Frequency Control Strategy Research Articles

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

A Coordinated Emergency Frequency Control Strategy Based on Output Regulation Approach for an Isolated Industrial Microgrid

Constructing isolated industrial microgrids with wind power is beneficial for improving the economic benefits of high-energy-consuming production, such as the electrolytic aluminum industry. Due to the specialized structure of industrial microgrids and the unique characteristics of the electrolytic aluminum load (EAL), the common emergency frequency control methods do not apply to the specific operational requirements of isolated industrial microgrids. Since EALs have huge regulating capacities and fast responses, this paper proposes a coordinated emergency frequency control scheme to deal with power disturbances in isolated industrial microgrids. The coordinated frequency control model of an industrial microgrid considering demand-side participation is derived. With the help of output regulation theory, a practical, feasible coordinated frequency controller is designed by introducing frequency deviation and power disturbance as feedback control signals. The proposed control scheme achieves reserve power distribution between the generation and demand sides. The microgrid frequency can be maintained within a permitted range in the presence of large power imbalances. The simulation results conducted in an actual isolated industrial microgrid validate the effectiveness and dynamic performance of the proposed control scheme.

Advanced frequency control schemes and technical analysis for large-scale PEM and Alkaline electrolyzer plants in renewable-based power systems

Integrating electrolyzers into power systems can significantly contribute to sustainable energy via the generation of green hydrogen while also enhancing frequency stability through effective regulation of the electrolyzers’ operating power. This study gives a comprehensive analysis of large-scale electrolyzer plants when providing frequency support to power systems. First, the authors present a model predictive control (MPC)-based secondary frequency controller, combined with a droop controller as the primary frequency controller and a virtual inertia controller. Additionally, the study introduces a universal system frequency response (U-SFR) modeling approach that enables high accuracy, low computation burden, and reduced initial parameters as a testbed. Finally, an in-depth analysis is conducted, focusing on different technical aspects of large-scale electrolyzer plants when providing frequency support services. Case studies integrating PEM and Alkaline electrolyzers into the modified IEEE 39-bus system with over 50% wind power penetration are conducted. It is found that the proposed U-SFR model achieves high accuracy with lower computational time compared to detailed physical models. Additionally, model predictive controllers improve frequency quality more effectively than PID and PID-FLC methods. PEM electrolyzers are found to be more efficient in providing grid frequency support than alkaline electrolyzers due to their technical characteristics. Finally, smaller hydrogen tanks may frequently breach storage constraints, negatively impacting the system’s frequency response capability.

Enhanced frequency control of a hybrid microgrid using RANFIS for partially shaded photovoltaic systems under uncertainties

Nowadays environmental concerns and fossil fuel limitations, as well as economic benefits and technical issues such as reliability lead conventional distribution networks to smart microgrids, where the renewable energy resources are merged with the grids. Nevertheless, the systems face new challenges due to the intermittent nature and uncertainties in the distributed generations, which cause frequency fluctuations in the microgrid. The photovoltaic cells are the main part of the contemporary microgrids. Although the photovoltaic (PV) systems depend on solar irradiance, and temperature and are affected by the partial shading phenomenon they could contribute to improving the microgrid frequency stability with a proper control scheme. In this paper, the frequency control strategy is designed for a hybrid stand-alone microgrid, which is robust against load disturbances, variations in weather conditions, and uncertainties in the microgrid parameters. The proposed intelligent control scheme relies on the Recurrent Adaptive Neuro Fuzzy Inference System (RANFIS). The Whale Optimization Algorithm (WOA) is employed to optimize the RANFIS controller structure and generate the parameters of membership functions. In this multi-objective optimization, the objectives are settling time (ST), overshoot (Osh), and Integral Square Error (ISE). The simulation results verify the high robustness and performance of the proposed RANFIS controller, compared to other controllers, during various operational circumstances, as well as the sporadic behavior of renewable energy resources (RES) such as fluctuations in solar radiation and certain uncertainties in the microgrid parameters.

Switching Frequency Control Strategy of Inverter for Multifrequency Multiload WPT System Based on Hysteresis Current Control

Switching Frequency Control Strategy of Inverter for Multifrequency Multiload WPT System Based on Hysteresis Current Control

Coordinated frequency control strategy for modern power system considering engagement willingness

The large scale integration of renewable energy sources (RES) is posing unprecedented challenges to the frequency stability of modern power systems, with system inertia being the primary concern. Under such a landscape, this paper introduces a coordinated control strategy aiming at enhancing frequency stability by swiftly adjusting the power output of flexible resources to mitigate power imbalances and further dampen frequency fluctuations. Firstly, a communication-free center of inertia (COI) frequency estimation method is proposed. An equivalent inertia estimation method tailored for modern power systems is then developed by exploiting the correlation between synchronous inertia and active power fluctuations. By integrating the COI frequency variation rate with the estimated equivalent inertia, the imbalance power is calculated and compensated through the proposed coordinated control strategy. In this work, two typical flexible resources including photovoltaic (PV) generation and electric vehicles (EVs) are modeled and integrated into the testing system in PSCAD/EMTDC. Considering the contradiction between maximizing generation profit and reducing power output to mitigate frequency variation, the Corporate Social Responsibility (CSR) of renewable Generation Companies (GENCOs) is modeled to sketch their willingness of engaging into frequency support. Moreover, the frequency modulation potential of EVs in both commercial and residential areas are quantified for practical implementation. The simulations results successfully validate the effectiveness of the proposed control strategy.

