System Frequency Fluctuation Research Articles

Inertia–Active Power Filter Design Based on Repetitive Control

The advent of distributed generation has brought with it a plethora of challenges for the nascent power systems that are being deployed on a large scale. Firstly, the majority of power electronic converters connected to the grid are in current source mode, which results in a lack of inertia and an inability to provide effective inertia or achieve damping support during fluctuations in grid frequency. Secondly, the issue of power quality, caused by the presence of harmonics, is becoming increasingly severe. This is particularly problematic in microgrids or systems with high line impedance, where harmonics can be amplified, thereby further compromising the stability of the power system. To address the deficiency in system inertia, numerous scholars are currently utilizing grid-forming (GFM) technology to achieve virtual inertia. In order to address the issue of system harmonics, it is possible to install active power filter (APF) devices at the point of common coupling (PCC), which serve to mitigate the effects of harmonics. This paper puts forth a proposal for the implementation of an APF with virtual inertia, based on PR + RC composite control. This composite control mechanism serves to enhance the harmonic suppression capabilities of the APF. The introduction of a frequency droop enables the capacitor voltage amplitude to be adjusted during fluctuations in system frequency, thereby achieving virtual inertia and providing active support for system frequency. The experimental results demonstrate that this strategy not only reduces the total harmonic distortion (THD) by 13% in comparison to PI control, indicating excellent harmonic suppression performance, but also allows the system to be inert, achieving positive results in suppressing frequency fluctuations during transients.

Centralized control of large‐scale wind farm for system frequency stabilization of the power system

AbstractIn recent years, wind power generation has been promoted as a countermeasure to global environmental problems. However, the introduction of many wind power generators into the power system may cause system frequency fluctuations. This paper proposes a control method to reduce system frequency fluctuations by using the rotor kinetic energy (RKE) of a variable speed wind turbine (VSWT). When the frequency falls, this method suppresses fluctuations by consuming RKE to increase VSWT power generation, and when it rises, it decreases VSWT power generation by accumulating RKE. The effectiveness of this method is confirmed through simulations using PSCAD/EMTDC.

Decentralized control strategy of thermostatically controlled loads considering the energy efficiency ratio and measurement error correction

The elimination of traditional thermal generators and the increase of renewable energy source access reduced the inertia of the power system, and thermostatically controlled loads have the potential to provide ancillary services to the power system. This paper introduces a decentralized control strategy for thermostatically controlled loads (TCLs), which focuses on assisting large-scale TCLs in providing frequency support to power systems while considering energy efficiency ratio and measurement error correction. Initially, the nonlinear relationship between heat flow and TCLs’ power is modeled using a polynomial relationship, and the coefficients are determined through a data-driven method to enhance accuracy in TCLs modeling and assess their regulation potential precisely. Subsequently, a decentralized control strategy tailored for power system frequency regulation is developed. To account for measurement errors in TCLs controllers, a measurement error back-correction method is proposed. Simulation examples demonstrate the effectiveness of the proposed control strategy in achieving accurate modeling and control of TCLs and reducing power system frequency fluctuations.

Heuristic Optimization of PV Energy Penetration to Resilience System Frequency Fluctuation

Heuristic Optimization of PV Energy Penetration to Resilience System Frequency Fluctuation

Frequency stability control method for multi-energy system considering storage characteristics and transmission inertia of hot and gas pipeline network

The grid-connected capacity of renewable energy generation in multi-energy microgrid is increasing. This leads to a decrease in the inertia level in the microgrid, which has a great impact on the frequency stabilization control. This article proposes an adaptive control method for frequency control of inertia in multi-energy microgrids. Firstly, the system frequency fluctuation problem is addressed. Analyze the response characteristics of virtual inertia and the influence of physical inertia of rotating equipment on system frequency dynamics in multi-energy microgrid. Secondly, study the energy transfer characteristics of electric, thermal and gas systems in multi-energy microgrids. The energy coupling model between the subsystems in the multi-energy microgrid is established. And according to the difference of energy transmission time inertia of electric, heat and gas subsystems, the microgrid inertia response time model of different energy systems is established. Then, according to the energy balance stability criterion of multi-energy microgrid, combined with the current operating state of the system and the state of the higher-level distribution network, the fast response adaptive over-compensation control of multi-energy microgrid cluster is carried out to realize the inertia allocation in multi-energy microgrids. Finally, the proposed advanced frequency control method of multi-energy microgrid considering inertia demand is verified by simulation.

