stations, is proposed for GSVSC stations to support LVRT and FR respectively; 3) an adaptive power allocation strategy, which considers the capacity constraint and the coordination factor with deviation and rate of change of voltage and frequency of different GSVSC stations, is conducted to achieve differential power distribution between LVRT and FR. Extensive test results have validated the effectiveness and performance of the proposed CVFS method.

Intelligent Renewable Energy Agent‐Based Distributed Control Design for Frequency Regulation and Economic Dispatch

The Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug‐and‐play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus‐based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning‐based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug‐and‐play activities of prosumers. Moreover, an optimization‐based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning‐based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning‐based DAI is thoroughly evaluated using the DRES‐based IEEE 14‐bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics.

Optimal Tuning of Virtual Inertia Control for Frequency Regulation of Microgrid

Optimal Tuning of Virtual Inertia Control for Frequency Regulation of Microgrid

A Hybrid Sparrow Search Optimized Fractional Virtual Inertia Control for Frequency Regulation of Multi-Microgrid System

A Hybrid Sparrow Search Optimized Fractional Virtual Inertia Control for Frequency Regulation of Multi-Microgrid System

Improved Gorilla Troops Optimizer-Based Fuzzy PD-(1+PI) Controller for Frequency Regulation of Smart Grid under Symmetry and Cyber Attacks

In a smart grid (SG) system with load uncertainties and the integration of variable solar and wind energies, an effective frequency control strategy is necessary for generation and load balancing. Cyberattacks are emerging threats, and SG systems are typical cyber-attack targets. This work suggests an improved gorilla troops optimizer (iGTO)-based fuzzy PD-(1+PI) (FPD-(1+PI)) structure for the frequency control of an SG system. The SG contains a diesel engine generator (DEG), renewable sources like wind turbine generators(WTGs), solar photovoltaic (PV), and storage elements such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) in conjunction with electric vehicles (EVs). Initially, the dominance of the projected iGTO over the gorilla troops optimizer (GTO) and some recently suggested optimization algorithms are demonstrated by considering benchmark test functions. In the next step, a traditional PID controller is used, and the efficacy of the GTO method is compared with that of the GTO, particle swarm optimization (PSO), and genetic algorithm (GA) methods. In the next stage, the superiority of the proposed FPD-(1+PI) structure over fuzzy PID (FPID) and PID structures is demonstrated under various symmetry operating conditions as well as under different cyberattacks, leading to a denial of service (DoS) and delay in signal transmission.

Minimum-order disturbance observer-based resilient controller for Frequency regulation of hybrid power systems

Minimum-order disturbance observer-based resilient controller for Frequency regulation of hybrid power systems

GWO- and SSA-Tuned PID Control for Frequency Regulation in Multi-Area PowerNetwork Integrated with Plug-in Electric Vehicle

ABSTRACT The Plug-in Electric Vehicles (PEVs) can be influential in containing power system frequency fluctuations. This study, therefore, investigates the efficacy of frequency regulation for PEV-integrated multi-area power network using Grey Wolf Optimizer (GWO) and Salp Swarm Algorithm (SSA) optimized Proportional-Integral-Derivative (PID) control. Instant investigation not only brings out the relative competence of GWO and SSA but also examines the impact of PEV in improving the system performance. The varying operating conditions are realized by subjecting the system to step and random load variations in either or both of the areas. With the proposed control scheme and involvement of PEV, system frequency and tie-line power excursions settle quicker with their peak swings also getting restricted to a lower value, while the oscillations are arrested as well to a great extent. Further, it’s the SSA that shows its superiority over GWO as per the simulation results executed in MATLAB.

Distributed intelligence for consensus-based frequency control of multi-microgrid network with energy storage system

Multi-Microgrids (MMGs) provide a highly promising approach for accommodating diverse distributed energy resources and improving the overall system performance. This enhanced performance encompasses aspects such as reliability, resilience, flexibility, and energy efficiency. As MMGs become more prevalent, the conventional centralized control approach struggles to adequately address the coordinated operation of system. Therefore, this article proposes a leader-follower-based distributed control strategy (DCS) for coordinated frequency control of numerous heterogeneous and geographically dispersed DERs in MMG network. The DCS employs a robust nonlinear fractional-order proportional integral derivative controller for frequency control of MMG. To optimize the parameters of the controller, an adaptive hybrid chaotic atom search-particle swarm optimization (ACAS-PSO)-based distributed intelligent technique is proposed. Initially, the proposed control strategy efficacy has been tested against various single/multidimensional benchmark functions. Then, it is applied to optimize the parameters of the controller for frequency regulation of MMG system by minimizing the integral absolute error (IAE). The findings obtained illustrate that the proposed ACAS-PSO control technique has an IAE improvement of 42.21 % to 65.3 % in MG 1, 55 % to 96 % in MG 2 and 42.2 % to 84.5 % in MG 3 compared to state-of-the-art. Moreover, the proposed method enhances the frequency response in terms of settling time by 23.4 % to 68.7 % in MG 1, 45.2 % to 68.3 % in MG 2 and 36.35 % to 48 % in MG 3, and the control effort is enhanced by 31.2 % to 87.6 % in MG 1, 31.3 % to 87.05 % in MG 2 and 31.25 % to 73.2 %, in MG 3 compared to other techniques presented. In addition, the robustness of the proposed DCS is validated against plug-and-play ability, communication failure, and delays. Further, the proffered controller is validated by implementing in real-time digital simulation using OPAL-RT.

