System Frequency Regulation Research Articles

Performance analysis of proportional integral derivative controller for frequency regulation of an interconnected power system integrated with renewable energy sources

This paper focuses on conducting a performance analysis of classical proportional integral derivative (PID) controller for frequency regulation in multi-area interconnected power system. The power system considered in the analysis includes wind and solar power sources integrated with thermal and hydal system. In area 1 along with thermal unit, WPG is appended and in second control area, a hydal unit is appended with SPG. The simulation of the present work is performed using MATLAB/Simulink. The dynamic responses obtained using the simulation using PID controller and without any controller have been compared. It is clearly demonstrated from the results that implementation of a PID controller effectively reduces the fluctuations in the system and hence, aids in order to maintain constant frequency.

Optimal vehicle-to-grid control for supplementary frequency regulation using deep reinforcement learning

• This research focus on the potential benefits of electric vehicles battery storage and vehicle-to-grid (V2G) technology to provide grid frequency response services. • We propose an optimal V2G control based on deep reinforcement learning which maximises profit to both electric vehicle (EV) owners and the energy aggregator. • The approach uses the deep deterministic policy gradient (DDPG) for scheduling the EV batteries charging/discharging to simultaneously satisfy the driving demand and participate in frequency regulation. • The proposed control scheme has been successfully tested on a two-area interconnected power system undergoing frequency deviations. The expanding Electric Vehicle (EV) market presents a new opportunity for electric vehicles to deliver a wide range of valuable grid services. Indeed, the emerging Vehicle-to-Grid (V2G) technology with bi-directional flow of power provides the grid with access to mobile energy storage for demand response, frequency regulation and balancing of the local distribution system. This reduces electricity costs at peak hours and can be profitable for customers, network operators and energy retailers. In this paper, an optimal V2G control strategy using Deep Reinforcement Learning (DRL) is proposed to simultaneously maximise the benefits of EV owners and aggregators while fulfilling the driving needs of EV owners. In the proposed DRL-based V2G control strategy, a Deep Deterministic Policy Gradient (DDPG) agent is used to dynamically adjust the V2G power scheduling to satisfy the driving demand of EV users and simultaneously perform frequency regulation tasks. The proposed V2G control scheme is tested on a two-area power system undergoing frequency deviations. The results showed that the proposed V2G control leads to a better frequency deviation reduction and improved Area Control Error (ACE), while satisfying the charging demands of EVs as compared to other strategies.

Resilient PIDA Control Design Based Frequency Regulation of Interconnected Time-Delayed Microgrid Under Cyber-Attacks

This paper addresses the robust and resilient PIDA control design for frequency regulation of the cyber-physical power system. PIDA control design is carried-out using a recently develop supply demand optimization algorithm (SDOA). This SDOA inspires by the supply-demand mechanism in economics theory. Besides, SDOA is a global optimization algorithm and simple in design. Further, this paper is introduced a new fitness function to obtain the unknown parameters of PIDA controller. From application point of view, the proposed control design is validated on the interconnected time-delayed microgrid system configuration with cyber-attacks. The effectiveness and efficacy of the proposed control design are analyzed under various practical conditions such as cyber-attacks, communication time-delay, parametric uncertainty in system parameters, stochastic load deviation, stochastic wind power deviation, and stochastic solar power deviation. Further, the superiority of SDOA shows in comparison to the grey wolf optimizer (GWO), and whale optimization algorithm (WOA). Finally, it is concluded that the proposed PIDA control design performs better, robust and resilient in comparison to the existing PID control design.

Improved Fractional-order Sliding Mode Controller for Frequency Regulation of a Hybrid Power System with Nonlinear Disturbance Observer