Control Strategy of Seamless Switching Between Grid-connected and Islanding Operation for Weak-connected Microgrid

A control strategy of seamless switching is proposed for the high-capacity microgrid, which is at the end of long-distance transmission. Firstly, the control modes of energy storage and diesel generator set under grid-connected conditions and islanding conditions are analyzed. Meanwhile, the seamless switching strategy for the microgrid, in which energy storage or diesel generator sets can be taken as the main power source adaptively, is proposed. During the switching from the grid-connected condition to the islanding condition, the connection line power is optimized according to the respective operational characteristics of the energy storage and diesel generator sets. And the interaction of the power step control with voltage blocking and the regulation characteristics of the line voltage regulator is utilized to effectively suppress the fluctuation of system voltage and frequency. Finally, the combination of auto-synchronization control and multiple synchronization control improves the success rate of switching from the islanding condition to the grid-connected condition. The on-site test results of an actual microgrid project verify the effectiveness of the proposed control strategy.

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.

A novel honey badger algorithm based load frequency controller design of a two-area system with renewable energy sources

When it comes to the process of ensuring the stability, quality, and reliability of a power system, one of the most crucial components is known as the load frequency controller (LFC). It does this by ensuring that there is a balance between the amount of power that is produced and the amount that is consumed. This paper proposes a novel evolutionary approach, referred to as the Honey Badger Algorithm (HBA), PI/PID controllers should be configured in the best possible way in order to address the LFC problem in the electrical power system. The research takes into account a power system that is integrated between two areas and uses renewable energy sources, such as a wind system and a solar system. The utilization of renewable energy sources has the potential to yield favorable outcomes in frequency control through the provision of prompt and adaptable responses to fluctuations in system frequency, helping to maintain grid stability. The proposed HBA method is utilized to refine the controller parameter values, using a fitness function anchored on the integral of absolute error (IAE) and the integral time multiplied by absolute error (ITAE). The performance of the proposed HBA-based controller has evaluated under 5% step load perturbation (SLP) in area-1. The HBA-based controllers demonstrate greater performance in terms of settling time, overshoot, and fitness value when compared to other well-known optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimization (GWO). According to the obtained results, the IAE-based PID controller has the best performance. The HBA-based PID controller is evaluated according to the following performance criteria; the objective function value is 0.4201, the settling time values and overshoot values for the area-1, area-2 and tie-line are 15.6, 33.7 and 27.9 s and −6.6, −0.7 and −0.0071 Hz, respectively. According to the findings, the HBA is both a dependable and effective tool for finding solutions to LFC research problems in multi-source power systems.

Research on frequency modulation capacity configuration and control strategy of multiple energy storage auxiliary thermal power unit

The rapid development of new energy sources has had an enormous impact on the existing power grid structure to support the “dual carbon” goal and the construction of a new type of power system, make thermal power units better cope with the impact on the original grid structure under the background of the rapid development of new energy sources, promote the consumption of new energy sources such as solar and wind energy, and further expand the channels for upgrading and flexibility transformation of thermal power energy storage, improve the stability, safety and economy of thermal power unit operation. Therefore, based on the research status at home and abroad, a hybrid energy storage system is added to solve the above problems. The lithium battery-flywheel control strategy and the regional dynamic primary frequency modulation model of thermal power units are proposed, and study the capacity configuration scheme of flywheel‑lithium battery hybrid energy storage system under a certain energy storage capacity, the frequency modulation performance is evaluated by the system frequency fluctuation degree, fluctuation peak range and other indicators. Study under a certain energy storage capacity thermal power unit coupling hybrid energy storage system to participate in a frequency modulation of the optimal capacity configuration scheme, and perform simulation verification using MATLAB/Simulink. When the thermal power unit is coupled with a 10.8612 MW/2.7151 MWh flywheel energy storage system and a 4.1378 MW/16.5491 MWh lithium battery energy storage system, while adaptive variable coefficient droop control is adopted, the system frequency range is 0.00328 p.u.Hz, and the fluctuation degree of the output power of the thermal power units is 0.00498 p.u.MW, compared to the individual frequency regulation of thermal power units, it reduces 52.07 % and. The results show that when the thermal power unit is disturbed by external load, hybrid energy storage assisted thermal power unit frequency modulation reduces the mechanical loss of thermal power unit to a certain extent and extends the service life of the unit, effectively improve the operation stability and economy of thermal power units, further reduce the phenomenon of abandoning wind and light, and better promote the utilization of new energy.