An improved equilibrium optimization-based fuzzy tilted double integral derivative with filter (F-TIDF-2) controller for frequency regulation of an off-grid microgrid

An improved equilibrium optimization-based fuzzy tilted double integral derivative with filter (F-TIDF-2) controller for frequency regulation of an off-grid microgrid

Multi-constrained optimal control of energy storage combined thermal power participating in frequency regulation based on life model of energy storage

The integration of renewable energy into the power grid at a large scale presents challenges for frequency regulation. Balancing the frequency regulation requirements of the system while considering the wear of thermal power units and the life loss of energy storage has become an urgent issue that needs to be addressed. This paper proposes a multi-constrained optimization strategy for coordinating the energy storage combined thermal power frequency regulation (ESCTPFR) control based on the life model of energy storage. Firstly, the paper constructs a multi-dimensional life loss model of energy storage based on charging/discharging times and available capacity. Additionally, a simplified model for the wear of thermal power units is also presented. Based on the fast response time and high response accuracy of energy storage, the frequency regulation loss resistance coefficient of energy storage and thermal power is constructed to improve the enthusiasm of energy storage. Secondly, a two-layer model is proposed to allocate power between thermal power and energy storage, taking into account the frequency regulation cost of the system and State of Charge (SOC) planning. The priority constraints are the system's frequency regulation capacity and the related SOC function with the SOC deviation coefficient used to constrain energy storage power for SOC recovery. Finally, the proposed strategy's cost-effectiveness and effectiveness in reducing frequency regulation loss are validated through simulation experiments based on historical data, along with its ability to recover SOC.

An Optimized PI Damping Control for Frequency Regulation in Low-Inertia Microgrid

An Optimized PI Damping Control for Frequency Regulation in Low-Inertia Microgrid

Frequency and voltage regulation control strategy of Wind Turbine based on supercapacitors under power grid fault

As the penetration rate of wind turbines gradually increases, the fluctuation of system voltage and frequency significantly increases under power grid fault. In severe cases, wind turbines will disconnect from the grid, leading to large-scale power outages. In order to improve the stability of wind turbine grid connection, a large number of literature has studied the frequency and voltage regulation strategies. However, the existing fault ride through control methods mainly solve the problem of reactive power support during faults, while frequency regulation schemes focus on the frequency changes caused by load fluctuations. There is little research on methods that simultaneously consider voltage ride through and frequency regulation under power grid fault. This paper proposes a voltage and frequency regulation control strategy of wind turbines based on supercapacitors to address the above issues. By switching the functional modes of the energy storage device at different stages, wind turbines achieve the fault through and frequency recovery, thereby enhancing the fault tolerance ability. In the strategy, control process is divided into power grid fault period and fault recovery stage, and it adjusts the auxiliary priority level of energy storage devices based on the electrical response degree of different stages. During the fault, energy storage device is in voltage regulation stage because the voltage drop degree is greater than the frequency fluctuation, and it can ensure the wind turbine does not run off the grid by providing dynamic reactive power support to power grid. In the fault recovery stage, energy storage device is in the frequency regulation stage because of the large frequency deviation, it output active power to accelerate the system frequency recovery rating. Finally, the effectiveness of the control strategy proposed in this paper is verified through simulation.

Fuzzy model predictive control for frequency regulation of temporary microgrids during load restoration

AbstractTemporary microgrids containing a high proportion of distributed renewable generations can offer great benefits during the recovery stage after large‐scale power system blackout, by picking up critical load and speeding up the entire grid recovery process. To solve the frequency security problem of temporary microgrids faced in the load restoration process, this paper proposes an adaptive fuzzy model predictive control (adaptive fuzzy‐MPC) strategy to obtain effective frequency regulation to suit the special characteristics of this process. First, the load restoration plan within a short time frame is regarded as the feedforward information and assists in the establishment of the MPC model. Then, considering the uncertainty of renewable distributed generations, the regulation resources are cooperated by an adaptive fuzzy controller which provides the weights of different regulation resources adopted in MPC. Specifically, the base weights of the energy storage system and renewable distributed generations are obtained from fuzzy rules, and then the weight for each regulation resource is modified by the designed adjustment rules. Simulation results illustrate that the proposed strategy can efficiently improve frequency performance while making various frequency regulation resources with different regulation capacities cooperate properly, which can further accelerate the load restoration progress and enhance system resilience.

