System Frequency Regulation Research Articles

A resilience enhanced hierarchical strategy of battery energy storage for frequency regulation

Battery energy storage system (BESS) has been regarded as an effective technology to regulate system frequency for power systems. However, the cost and the system security of battery energy storage are the bottle necks for the battery energy storage system to be applied to practical projects for frequency regulation. Comparing with the large-scale battery energy storage system, the small-scale energy storage system has better robustness and could reduce the cost and improve the system security with lower energy capacity and wider regional distribution. However, coordinating the state of charge (SOC) of a large number of small-scale battery storage system to provide effective frequency regulation service is a challenge for the operation of power systems. In this paper, a hierarchical energy management strategy, which can be applied to different scenarios with and without limited communication systems, has been proposed to coordinate a large number of small-scale energy storage systems to regulate frequency for power systems. The rated power capacity, energy capacity, and SOC of the BESS are considered in the proposed strategy to maximize the capability of frequency regulation for power systems. The proposed strategy can be applied to power systems without powerful and expensive communication systems and coordinate BESS according to SOC of the BESS and the quality of communication system. The resilience of power systems after extreme natural events can be enhanced by the frequency regulation service of BESS under proposed strategy.

Small‐signal modelling and analysis of microgrids with synchronous and virtual synchronous generators

AbstractIn autonomous alternating current microgrids, the grid‐forming virtual synchronous generators can cooperate with the conventional synchronous generators to improve system inertia and frequency regulation capability. However, undesired active power oscillations between the synchronous generators and grid‐forming virtual synchronous generators may trigger their overcurrent protection and even result in a blackout. To explicitly reveal the oscillatory modes over all frequency bands, a high‐fidelity full‐order state‐space model is first developed. A potentially destabilising sub‐synchronous oscillation mode resulting from the interaction between grid‐forming virtual synchronous generators voltage controller and synchronous generators q‐axis damper winding is identified. Other modes reflecting the low‐frequency oscillation and frequency restoration dynamics are also assessed. Subsequently, to make a reasonable trade‐off between the accuracy and simplicity of system modelling, an enhanced quasi‐stationary model dedicated to low‐frequency oscillation evaluation is simplified from the full‐order type. The enhanced quasi‐stationary model features simplicity and low‐order benefits, which makes it more practical for multi‐generator system analysis. Moreover, by considering the dynamics of synchronous generators field winding and excitation system, the enhanced quasi‐stationary model significantly improves the low‐frequency oscillation characterisation accuracy compared with the existing quasi‐stationary model. The two developed models are comprehensively compared with the existing small‐signal models. Real‐time simulations based on RT‐LAB are conducted to verify the correctness of the theoretical analysis and the accuracy of the proposed small‐signal models.

Non-integer disturbance observer-aided resilient frequency controller applied to hybrid power system

This work made the earliest effort to investigate the performance of a non-integer disturbance observer (NiDob)-aided non-integer sliding mode controller (NiSMC)/second-order SMC (SOSMC) for frequency regulation of an islanded hybrid power system with matched/mismatched uncertainties. The unavoidable presence of unknown system disturbances, uncertainties, and practical limitations of control inputs are considered while developing the proposed resilient nonlinear controllers. Firstly, a NiDob is designed to estimate unknown bounded aggregated plant uncertainties. Subsequently, an improved NiSMC/SOSMC is developed by conveniently augmenting disturbance estimation. Quasi-oppositional marine predators' algorithm (QOMPA) is applied to obtain the near-optimum gains of the proposed frequency controllers minimizing an integral error-based objective function. The finite-time convergence of NiDob and closed-loop asymptotic stability of the studied power system has been established using the Mittag-Leffler/Lyapunov argument. The usefulness and practicability of the applied frequency have been demonstrated by performing a comparative study with SMC, NiSMC (without NiDob), NiDob-aided NiSMC, and other results reported in the literature. The numerical simulations showcase the efficacy of the applied control methodology regarding minimal chattering, faster disturbance rejection, and improved transient performance. The proficiency of the developed control methodology is validated on an IEEE-39/IEEE-68 bus testbed. Finally, the ability of the suggested resilient control algorithm in the detection and mitigation of deception-type cyber-attacks has been tested and affirmed in cyber-physical environments.