With extensive integration of renewable resources and ever-rising consumer demand, the existing power systems inevitably experience the somber frequency instability problem. This work attempts to design and apply a nonlinear disturbances observer (NDO)-aided improved resilient controller to minimize the frequency deviation of a hybrid power system (hy-PS) following unknown/uncertain system disturbances. The NDO is developed to estimate time-varying uncertain bounded plant disturbance, including parametric uncertainties, load fluctuation, unsteady wind velocity, and unmodelled system dynamics. Subsequently, an active disturbance rejection control is implemented using an improved fractional-order sliding mode controller (iFOSMC) having NDO integrated to cope with system disturbances and offer finite-time asymptotic state convergence to the equilibrium point with least oscillations in control effort. Finite-time convergence of estimation error and asymptotic stability of the developed closed-loop system is also provided by applying the Lyapunov argument to guarantee the reliability of the proposed control algorithm. Besides, the mastery of the proposed NDO-aided iFOSMC compared to other well-known controllers has been demonstrated through comparative study under different perturbed conditions. The obtained system outputs show the high-effective performance of the proposed NDO-aided iFOSMC in terms of faster disturbance compensation, least chattering in control effort, and improved system outputs. The efficacy of the proposed frequency controller has also been tested against false data injection (FDI) attacks in a cyber-physical environment.

Improvement of Frequency Regulation of a Wind-Integrated Power System Based on a PD-PIDA Controlled STATCOM Tuned by the Artificial Rabbits Optimizer

Improvement of Frequency Regulation of a Wind-Integrated Power System Based on a PD-PIDA Controlled STATCOM Tuned by the Artificial Rabbits Optimizer

Novel Optimal Load Control for Power System Frequency and Voltage Regulation

Novel Optimal Load Control for Power System Frequency and Voltage Regulation

Day-ahead load optimal distribution of thermal power coupled electric energy storage frequency regulation system considering the effect of overtemperature

Coupling energy storage devices on the generation side can significantly improve the AGC frequency regulation performance of thermal power units and bring frequency regulation benefits. In the traditional joint frequency regulation mode, energy storage is generally used to compensate the deviation between thermal power output and dispatching command, without considering the deep coordination between thermal power units and energy storage system. Thermal power units are still in fast variable load operation state, and the potential of coupled energy storage in improving the safety and other comprehensive performance of the units has not been explored deeply. Therefore, in this paper, the energy storage system is used to actively cooperate with the unit for secondary frequency regulation, and the load command is optimally distributed by minimizing the total cost function, including the variable load cost of the unit. The maintenance cost caused by overtemperature is considered in the cost model, and is analyzed through a simulation example. The results show that the total cost, considering the influence of over temperature on the optimal load distribution, is lower than that when the unit responds at a constant high load changing rate. The active load bearing of energy storage alleviates the cost increase caused by rapid load changing of the unit, making the unit benefit from energy storage and the total cost tends to be optimal.

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.

An Improved Adaptive Load Shedding Control Strategy for Primary Frequency Regulation of Wind Power Generation System

With the continuous improvement of the proportion of wind power generation, the volatility and uncertainty of wind power pose a serious threat to the stable operation of the power system. However, the traditional primary frequency regulation strategy does not fully consider the influence of wind turbine inertia. In addition, it may cause the secondary frequency drop if the instantaneous power of the wind turbine drops too much during the speed recovery. To solve this problem, this paper proposes an adaptive primary frequency regulation strategy for the mobile load shedding power tracking curve. Under this strategy, by controlling the output active power reference value, the kinetic energy stored in the rotor is fully released to support the grid frequency for a short time. Then, in the process of rotor speed recovery, the frequency secondary drop can be alleviated by moving the load-shedding power tracking curve. Finally, the simulation results in MATLAB/Simulink verify the rationality of the proposed adaptive load-shedding control strategy.

Distributional two-level synergetic unit commitment considering three scheduling states

The high penetration of distributed energy resources (DERs) (i.e. solar energy, wind power, photovoltaic and energy storage system) are integrated into the transmission grid (TG) and the active distribution networks (ADNs) in a centralized or decentralized manner or distributed manner, which significantly impose crucial challenges on the frequency regulation of power system. To make full use of the frequency regulation effect in dispatch and control, and considering the topology interlinked and the information sharing between TG+ADNs, in this paper, a distributional TG+ADNs two-level synergetic unit commitment (TLS-UC) considering three scheduling states is proposed. To explore the synergetic capacity of the TG+ADNs accommodating uncertainties of load and DERs, three scheduling states including the automatic synergetic state (i.e. primary frequency regulation (PFR)), the coordinated synergetic state (i.e. secondary frequency regulation (SFR)) and the controlled synergetic state (i.e. economic dispatch (ED)) of TG are defined and implemented to deal with uncertainties through the interconnection of the tie-line between TG+ADNs. A decentralized and parallel analytical target cascading (ATC) algorithm is employed to solve the proposed TLS-UC model. A modified 6-bus TG system with 2 ADNs (T6D2) and IEEE 118-bus TG system with 10 ADNs (T118D10) are used to test the proposed TLS-UC model. Test results illustrate that the proposed method is effective and efficient, and it can enhance the synergic capacity of power system to keep source power balance under uncertainties.

