Frequency Support with Wind Turbines: a Constant Deloading Approach

Purpose of the Study. The study aims to investigate various approaches to mitigating the impact of renewable energy sources on the stability of the power system and to determine the optimal combination of conventional and renewable energy sources to ensure stable and reliable energy supply. The effectiveness of different approaches to minimizing the impact of renewable energy on grid stability is evaluated. Materials and Methods. Methods of eigenvalue analysis and transient process modeling were used. Correlation and regression analysis methods were used to assess the impact of renewable energy sources (RES) on the stability of the energy system. Data from the system operator «Ukrenergo» were used, including power system load schedules (hourly, daily, seasonal). Data from the Ukrainian Hydrometeorological Center for the years 2023–2024 were used to model RES generation: data on solar radiation (hourly, daily) and wind speed (hourly, daily). Technical specifications of solar panels and wind turbines installed in Ukraine were also used. The QGIS software was utilized for geoinformation analysis. The study employed artificial neural network theory, optimization methods based on linear programming and logical production rules, as well as methods for assessing and regulating steady-state operating conditions of the system, taking into account energy storage devices. Results. The scientific novelty lies in the development of an innovative method for assessing the reduction of the impact of renewable energy sources on power system stability. The feasibility of using artificial neural networks to forecast wind and solar power generation was demonstrated, as well as the optimization of the model structure and parameters to improve forecasting accuracy. The optimal combination of conventional and renewable energy sources was determined to ensure stable and reliable operation of the power system. Practical Significance. Based on experimental and theoretical research, specific recommendations and solutions are provided aimed at ensuring the stability of the power system during the integration of renewable energy sources, including optimization of management, use of new technologies, and infrastructure development

Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time

Due to a large number of grid-connected wind turbines don't have the ability to respond to system frequency changes, problems on frequency stability of power system have become increasingly prominent. At present, the primary frequency regulation of wind turbines is mostly based on the method of additional frequency regulation control to change the reference value of electromagnetic power, so as to realize that wind turbines can participate in the system frequency regulation. However, the method of additional frequency regulation control does not t consider the conflict of power increment between the power tracking controller and the frequency regulation controller, which lead to the power increment required for the primary frequency regulation can't reach the target value. Based on this issue, the paper combines the original power tracking technology of wind turbines with the existing frequency regulation technology, and change the signal of frequency deviation by the power tracking curve of the wind turbine, which realize the inertial response and primary frequency regulation of the unit through the fast active power control of the wind turbine. That the purpose of frequency regulation is realized and the power tracking curve is changed, by converting the active output required for the frequency regulation control into the increment load reduction. The simulation results show that, based on the control method in the paper, the double-fed wind turbine can fully achieve the target increment needed for primary frequency regulation, which makes the wind turbines more reliable and effective to participate in system of frequency regulation.

This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s.

Voltage source converters (VSCs) have emerged as the key components in modern power systems, facilitating efficient energy conversion and flexible power flow control. Understanding the fundamental circuit model of VSCs is essential for their accurate modeling and analysis in power system studies. A basic voltage source converter circuit model connected to an LC filter is essential because it lowers the harmonic distortions and enhances the overall power quality of the micro-grid. This guarantees a clean and steady power supply, which is necessary for the integration of multiple renewable energy sources and sensitive loads. A comprehensive methodology for developing a basic circuit model of VSCs, focusing on the key components and principals involved, is presented in this paper. The methodology includes the modeling of space vector pulse-width modulation (SVPWM) as well as the direct quadrature zero synchronous reference frame. Different design controls, including the design of current control loop in the S-domain, the design of the direct current (DC) bus voltage control loop in the S-domain, and the design of the alternating current (AC) voltage control loop in the S-domain, are explored to capture the dynamic behavior and control strategies of VSCs accurately. The proposed methodology provides a systematic framework for modeling VSCs, enabling engineers and researchers to analyze their performance and assess their impact on power system stability and operation. Future studies can be conducted by using case studies and simulation scenarios to show the efficiency and applicability of the developed models in analyzing VSC-based power electronics applications, including high-voltage direct current (HVDC) transmission systems and flexible alternating current transmission systems (FACTS). The significance of this work lies in its potential to advance the understanding and application of VSCs, contributing to more resilient and efficient power systems. By providing a solid foundation for future research and development, this study supports the ongoing integration of renewable energy sources and the advancement of modern electrical infrastructure.