Sequential frequency regulation strategy for DFIG and battery energy storage system considering artificial deadbands

To address the issues of the mechanical stress of doubly-fed induction generator (DFIG) and the service life of energy storage systems (ESSs) resulting from excessively and frequently participate in grid frequency regulation (GFR), this paper suggests a sequential GFR of the DFIG and ESS considering the artificial dead-bands. Firstly, the system frequency deviation expression is derived considering the dead-band to analyze the influences of the dead-bands on the grid frequency dynamics. Secondly, based on the artificial dead-bands, the GFR process is divided into three zones: the GFR zone of DFIG, the transaction zone of DFIG and ESS, and the GFR zone of ESS. To avoid the large mechanical stress of the wind turbines and shallow charging and discharging phenomenon of the ESS, the DFIG provides GFR function to mitigate the system frequency fluctuations, while ESS provides frequency regulation function to arrest the grid frequency variation taking into account the condition of state-of-charge constraints for severe frequency disturbances. Finally, simulations are conducted to verify the effectiveness of the proposed strategy under varying wind speed conditions combined with large disturbance. The results show that the proposed sequential GFR can maintain the frequency support while reducing the negative effects of mechanical fatigue of the DFIG and service life of ESS.

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.

Improved current droop control strategy of parallel inverters for microgrid based on negative feedback of current

In order to increase the current distribution accuracy of the system and restrain the system frequency fluctuation on the basis of reducing the control operation complexity, this paper proposes an improved current droop control strategy. The strategy can be described as follows: Firstly, a virtual negative inductance and a virtual resistance are introduced into the voltage and current double closed loop control to balance the output impedance of the inverter unit. Then an impedance ratio is introduced to solve the contradiction between the virtual complex impedance and potential drop. Finally, by introducing new current variables and using these current variables to form the negative feedback to the droop coefficient in the voltage equation in the droop control, the allocation of the active current and reactive current is implemented in terms of the inverter capacity, and the plug and play function of the inverter unit is realized in the low-voltage microgrid. A great number of simulation results show that by the strategy, the active and reactive currents output can be distributed in terms of the own capacities of the inverters in the system, the smooth operation of parallel inverters with the same or different capacities is ensured and the computational complexity is simplified.

Factors affecting forensic electric network frequency matching – A comprehensive study

The power system frequency fluctuations could be captured by digital recordings and extracted to compare with a reference database for forensic timestamp verification. It is known as the Electric Network Frequency (ENF) criterion, enabled by the properties of random fluctuations and intra-grid consistency. In essence, this is a task of matching a short random sequence within a long reference, whose accuracy is mainly concerned with whether this match could be uniquely correct. In this paper, we comprehensively analyze the factors affecting the reliability of ENF matching, including the length of test recording, length of reference, temporal resolution, and Signal-to-Noise Ratio (SNR). For synthetic analysis, we incorporate the first-order AutoRegressive (AR) ENF model and propose an efficient Time-Frequency Domain noisy ENF synthesis method. Then, the reliability analysis schemes for both synthetic and real-world data are respectively proposed. Through a comprehensive study, we quantitatively reveal that while the SNR is an important external factor to determine whether timestamp verification is viable, the length of test recording is the most important inherent factor, followed by the length of reference. However, the temporal resolution has little impact on performance. Finally, a practical workflow of the ENF-based audio timestamp verification system is proposed, incorporating the discovered results.