Reactive power assisted frequency regulation scheme for large-scale solar-PV plants

Due to reduction in power system inertia and frequency regulation reserve with high penetration of power-electronic converter (PEC) interfaced renewable sources, advanced control strategies must be developed to exploit the full potential of solar-photovoltaic (PV) systems to improve the frequency stability. In this paper, a new frequency regulation approach is proposed based on reactive-power control (i.e., frequency regulation via reactive-power control (FRQC) scheme) for solar-PV systems, which manipulates the active power demand as a function of the system frequency deviation by varying network voltages via reactive power control. The feasibility of the proposed control scheme is investigated using both theoretical analysis and simulation studies following a loss of a major generation source. Frequency and voltage deadbands, and time delays are added to the FRQC scheme to activate and deactivate the control scheme, and hence it maximises the frequency support without affecting the voltage stability. The reliability and voltage stability (RVS) test system is used to demonstrate the effectiveness of the proposed FRQC scheme under different gains of the controller, PV loading levels, and different PV penetration levels. The comparative results show that the proposed FRQC scheme improves the post-fault frequency trajectory, and hence the system frequency stability.

A Novel Optimal Robust Design Method for Frequency Regulation of Three-Area Hybrid Power System Utilizing Honey Badger Algorithm

This study proposes an innovative technique for the load frequency control (LFC) of a three-area hybrid power system by putting into consideration of honey badger algorithm-based fractional order proportional integral derivative (FOPID) controller. The algorithm’s veritable use is attempted by arranging a FOPID controller for the hybrid system’s frequency regulation; the incredible potential of the proposed algorithm is proven. The proposed HBA-based FOPID regulator is presented by comparing its results with other current methods. It is seen that the said regulator is more viable for the frequency control contrasted with the regular regulator by considering a change in system parameters and different paces of RES penetration. It is also seen that the recommended controller is found to be more effective for load frequency control than the conventional controllers.

Research on the Primary Frequency Regulation Control Strategy of a Wind Storage Hydrogen-Generating Power Station

Wind curtailment and weak inertia characteristics are two factors that shackle the permeability of wind power. An electric hydrogen production device consumes electricity to produce hydrogen under normal working conditions to solve the problem of abandoning wind. When participating in frequency regulation, it serves as a load reduction method to assist the system to rebuild a power balance and improve the wind power permeability. However, due to its own working characteristics, an electric hydrogen production device cannot undertake the high-frequency component of the frequency regulation power command; therefore, an energy storage device was selected to undertake a high-frequency power command to assist the electric hydrogen production device to complete the system frequency regulation. This paper first proposes and analyzes the architecture of a wind storage hydrogen-generating station for centralized hydrogen production with a distributed energy storage, and proposes the virtual inertia and droop characteristic mechanism of the wind storage hydrogen-generating station to simulate a synchronous unit. Secondly, an alkaline electrolysis cell suitable for large-scale engineering applications is selected as the research object and its mathematical model is established, the matching between different energy storage devices and their cooperation in power grid frequency regulation is analyzed, and a super capacitor is selected. A control strategy for the wind storage hydrogen-generating power station to participate in power grid frequency regulation with a wide time scale is then proposed. Using the first-order low-pass filter, the low-frequency component of the frequency regulation power command is realized by an electric hydrogen production device load reduction, and a high-frequency component is realized by the energy storage device. Finally, the effectiveness and rationality of the proposed control strategy are verified by establishing the simulation model of the wind storage hydrogen-generating power station with different initial wind speed states, comparing the system frequency dip values under the proposed multi-energy cooperative control strategy and a single energy device control strategy.