A novel hybrid LFC scheme for multi-area interconnected power systems considering coupling attenuation

In this paper, a hybrid load frequency control (LFC) scheme is proposed for multi-area interconnected power systems to decouple the intricate double control objectives, by dividing all subareas into the responsible areas and the free areas. The LFC in the responsible area has the function of regulating both the local frequency and the tie-line power, while the control objective of the LFC in the free area is thus simplified to regulate the local frequency only. Then, addressing the complex network coupling and uncertain dynamics, an integrated LFC controller is proposed for the free areas, which consists of two parts, namely, the coupling attenuation baseline controller and the disturbance compensation controller. The coupling attenuation baseline controller satisfying the predefined bounded L2-Gain condition is derived based on the solution to a multi-player zero-sum differential game. Additionally, a novel generalized integral observer is designed to estimate the system’s integrated disturbance, and the corresponding disturbance compensation controller is derived. After that, the ultimately uniformly bounded (UUB) stability of the integrated LFC controller combining baseline controller and disturbance compensation controller is proven rigorously. Finally, the performance superiority of the proposed hybrid LFC scheme is validated by the simulations in challenging operating modes.

Study on photovoltaic primary frequency control strategy at different time scales

AbstractDuring the participation of photovoltaics in grid frequency regulation, different frequency regulation tasks are required at different time scales. The grid demands that photovoltaics (PVs) improve steady‐state frequency when facing short‐term load fluctuations, while also enhancing frequency response to long‐term environmental and load changes. Therefore, this study takes different time scales as the starting point. First, a two‐stage PV grid‐connected inverter generation system model is established, and an overall control strategy is proposed. Next, for short‐term time scales, a virtual inertia strategy based on direct current (DC) voltage droop control is proposed to utilize the energy storage effect of DC capacitors to suppress frequency fluctuations. For long‐term time scales, a strategy for controlling the variable reactive power reserve capacity is proposed to address the inadequacy of frequency regulation caused by traditional fixed de‐rating rate strategies and redundant standby power. Finally, the effectiveness of the proposed strategies is verified through model validation in MATLAB/Simulink. Simulation results demonstrate the effectiveness of the strategies at different time scales, aiding in improving grid frequency response.

Privacy-preserving distributed frequency control for AC microgrids with constrained communication

Distributed control of AC microgrids is one of the most popular methods in the islanded operation mode. Whereas, most of the existing studies either do not consider the potential threat of privacy security or rely on the assumption of ideal communication networks. To this end, this paper presents a novel privacy-preserving distributed secondary frequency control strategy for the privacy protection problem of an islanded AC microgrid with constrained communication. The key contributions of this paper are threefold. (1) Different from the existing privacy-preserving approaches used in AC microgrids, a time-varying function is introduced to mask interactive information such that the frequency cannot be reconstructed by malicious attackers. (2) An event-triggered communication scheme is employed to cope with the constrained communication environment. (3) A privacy-preserving distributed event-triggered control strategy with communication delay is developed such that the frequency restoration and active power sharing of the microgrid are guaranteed. Moreover, the maximum communication delay that the proposed control can withstand is analyzed. Simulation results show the properties of the privacy preservation, the decrease of communication load, and the bounded communication delay allowed in the proposed control strategy.

A heuristic-fuzzy improved virtual synchronous generator control strategy for charging station frequency regulation

To make use of charging station (CS) frequency regulation (FR) capability to get a better FR effect, a CS virtual synchronous generator (VSG) frequency control strategy based on the heuristic-fuzzy logic controller (FLC) is proposed in this paper. First, a CS VSG control architecture with heuristic-FLC is designed. Second, considering electric vehicle (EV) users' charging demands, a CS FR capability evaluation model based on the constant current-constant voltage (CC-CV) charging process and a V2G power response model based on frequency up/down-regulation participation factor are designed. Furthermore, a heuristic-FLC optimized by genetic algorithm (GA) is designed to improve the FR effect of the VSG control strategy. Simulation results show that the proposed strategy can make full use of the CS FR capability and get a better FR effect. In addition, the V2G power response model proposed in this paper can achieve the orderly responses of EVs in a CS under different scenarios.