A Novel Ultra Local Based-Fuzzy PIDF Controller for Frequency Regulation of a Hybrid Microgrid System with High Renewable Energy Penetration and Storage Devices

A new ultra-local control (ULC) model and two marine predator algorithm (MPA)-based controllers; MPA-based proportional-integral-derivative with filter (PIDF) and MPA-based Fuzzy PIDF (FPIDF) controllers; are combined to enhance the frequency response of a hybrid microgrid system. The input scaling factors, boundaries of membership functions, and gains of the FPIDF con-troller are all optimized using the MPA. In order to further enhance the frequency response, the alpha parameter of the proposed ULC model is optimized using MPA. The performance of the pro-posed controller is evaluated in the microgrid system with different renewable energy sources and energy storage devices. Furthermore, a comparison of the proposed MPA-based ULC-PIDF and ULC-FPIDF controllers against the previously designed controllers is presented. Moreover, a vari-ety of scenarios are studied to determine the proposed controller’s sensitivity and robustness to changes in wind speed, step loads, solar irradiance, and system parameter changes. The results of time-domain simulations performed in MATLAB/SIMULINK are shown. Finally, the results demonstrate that under all examined conditions, the new ULC-based controllers tend to further enhance the hybrid microgrid system’s frequency time response.

Strategy of 5G Base Station Energy Storage Participating in the Power System Frequency Regulation

Strategy of 5G Base Station Energy Storage Participating in the Power System Frequency Regulation

Optimal capacity configuration of the wind-storage combined frequency regulation system considering secondary frequency drop

With wind power integrated into the power system on a large scale, the system has become vulnerable to the frequency stability issue. The battery energy storage system (BESS) is considered the key solution to improving the system frequency regulation performance due to its fast response ability. Furthermore, the construction of wind-storage combined frequency regulation systems has been developed for many years, in which the optimal capacity configuration of the wind-storage system is getting more attention. However, the secondary frequency drop (SFD) caused by wind turbines (WTs) participating in primary frequency regulation (PFR) is neglected in most existing capacity configurations, which is worthy of further study. In this paper, the optimal capacity of the wind-storage combined frequency regulation system is studied from the perspective of SFD. The time-domain expressions of two-stage system frequency response considering SFD are derived based on the wind-storage combined frequency regulation model. Next, considering the technical and economic characteristics of wind-storage combined frequency regulation, an optimization model of the energy storage capacity configuration is established with the objective of minimizing the sum of the maximum frequency deviations in two stages and the energy storage cost. The optimization model is solved by the multi-objective salp swarm algorithm (MSSA) to obtain the setting value of wind-storage combined frequency regulation parameters and the optimal energy storage capacity. The effectiveness of the proposed method is verified in MATLAB. The simulation results show that the proposed model can effectively improve the frequency regulation effect of the system and ensure the optimal capacity configuration with better economy.

Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy

Energy storage (ES) can mitigate the pressure of peak shaving and frequency regulation in power systems with high penetration of renewable energy (RE) caused by uncertainty and inflexibility. However, the demand for ES capacity to enhance the peak shaving and frequency regulation capability of power systems with high penetration of RE has not been clarified at present. In this context, this study provides an approach to analyzing the ES demand capacity for peak shaving and frequency regulation. Firstly, to portray the uncertainty of the net load, a scenario set generation method is proposed based on the quantile regression analysis and Gaussian mixture model clustering. Then, a multi-scenario and multi-time scale optimal operation model is established to handle the uncertainty of net load, and the power correction model for ES operations is established to accommodate the balance of ES charging/discharging and optimization of system operation cost. Finally, based on the solution results of the above models, the method for determining the system's demand for ES capacity is proposed, and the relationship between the penetration of RE, ES power and capacity, and the confidence level of meeting demand is obtained. Numerical studies show that with a confidence level of 90% for satisfying demand, the 49.5% RE penetration system (the maximum load is 9896.42 MW) needs ES power and capacity of 1358 MW and 4122 MWh for peaking and ES power and capacity of 478 MW and 47 MWh for frequency regulation. Further, as the penetration of RE increases, the proportion of ES demand power to the system's power supply capacity and duration demand of ES also increase.