Stochastic flexible transmission operation for coordinated integration of plug-in electric vehicles and renewable energy sources

Large-scale integration of renewable energy sources (RES) is raising emerging issues of power system frequency stability. The lack of inertia in the use of power electronic converters (PEC) leads to a high rate of change of grid frequency (RoCoF) during load-generation imbalance conditions, which implies a systematic risk of cascading trips. This emerging issue has raised the risk of unexpected low-frequency load shedding and major power interruption events. Operators are thus forced to limit the share of renewable energy, but this measure will severely hinder the penetration of renewable energy in the future power system. Aiming to improve the friendliness of large-scale RES integration, the PEC, as the connection interface to the grid, is supposed to provide inertial support to enhance power system stability. This paper will propose a novel inertial simulation scheme (IES), including the energy topology design and the corresponding grid-forming control strategy, to imitate the dynamic behaviour of an equivalent synchronous generator (SG). When the PEC in the entire power system performs similar inertial characteristics as SG, the system frequency stability will be substantially improved, thus further increase in RES participation can be achieved.

The growing integration of renewable energy sources and electric vehicles (EVs) into grid systems poses serious challenges to energy forecasting, power management, and system stability. The conventional energy management frameworks fall short in responding to the uncertainty of renewables and dynamic load behavior of EVs. In this context, the current research suggests a novel hybridized energy management and power forecasting scheme to enhance grid resilience, sustainability, and efficiency. A two‐way communication model is established to facilitate real‐time electricity price and power coordination among solar photovoltaic (PV), wind turbines, energy storage devices, EVs, and industrial loads. The new kernel‐based nonparametric energy mode optimizer model, which is trained on historical energy and weather patterns, strongly enhances the accuracy of power forecasting and system response. Experimental outcomes confirm peak PV generation of 17 kW at noon with inverter output of 16 kW, and voltage across microgrids (MG1: 0.94–1.06 V, MG2: 0.90–1.025 V) within the stable range. Battery state of charge varies between 180 and 200%, providing stable energy supply and load balancing. This research fills core energy forecasting and grid interfacing gaps and provides a scalable solution for future smart grid and EV‐integrated renewable systems.

Wind turbines (WTs) participate in frequency regulation, which is one of the means to solve the problem of inadequate regulation capacity in power systems with a high proportion of renewable energy. The doubly fed induction generator (DFIG) can reserve part of power to achieve bidirectional regulation capability through rotor over-speed and increasing pitch angle. In this paper, it is pointed out that the available bidirectional regulation power of the WT is constrained by the maximum regulation power under the rotor speed regulation. The regulation power constraints under the pitch regulation considering the time scale are calculated. The adjustment coefficient of WT participating in frequency regulation is designed. Considering the regulation power constraints, the frequency difference interval in which the WT can provide the regulation power according to the adjustment coefficient is analyzed. The rotor speed and pitch coordinated control strategy of DFIG with different wind speeds is designed. Based on 24-hour measured data from a wind farm, the power constraints and their effects of WTs in the wind farm participating in frequency regulation are verified by simulation. The regulation power of the wind farm, frequency quality, and wind power utilization under the different control strategies are analyzed. The results show that the effects of bidirectional power constraints must be taken into account when evaluating the effectiveness of WTs in continuous frequency regulation.

Due to the increasing penetration of wind power, operating characteristic of wind turbines may cause great concerns to frequency stability of the power system with large-scale wind power integration. Wind power plant participation in frequency regulation and the coordination with stored energy of the power system will be a trend in the future. However, the traditional wind turbines may not have the capability of frequency regulation. The issue of how wind power plants can participate in frequency regulation becomes research hotspot in recent years. Currently, most of frequency control methods of wind turbines are designed to have a new control loop from the existing control modules, such as speed control, droop control and pitch angle control. Theses novel control methods could realize the frequency support in some cases. However, these single-functional control loops have some drawbacks and could not solve the issues of frequency control effectively. For instance, the speed control could only work under the rated wind speed and the pitch control may not have fast-speed response ability to support the frequency control when the system frequency starts to fall. In this paper, a comprehensive review is presented regarding the latest studies about the multi-controller method of variable speed wind turbines in order to solve the problems of primary and secondary frequency control.