Frequency Control Approach and Load Forecasting Assessment for Wind Systems

Frequency deviation has to be controlled in power generation units when there are fluctuations in system frequency. With several renewable energy sources, wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature. Whenever there is a mismatch between generation and demand, the frequency deviation may arise from the actual frequency 50 Hz (in India). To mitigate the frequency deviation issue, it is necessary to develop an effective technique for better frequency control in wind energy systems. In this work, heuristic Fuzzy Logic Based Controller (FLC) is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems. Frequency control is applied to reduce the frequency deviation due to fluctuations and load prediction information using ANN (Artificial Neural Network) and SVM (Support Vector Machine) learning models. The performance analysis of the proposed method is done with different machine learning based approaches. The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy. Simulation results show that the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Normalized Mean Absolute Error (NMAE) values are scaled down by 41.1%, 9.9% and 23.1% respectively in the proposed method while comparing with existing wavelet and BPN based approach.

An improved damping adaptive grid‐forming control for black start of permanent magnet synchronous generator wind turbines supported with battery energy storage system

Permanent magnet synchronous generators (PMSGs) equipped with back-to-back grid-forming converters have the ability to form a 100% renewable energy power grid. Black-start is the key capability of grid-forming converters when restoring the system from a blackout. It is necessary for a grid-forming converter of the PMSG to operate in black-start and grid-connected active support modes. This paper investigates an improved damping adaptive grid-forming (DA-GFM) control method for PMSG wind turbines to suppress system frequency fluctuations and achieve a smooth black start. The proposed DA-GFM control introduces the frequency variations and natural constant function to adaptively adjust its damping coefficient in the dynamic operating progress. The implementations and effects of the proposed DA-GFM control method on the grid-forming converter are theoretically evaluated. Additionally, the detailed black-start operation process is completely studied through a microgrid system with two PMSG wind turbines and energy storages. Compared with the conventional grid-forming control, the proposed control scheme can not only suppress frequency fluctuations better, but also achieve a smooth forming power grid process. Finally, experiment tests on a controller-level hardware-in-the-loop simulation platform are carried out to validate the proposed control strategy for achieving a smooth black start.

Research on the Frequency Regulation Strategy of Large-Scale Battery Energy Storage in the Power Grid System

Driven by the carbon peaking and carbon neutrality target, the large-scale grid-connected of renewable energy such as wind and solar has increased, and the volatility and randomness have posed new challenges to the stability of the power grid frequency. In this case, battery energy storage is a grid auxiliary resource with fast response and adjustable parameters, which can provide frequency support for the grid system in a short period. This paper studies the frequency regulation strategy of large-scale battery energy storage in the power grid system from the perspectives of battery energy storage, battery energy storage station, and battery energy storage system, respectively. First of all, the droop control based on logistic function and the virtual inertia control based on piecewise function are proposed for battery energy storage frequency regulation, which improves the performance of battery energy storage power output effectively. Second, the weighting factor is set according to the current battery charge to achieve the most optimal distribution of frequency regulation power for each battery pack in the battery energy storage station. In the end, a control framework for large-scale battery energy storage systems jointly with thermal power units to participate in system frequency regulation is constructed, and the proposed frequency regulation strategy is studied and analyzed in the EPRI-36 node model. The results of the study show that the proposed battery frequency regulation control strategies can quickly respond to system frequency changes at the beginning of grid system frequency fluctuations, which improves the stability of the new power system frequency including battery energy storage. In addition, this paper also provides a certain reference for the construction of the new power system dispatching that integrates “Generation-Grid-Load-Storage” in the future.

Artificial Gorilla Troops Optimizer for Frequency Regulation of Wind Contributed Microgrid System

Abstract The high penetration of renewable energy resources (RESs) based microgrids (MGs) into the modern power system brings severe system frequency fluctuations due to RESs uncertain nature. In such cases, supplying an MG model with an effective load frequency control (LFC) plays a crucial part in regaining the stability of the electrical network. In this work, a wind turbine generator (WTG) and diesel generator (DEG) are efficiently planned as autonomous diesel wind energy-based microgrid (DWMG). A wind-contributed dynamic model, speed regulator, and proportional-integral-derivative (PID) frequency controller are designed to make the WTG system aware of power fluctuations. Additionally, an integral type sliding mode control (I-SMC) is designed to generate the supplementary control action for the frequency regulation against the load and source uncertainties. A recently invented artificial gorilla troops optimizer (GTO) is utilized to obtain the controller parameters. The results reveal the proposed method's benefits, such as least frequency deviations, shorter settling time, and minimum integral errors over state-of-the-art methodologies.