Resilient robust model predictive load frequency control for smart grids with air conditioning loads

AbstractThis paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two‐area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi‐area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances.

2DOF-PID-TD: A new hybrid control approach of load frequency control in an interconnected thermal-hydro power system

The Load Frequency Control (LFC) scheme, with its primary aim being the maintenance of uniform frequency, has been a heavily researched topic for decades. Achieving a consistent frequency necessitates a delicate balance between load demand and power generation. Researchers strive to find an optimal solution within the LFC domain—one that can effectively withstand drastic load fluctuations. Despite a plethora of efforts, the LFC dilemma remains unresolved, complicated by factors such as dwindling demand-supply and the rapid integration of renewables. Furthermore, the lack of innovation in controller structure design exacerbates the complexity of solving modern LFC problems. Consequently, a robust control approach capable of handling uncertainties while simultaneously regulating system frequency becomes crucial. In light of this, we propose a novel hybrid control architecture called 2DOF-PID-TD. This architecture combines Two Degrees Of Freedom Proportional-Integral-Derivative (2DOF-PID) and Tilt-Derivative (TD) controllers. To optimize the proposed controller, we employ a metaheuristic called the Artificial Gorilla Troops Optimizer (AGTO), which mimics the social behavior and intelligence of gorilla troops. The proposed approach is analyzed in a realistic multi-area multi-source hydro-thermal system, accounting for nonlinearities, random load perturbations, and system parametric uncertainties. Experimental results, when compared with current state-of-the-art optimization algorithms and traditional controller structures, demonstrate the prowess of our approach in terms of precision, robustness, and resilience.

Frequency Coordination Control Strategy for Large-Scale Wind Power Transmission Systems Based on Hybrid DC Transmission Technology with Deep Q Network Assistance

Wind power is currently the most mature representative of sustainable energy generation technology, which has been developed and utilized on a large scale worldwide. The random and fluctuating nature of wind power output poses a threat to the secure and stable operation of the system. Consequently, the transmission of wind power has garnered considerable attention as a crucial factor in mitigating the challenges associated with wind power integration. In this paper, an artificial-intelligence-aided frequency coordination control strategy applicable to wind power transmission systems based on hybrid DC transmission technology is proposed. The line commutated converter (LCC) station at the sending end implements the strategy of auxiliary frequency control (AFC) and automatic generation control (AGC) to cooperate with each other in order to assist the system frequency regulation. The AFC controller is designed based on the variable forgetting factor recursive least squares (VFF-RLS) algorithm for system identification. First, the VFF-RLS algorithm is used to identify the open-loop transfer function of the system. Then, the AFC controller is designed based on the root locus method to achieve precise control of the system frequency. The DC line power modulation quantity is introduced in the AGC to automatically track the active power fluctuation and frequency deviation of the system. The AGC utilizes the classical proportional-integral (PI) control. By selecting the integrated time absolute error (ITAE) performance index to construct the reward function, and using a deep Q-network (DQN) for controller parameter optimization, it achieves improved regulation performance for the AGC. The voltage source converter (VSC) station at the receiving end implements an adaptive DC voltage droop control (ADC)strategy. Finally, the effectiveness and robustness of the proposed frequency control strategy are verified through simulation experiments.

Improving Frequency Control Strategy of Interconnected Power Systems with Renewable Energy Integration Using an Adaptive Neuro-Fuzzy Inference System Controller

Improving Frequency Control Strategy of Interconnected Power Systems with Renewable Energy Integration Using an Adaptive Neuro-Fuzzy Inference System Controller

Design of an adaptive frequency control for flywheel energy storage system based on model predictive control to suppress frequency fluctuations in microgrids

Frequency fluctuations are brought on by power imbalances between sources and loads in microgrid systems. The flywheel energy storage system (FESS) can mitigate the power imbalance and suppress frequency fluctuations. In this paper, an adaptive frequency control scheme for FESS based on model predictive control (MPC) is proposed to suppress the frequency fluctuation in microgrids. Firstly, a linear-fuzzy control is proposed and employed in a frequency regulation controller (FRC) to carry out adaptive frequency control according to the frequency fluctuation and the energy of the FESS, which improves the utilization rate of the FESS, and at the same time avoid the power oscillation caused by overcharging and over-discharging situations. Secondly, a voltage regulation controller (VRC) for dynamic power balance is designed to improve the speed of FESS power balance and suppress the large fluctuation of DC bus voltage. In addition, a MPC based on quadratic programming method is proposed to make the actual current quickly follow the reference current, speed up the power response, and respond to system changes in time through real-time feedback state variables to improve system stability. Finally, the stability of the proposed control system is verified by simulation and experiment.

Load Frequency Control Strategy of Interconnected Power System Based on Tube DMPC

Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive Control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness.