Optimal Solution for Frequency and Voltage Control of an Islanded Microgrid Using Square Root Gray Wolf Optimization

Voltage and frequency deviation in the islanded operation of a microgrid (MG), due to the uncertainty and lack of inertia in the selection of optimal proportional integral (PI) controller gain, is a challenging task. Although various optimization algorithms have been proposed to achieve this task, most of them require a large number of iterations and are time intensive, making them inefficient for real-time applications. Gray wolf optimization (GWO), a new meta-heuristic algorithm, addresses these issues and has many advantages, including simplicity due to fewer control parameters, flexibility, and globalism. This paper proposes a simple and efficient modified algorithm, called square root gray wolf optimization (SRGWO) algorithm, to realize superior hunting performance. SRGWO is verified using twenty-three benchmark test functions. The algorithm is applied for optimal voltage and frequency regulation of a photovoltaic-based microgrid system operating in the islanded mode during distributed generation insertion and load change conditions. The voltage and frequency gain parameters of the PI controller are optimized. A comparison of the simulation results of the SRGWO algorithm with those of the original gray wolf algorithm (GWO), particle swarm optimization (PSO), augmented gray wolf optimization (AGWO), enhanced gray wolf optimization (EGWO), and gravitational search algorithm (GSA) reveal that the proposed SRGWO algorithm significantly improves system performance while maintaining its simplicity and easy implementation. Furthermore, the SRGWO algorithm obtains the minimum fitness function value in fewer iterations than other algorithms. Moreover, it improves the power quality of the system with regard to minimum total harmonic distortion.

Decentralized Grid-Forming Control Strategy and Dynamic Characteristics Analysis of High-Penetration Wind Power Microgrids

As wind power generation transits from centralized development mode to decentralized on-site consumption mode, microgrid (MG) can provide an efficient solution for wind power integration into the distribution network. However, the high-penetration wind power MG is the typical weak power grid system. The traditional wind turbine generator (WTG) participates in system frequency regulation through grid-following current source, which relies on the phase-locked loop for voltage phase synchronization and is unable to provide strong frequency support in weak power grid conditions. To fill this gap, this paper presents a decentralized grid-forming control strategy of high-penetration wind power MG. A wind power adaptive dynamic droop mechanism considering wind energy characteristics and rotor speed dynamic is proposed, cooperating with the implementation of wind maximum power point tracking (MPPT) for economical operation. A detailed small-signal model for voltage source wind power-based system considering electromechanical transients and adaptive droop mechanism is developed. The dominant modes are figured out and the critical control parameters are established and optimized. The proposed wind power adaptive droop mechanism can effectively provide frequency regulation and robust control performance. Theoretical analysis, time-domain simulation results and hardware-in-the-loop experiments under various scenarios verify the feasibility and effectiveness of the proposed strategy.

Chance-Constrained Optimal Configuration of BESS Considering Uncertain Power Fluctuation and Frequency Deviation Under Contingency

With the accelerating integration of variable renewable energies (VREs), power systems become more vulnerable to active power disturbances, and more drastic frequency dynamics emerge. The battery energy storage system (BESS) is able to handle the uncertainties of VREs, and the decreasing system inertia and frequency regulation capability. This paper proposes a chance-constrained optimal configuration scheme for the BESS to maintain both the uncertain power fluctuations and frequency deviation within predefined limits. First, the required frequency regulation capability of the BESS constrained by the maximum transient frequency deviation (MTFD) and quasi-steady-state frequency deviation (QSSFD) is estimated. Then, the kernel density estimation method is utilized to model the net power fluctuations of VREs and load. A multi-objective chance-constrained programming model accounting for the life cycle cost, energy arbitrage, uncertain power fluctuation, MTFD, and QSSFD is established to optimize the capacity of the BESS. Furthermore, the Bernstein approximation is utilized to process the chance constraint, and transform the optimization model into a deterministic form. Based on the linear weighted method and Benders decomposition, the optimization model is solved through alternating iteration. Case studies were conducted to validate the proposed scheme, showing superior performance in smoothing uncertain power fluctuations, and reducing frequency deviation under contingencies.