Technical and Economical Evaluation of Micro-Solar PV/Diesel Hybrid Generation System for Small Demand

This paper is intended as an investigation on a reliability of solar PV(Photovoltaic) and DG (Diesel Generator) hybrid system and the economical evaluation. In the remote area or island countries, diesel generator is a common technology for supplying power. In general, the price of diesel oil is expensive in remote areas. Therefore, introduction of the technologies which can reduce the fuel consumption for power generation is important in those area. Interconnection of solar PV with isolated diesel distribution lines is one of the options when expanding power generation facilities. However, the output of solar PV is influenced by the weather condition, it is difficult to ensure a constant output and control power amount. Using unstable input for power generation such as solar PV increases the risk of power outage due to instability of system voltage and frequency fluctuations. In this study, experiments were conducted to clarify the unstable condition using the micro-solar diesel hybrid system using solar PV (2kW), Battery Bank (24V,420Ah) and Diesel Generator (4.7kVA) and load(1500W) at Ashikaga University (AU). The experiments are conducted by two different setups, a hybrid system of solar PV and DG and the hybrid with battery bank. The results of the experiments show the frequency fluctuations becomes smaller by the hybrid system with battery bank. And the mechanical governor which attached to the DG has important function to stabilize frequency fluctuation. In the study, economic viability of the solar PV and DG hybrid system is examined by computing the Internal Rate of Return (IRR). In the calculation of the least-cost alternative system, a diesel engine powered generation system with the capacity to generate the same amount of electricity as the solar PV-DG hybrid system was used. The IRRs of the solar PV – diesel hybrid system is positive in all configuration. IRR becomes larger in the hybrid system without a battery bank and also it becomes larger with increase of the penetration ratio of solar PV. The configuration of solar PV and DG hybrid system have to be considered by the type of power demand. If the demand user requires quality power such as stabilized voltage and frequency in minimum range, battery bank have to be installed to the system. If the economical operation by saving the amount of fuel consumption is more important, battery bank does not need to be included. The system is feasible on the both aspect of technical and economical, therefore it can be introduced as reliable energy supply system for small power demand in remote areas.

Stability Improvement of Power System with a Wind Farm by the Virtual Synchronous Generator Control of Battery

As the introduction of renewable energy sources (RESs) increases, there is a concern that the inertia and synchronizing power of power systems will decrease and the system stability will deteriorate. In particular, if a large-scale wind farm is disconnected from the grid in compliance with the grid code due to a grid fault, the entire grid may become unstable. Under such a background, this paper proposes a storage battery system which has the functions of reactive power control and virtual synchronous generator (VSG) control as well as control of system frequency fluctuations due to fluctuating output from the wind farm. In addition, a new control method is also implemented in the battery control system in order to improve the total charge/discharge efficiency of the battery by suppressing unnecessary charging and discharging. The proposed battery system is composed of multiple batteries installed at different locations and they are controlled coordinately. Effectiveness of the battery system for system stabilization is verified by simulation analyses.

Flexible selection framework for secondary frequency regulation units based on learning optimisation method

Grid connections of new energy sources have increased system frequency fluctuations, which requires secondary frequency regulation(SFR) to ensure the safe and stable operation of the system. SFR balances the power deviation between generation and consumption in real time by changing the setpoints of the units. In this paper, we propose a dynamic area control error (ACE) threshold value-based method to rationalise unit selection for SFR tasks. Each conventional unit or virtual generation unit has an independent threshold to aid dispatchers in determining the response priority of each unit to SFR tasks relative to scheduled tasks. In this manner, power generation units can switch their control mode flexibly and track different setpoints using automatic generation control(AGC) and load control technology. We designed a threshold solver based on a sequential decision framework using the deep reinforcement learning method. The solver can dynamically update thresholds based on the real-time system operational state such that the selected units can better meet the current dispatching needs. In the experiment, we tested the performance of the solver in several typical scenarios; the proposed method was found to be effective in maintaining the frequency stability of the system, with lower operation costs.