Energy storage (ES) has a flexible regulation performance to improve the frequency stability of the wind turbine system. However, the doubly-fed induction generator (DFIG) has different frequency regulation capabilities operating at different wind speeds, and how to collaborate the operation of DFIG with ES for frequency regulation is a very critical problem. A combined wind-storage frequency regulation control strategy considering full wind speed is proposed in this paper. Firstly, the equivalent model of the combined wind-storage system is established, and the virtual inertia control of DFIG and the droop control of ES are analyzed for the system frequency regulation. Secondly, propose the combined wind-storage frequency regulation control strategy based on the operation characteristics of the DFIG in different wind speed zones. At low wind speed, the frequency regulation capability of DFIG is fully considered, and ES is allowed to start first for frequency regulation. Improving frequency fluctuations at medium wind speeds using a combined wind-storage system operating in concert. In order to allow DFIG to operate safely at high wind speeds only frequency regulation is performed by ES. Finally, the effectiveness of the proposed strategy is verified by simulating the sudden increase of system load under different wind speed zones on MATLAB/Simulink platform. The results indicate that the strategy has better frequency regulation effect and effectively improves the stability of the system frequency.

This article addresses the complexities brought about by the increased integration of renewable energy sources into power networks, a trend that can lead to system degradation and instability. In response, this study advocates for the adoption of a Demand Response (DR) approach as a pivotal component in ensuring the future reliability of the electric power system. To facilitate frequency regulation in an interconnected hybrid power system, a modified two-degree-of-freedom (fractional order proportional integral)-tilt derivative [TDOF (FOPI)-TD] controller has been developed. The controller’s parameters are finely tuned through the application of Quasi Opposition-based Harris Hawks Optimization (QOHHO), a method proven to outperform other optimization algorithms. The findings demonstrate a significant enhancement in system frequency stability with the implementation of the QOHHO-based controller, even when factoring in uncertainties, physical constraints, and high penetration of renewable energy sources. Additionally, an evaluation of variations in system frequencies and tie-line power adjustments reveals minimal deviations with DR, measuring at −3.59E–05, −1.89E–04, and 9.26E–05, respectively. In stark contrast, in the absence of DR implementation, the deviations are notably higher, recorded at −1.31E–04, −2.73E–04, and 1.73E–04, respectively. Furthermore, a real-time assessment conducted on the OPAL-RT OP4510 platform validates the proposed strategy’s applicability under source and load intermittency conditions. This substantiates its effectiveness in real-world scenarios.

For power quality enhancement, voltage, frequency, and power flow between control areas in an interconnected power system are required to be maintained. Hence, the voltage and frequency should be continuously controlled to maintain the balance of real and reactive power during load variation. Integration of renewable energy sources (RES) in the power system leads to inertia losses and mismatches between load demand and generation capacity; hence, the load frequency controller should overcome these challenges.In this paper, a Dandelion optimizer (DO)-based tilted-integral-derivative (TID) controller is introduced for load frequency control (LFC) of a single-area power system considering renwables pentration. A single-area power system with both solar and wind power plants as renewables and reheat-thermal, hydraulic, and gas power plants as conventional power plants is modeled using the Matlab toolbox. The controller-based DO optmizer is tested under different loading variations to verify its robustness. The sperm swarm and black widow optimization algorithms are compared with the introduced algorithm to verify its superior dynamic responses for loading variation.

The increasing integration of renewable energy sources (RESs), such as wind turbines and photovoltaic (PV) arrays, introduces low-inertia characteristics that negatively affect the dynamic stability of modern power systems. To address this challenge, this study proposes an Adaptive Neuro-Fuzzy Damping Controller (ANFDC) based on a high-voltage direct current (HVDC) link to enhance system damping and stability. The controller employs a fuzzy linguistic rule to tune its parameters, converting dynamic input signals into linguistic variables during an offline training phase. A hybrid microgrid system combining offshore and onshore wind turbines, PV units, and small-scale synchronous generators (SSSGs) is used to train and validate the controller. During real-time operation, the ANFDC adaptively adjusts control signals to mitigate oscillations without relying on an explicit system model. The proposed method effectively integrates the strengths of neural networks and fuzzy logic to achieve fast, robust, and adaptive damping control. Simulation results on a grid-connected hybrid microgrid under various short-circuit faults demonstrate that the ANFDC significantly improves damping performance and maintains high stability margins compared with conventional control approaches. INDEX TERMS: Lowinertia resources (LIRs), high voltage direct current (HVDC), microgrid, wind turbines (WT), photovoltaic arrays (PV), small-scale synchronous generators (SSSGs), adaptive neuro-fuzzy-based damping controller (ANFDC).