Frequency regulation control strategy of wind farms based on temporal and spatial uncertainty

The Frequency regulation capability of a double-fed induction generator (DFIG) in a large wind farm is influenced by its operating state and installation position, as well as the wind speed (size and direction) at which it is located, resulting in temporal and spatial uncertainties. First, to simplify control difficulty and optimize frequency regulation increment allocation, model predictive control(MPC) and k-means clustering method are used for spatial optimal grouping of DFIG in wind farms based on the uncertainties of DFIG installation location and wind direction. Second, a timing coordinated control strategy is proposed based on the uncertainties of the DFIG operation state and wind speed. The strategy allows the DFIG to enter and exit the frequency regulation system based on wind speed, active power increment, and sustainable time and then reduces the negative effect of the DFIG exiting the frequency regulation system. Finally, based on temporal and spatial optimization, a coordinated control strategy based on temporal and spatial uncertainties is proposed to optimize the overall frequency regulation capability of wind farms. Simulation examples show that the time–space coordinated control strategy treats each group as an independent DFIG control, which not only simplifies the control problem but also fully utilizes the DFIG frequency regulation capacity and effectively improves the system frequency characteristics.

Probabilistic Description of the State of Charge of Batteries Used for Primary Frequency Regulation

Battery participation in the service of power system frequency regulation is universally recognized as a viable means for counteracting the dramatic impact of the increasing utilization of renewable energy sources. One of the most complex aspects, in both the planning and operation stage, is the adequate characterization of the dynamic variation of the state of charge of the battery in view of lifetime preservation as well as the adequate participation in the regulation task. Since the power system frequency, which is the input of the battery regulation service, is inherently of a stochastic nature, it is easy to argue that the most proper methodology for addressing this complex issue is that of the theory of stochastic processes. In the first part of the paper, a preliminary characterization of the power system frequency is presented by showing that with an optimal degree of approximation it can be regarded as an Ornstein–Uhlenbeck process. Some considerations for guaranteeing desirable performances of the control strategy are performed by assuming that the battery-regulating power depending on the frequency can be described by means of a Wiener process. In the second part of the paper, more realistically, the regulating power due to power system changes is described as an Ornstein–Uhlenbeck or an exponential shot noise process driven by a homogeneous Poisson process depending on the frequency response features requested of the battery. Because of that, the battery state of charge is modeled as the output of a dynamic filter having this exponential shot noise process as input and its characterization constitutes the central role for the correct characterization of the battery life. Numerical simulations are carried out for demonstrating the goodness and the applicability of the proposed probabilistic approach.

A low frequency oscillation suppression method for grid-connected DFIG with virtual inertia

This paper addresses the problem that grid-connected doubly-fed induction generators (DFIG) with virtual inertia control cannot simultaneously take into account both rotor angle stability and system frequency regulation. Based on the dynamic energy curvature, a method of suppressing low frequency oscillation that considers the effect of frequency regulation is proposed. First, according to the relationship between the system stability and the dynamic energy of DFIG, the energy curvature model of the system is constructed. From that a detailed expression of the energy curvature is obtained, so that current compensation terms can be inversely deduced, and compensation branches can be constructed accordingly to improve the energy curvature during low frequency oscillation. Further, with the goal of optimizing both energy curvature and frequency regulation, a branch parameter optimization scheme is established. This scheme places equal emphasis on oscillation suppression and frequency support in order to maximize the capability of DFIG in both oscillation suppression and frequency regulation. Finally, the proposed method is verified through simulation tests performed in RTDS according to the parameters of a real grid-connected wind farm. The results show that, by ensuring the steady-state frequency deviation and reducing the maximum frequency deviation, this method can effectively increase the energy curvature, quickly suppress the low frequency oscillation in various disturbance cases, and simultaneously improve the frequency support capability of DFIG.

Teaching-learning optimizer-based FO-PID for load frequency control of interlinked power systems

ABSTRACT Designing an effective, robust controller is vital for regulating system frequency corresponding to the load demand variations in a traditional interlinked power system. Hence, load frequency control plays a critical role in countering load disturbances and minimizing unscheduled power flow between interlinked areas. This paper proposes a novel teaching-learning optimization (TLO) based dual-loop load frequency control approach aided by fractional-order proportional-integral-derivative controller (FO-PID). The inner control loop of the presented method employs internal model control and model approximation. On the other hand, the external loop uses a TLO-tuned FO-PID controller to improve the outcomes of the proposed approach. The proposed method is validated using a 1-area multi-stage reheat hydrothermal power system (RHTPS), 2-area reheat thermal power system (RTPS), and 2-area multi-stage RHTPS. The robustness of the proposed method is demonstrated by executing sensitivity analysis. It is established from the simulation results that the proposed approach enhances the dynamic response of the test systems substantially.