Droop-Free Sliding-Mode Control for Active-Power Sharing and Frequency Regulation in Inverter-Based Islanded Microgrids

This paper introduces a simple decentralized sliding-mode (SM) approach to control active power sharing by regulating the local frequency in inverter-based islanded microgrids (MGs). Its sliding surface arises from the frequency correction term introduced in the droop-free technique; it relates local active power to neighboring MGs’ active power by considering available communications among voltage source inverters. Then, this schema allows one to avoid hierarchical control just as the droop-free method does, and the benefits associated are twofold. First, it reduces the steady-state frequency error while providing accurate active power distribution. Second, the system stays reliable, withstands uncertainties, and provides a fast transient response. A Lyapunov analysis confirms stability, and simulations on a realistic four-inverter MG platform substantiate the control scheme’s effectiveness. Its performance regards frequency regulation while achieving active power sharing, stability, and robustness against clock drifts and load steps.

A Stochastic Controller for Primary Frequency Regulation Using ON/OFF Demand Side Resources

A Stochastic Controller for Primary Frequency Regulation Using ON/OFF Demand Side Resources

Nonlinear coordination strategy between renewable energy sources and fuel cells for frequency regulation of hybrid power systems

This study proposes an advanced control strategy for the coordination of an energy storage system (ESS) based on fuel cells (FCs) and renewable energy sources (RESs) to enhance frequency dynamic performance in hybrid power systems (HPSs). The proposed coordination control strategy is based on the nonlinear proportional-integral (NPI) controller, which increases the system's flexibility in dealing with disturbances and changing operating conditions. In addition, it improves the system's dynamic response and attempts to address system weakness caused by highly penetrating RESs. The proposed NPI controller is optimally designed using a new optimization algorithm, called dandelion optimizer (DO), whose proficiency and effectiveness are verified by comparing its performance with other well-known optimization algorithms used in the literature; particle swarm optimization (PSO), grey wolf optimization (GWO), and ant lion optimization (ALO) algorithms considering various standard objective functions. Furthermore, the proposed NPI controller performs better than other control strategies used in the literature under load/RESs fluctuations. The effectiveness of the proposed nonlinear coordination control strategy is examined and investigated through a self-contained HPS that includes a diesel generator, RESs (i.e., photovoltaic and wind power plants), battery ESS, flywheel ESS, aqua electrolyzer for hydrogen production, FCs, electric vehicles, and customer loads. The simulation results carried out by the MATLAB software demonstrate the superior performance of the proposed DO-optimized NPI controller for HPS frequency regulation, even when the power system's parameters have substantial variations. Moreover, the results revealed that the proposed strategy significantly reduces the frequency deviation by approximately 95% compared to the conventional coordination strategy based on the fixed contribution of RESs and by 90% compared to the adaptive coordination control based on the PI controller.

Forward engineering of local load control for primary frequency and voltage regulation

Recently, more frequency and voltage stability issues have been caused by the uncertain load demand and intermittent renewable energy generation in smart grids. This has put significant pressure on turbine governor action. One potential solution is load participation in primary control. In this study, we investigate a general class of local load control (LLC) to provide grid frequency and voltage support while minimizing the pre-defined disutility to utilities and clients. Our approach relies only on local frequency and voltage measurements. We prove that the network dynamics under optimality conditions solve the dual problem of the disutility function adaptively, providing a principled way to guide forward engineering in local load control design. Simulation-based comparative study of choosing different types of disutility functions is conducted on response performance concerning frequency/voltage nadir and steady-state error. Moreover, the effectiveness and rationality of the proposed approach are verified in MATLAB/SIMULINK under various scenarios, including load variations, renewable power output fluctuations, and different penetrations of renewable energy.

Grid integration of wind farms and the interconnection requirements for the U.S. grid

Abstract The integration of wind turbines and wind farms into the power grid presents significant challenges, including the variable and uncertain nature of wind power, the need for efficient transmission and distribution of power, and the need for advanced control and monitoring systems. Recent advances in wind turbine and wind farm technology have greatly improved their ability to integrate into the power grid. This includes the use of variable speed generators and power electronics, such as inverters, which allow for more accurate control of the power output and better alignment with the grid’s frequency. Additionally, advanced control algorithms, such as model predictive control and feedback linearization, have been developed to improve the stability and reliability of wind power systems. Communication systems have also been integrated into wind power systems to allow for real-time monitoring and control of the wind farm’s performance. This improves the ability of the wind farm to provide ancillary services, such as frequency regulation and voltage control. In this work, the major interconnection requirements for the U.S. grid for the expected mass integration of wind farms were discussed focusing on sustainable and reliable control strategies. The paper explores various control schemes that are used for providing response for grid reliability and sustainability. The analysis also included the future steps that OEMs of wind turbines need to take and the regulatory framework that is needed for ancillary services to provide an equitable platform for all generating units – conventional and renewables.

Research on wind-storage coordinated frequency regulation strategy of high permeability wind power connected to regional power grid

With the increasing penetration of wind power in the power system, the proportion of wind turbines in the power system is increasing, replacing the traditional units, which reduces the inertia constant of the system and the reserve capacity of frequency regulation. This paper analyzes several schemes of wind power participating in system frequency regulation, and summarizes a coordinated frequency regulation control strategy of wind power and storage. The main purpose is to make full use of the inertia of the wind turbine rotor to reduce the allocation of energy storage capacity and effectively reduce the energy storage capacity.