A smooth method for primary frequency of wind turbine based on variable power point tracking and energy storage cooperative control

AbstractThe conventional deloading control has certain problems, such as low power generation efficiency and a small speed adjustment range. To improve the system's frequency quality and enhance the power grid's stability, this study comprehensively considers the effect of random source‐load power fluctuations on the system frequency. Moreover, this study proposes a smooth primary frequency control strategy for wind turbine based on the coordinated control of the variable power point tracking and supercapacitor energy storage. The impact of wind power fluctuations on the system frequency at different timescales for wind turbine is studied based on the historical data of wind power fluctuations in a strong wind meteorological cycle of a wind farm. The method determines the capacity of the energy storage device required for frequency smoothing at the optimal timescale. In combination with the required capacity of the wind turbine to participate in the system's primary frequency regulation, the supercapacitor energy storage device is optimally configured, and a set of supercapacitor energy storage device with the lowest cost under the highest charging/discharging efficiency is designed. Simulation and experimental results show that the primary frequency adjustment capability of the control strategy proposed in this study is significantly improved compared with the conventional primary frequency modulation control.

Research on Mathematical Model of Energy Storage Assisted Wind Turbine Frequency Regulation Control Strategy

Abstract This paper proposes an improved frequency regulation strategy for the Doubly-Fed Induction Generator (DFIG) to mitigate the impact of wind power integration on system frequency. An energy storage system is used on the wind side to participate in the frequency regulation. The influence of wind power integration on the power system frequency is analyzed and an improved frequency control strategy for the DFIG is proposed. A control strategy for energy storage devices to support wind turbine frequency control is proposed. An energy storage device model is established that considers power correction based on the state of charge to avoid excessive charging and discharging. According to the operating state of the DFIG, a control strategy for the frequency regulation of the energy storage device is proposed. According to different wind speeds, a control strategy is proposed for the participation of the joint wind storage system in the frequency regulation. This control strategy can provide frequency support to the system when the wind turbine cannot participate in frequency regulation according to the different operating conditions of the wind turbine. After the wind turbine uses the improved frequency control strategy, it accelerates the recovery of the DFIG rotor speed while avoiding a secondary drop in the system frequency. In addition, when the energy storage device has a poor state of charge and the system frequency is good, it charges to restore the state of charge.

LOAD FREQUENCY CONTROL STRATEGY FOR MICROGRID SYSTEMS USING COMPUTATIONAL INTELLIGENCE OPTIMIZED PID CONTROLLER

This paper presents Load Frequency Control (LFC) strategy for Microgrid (MG) systems, proposing a strategy based on a PID optimized controller using computational intelligence (CI) technique. The MG system, relying solely on Renewable Energy Sources(RES) is modeled in SIMULINK. A novel multi-objective function comprising of Weighted Integral Square Error, WISE and Weighted Integral Absolute Square Error WIASE (WISE+WIASE) is proposed to enhance system performance. Using Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO), the Proportional, Integral and Derivative controller parameters are tuned. Simulation results revealed the superiority of the PSO-PID with the least steady state error of 4.1560e-07 and overshoot 0.0611 respectively over the APSO-PID with steady state error value of 4.8144e-07 and overshoot of 0.1334 respectively. PSO-PID also outperformed APSO-PID across other indices (ITAE, IAE, ITSE and ISE) making the PSO-PID to excel over the APSO-PID in terms of steady state error and overshoot. The controllers proved to be robust in various conditions with the PSO-PID controller exhibiting more robustness and stability than the APSO-PID controller. Comparing PSO and APSO algorithms, PSO outperformed APSO in terms of robustness because for five number of runs in both algorithms, the PSO algorithm presents a more quality and consistent results and also display a better convergence than the APSO algorithm . Conversely, APSO surpasses PSO in fast computational time and convergence speed. These findings underscore the effectiveness of the proposed LFC strategy for RES based Microgrid systems, providing valuable insights into comparative performance of PSO and APSO algorithms.

Distributed optimal load frequency control for multi-area power systems with controllable loads

With the proliferation of distributed energy resources, power imbalance may fluctuate rapidly with a large amount, which may make the conventional frequency control scheme not respond fast to the fluctuation and operate in an economically inefficient manner. This paper proposes a distributed optimal load frequency control (DOLFC) scheme for multi-area power systems with controllable loads, which optimally dispatches generators and controllable loads to participate the frequency regulation with cost minimization. It can restore the nominal frequency and only needs local measurements and local communication for implementation. With the DOLFC, we prove that the closed-loop LFC system asymptotically converges to an equilibrium point which minimizes the control cost of generators and controllable loads, and the equilibrium point is unique if and only if the multi-area power network is a tree. Compared with the conventional LFC and economic LFC in literature, the proposed DOLFC has higher economic efficiency and better control performance. Finally, a four-area power system is employed to illustrate the effectiveness of the proposed scheme by numerical simulations.