Direct Search Algorithm for Load Frequency Control of a Time-Delayed Electric Vehicle Aggregator

This article addresses the topic of frequency regulation of a single-area power system connected to an electric vehicle (EV) aggregator over a non-ideal communication network. It is considered that the command control action is received by the EV aggregator with constant delay and the power system includes uncertain parameters. Due to the presence of uncertainties and the delay term, the frequency regulation problem is non-convex and hard to solve. The present approaches in the literature convert the non-convex control design problem into a convex problem with a set of Linear Matrix Inequalities (LMIs), which is conservative and in many cases results infeasibility. In this paper, an innovative iterative algorithm, called direct search, is employed for the time-delayed system to design the unknown parameters of a pre-assumed controller. The controller choice is not limited and various controllers’ structures can be assumed. Without loss of generality, a proportional-integral (PI) controller is designed. The novel direct search algorithm can determine a feasible solution whenever at least one solution lays in the design space. Hence, by selecting a wide design space, we can anticipate that the PI controller guarantees closed-loop stability. Numerical simulations are carried out to demonstrate the performance of the developed controller compared to the state-of-the-art approach.

Review on Soft Computing-Based Controllers for Frequency Regulation of Diverse Traditional, Hybrid, and Future Power Systems

In recent decades the power system has become a complex network, to design a load frequency control (LFC) requires solving a complex equation. Optimisation techniques are essentially required to optimise the parameters of different controllers used for LFC issues in the power system. In a unified power system, the LFC is examined from all angles using different optimisation strategies to optimise the conventional PI, PID, cascaded, and fuzzy controllers as well as recently designed controllers. This manuscript specifically reviews the use of soft computing techniques in the frequency regulation of the power system with single/multiple areas that include conventional, renewable, and combinations of both, with FACTS devices and certain energy storage devices such as superconductor magnetic energy storage (SMES) and battery sources. Furthermore, deregulated power systems and microgrids are also considered for the study. To regulate LFC under various disturbances such as generation rate constraints (GRC) and dead band control, a few additional control approaches are utilised. Models of the power system are discussed and analysed. In addition, the merits and drawbacks of the studied techniques/structures that address design and implementation issues—as well as control issues that relate to the LFC problems—have been discussed.

Multi-resolution based PID controller for frequency regulation of a hybrid power system with multiple interconnected systems

Automatic generation control (AGC) in modern power systems (PS) is difficult because the output power of many power resources is intermittent, and the load and system parameters vary widely. In this paper, a novel control scheme known as the wavelet based multiresolution proportional integral derivative (MRPID) controller for multiple interconnected hybrid power sources is presented. The discrete wavelet transform (DWT) is used in the proposed wavelet based MRPID controller to split the error between the actual and target responses into different frequency components at several stages. To ensure optimum system performance, the gains of the MRPID controller are fine-tuned using the Fox Optimizer Algorithm (FOA), a new powerful metaheuristic technique. The proposed MRPID controller is evaluated in a three-area hybrid system where each area contains a combination of conventional generation (gas, thermal reheat and hydro) and renewable generation sources (solar, and wind). The proposed controller also accounts for system non-linearities, including boiler dynamics, time delay, dead band, generation rate limitation, system uncertainties, and load changes. In the hybrid system studied, the proposed MRPID is compared with FOA-tuned PID and PI controllers. The proposed MRPID controller tuned with FOA algorithm effectively reducing the peak overshoot of 89.03%, 76.89 and 56.96% and undershoot of 69.52%,66.90 and 94.29% for ∆Ptie12, ∆Ptie23 and ∆Ptie13 respectively as compared to FOA based PI controller.

Frequency stability analysis of power system with photovoltaic frequency regulation considering communication delay

Photovoltaic (PV) grid-connected will reduce the inertia of the system, which has an effect on power system frequency reliability. In order to clarify the frequency stability situation of power system when photovoltaic participates in frequency regulation, this paper first establishes the load frequency control (LFC) model of the power system with photovoltaic based on the analysis of the traditional LFC model of the power system. Secondly, based on the Pade approximation method, the communication delay in the control loop is linearized. The frequency stability of power system with photovoltaic participation in frequency regulation is characterized by system frequency steady-state error, feedback system sensitivity, and closed-loop system stability margin. According to the index calculation and theoretical derivation, the influence of system frequency regulation related parameters on frequency characteristics is analyzed. Finally, on the matlab/simulink simulation environment, the frequency stability methodological approach presented in this research is validated. The results of the simulations demonstrate the effectiveness of this analytical approach in assessing the frequency stability of a power system with photovoltaic frequency regulation while taking communication latency into account. It can provide guidance for the power grid’s secure operation and control when photovoltaics participate in frequency regulation.

The Analysis and Calculation of Power Angle Dynamics in Grid Forming Converter Under Large Disturbances Based on KBM Asymptotic Method

Grid forming (GFM) control strategy has been increasingly used in grid-connected converters to regulate system frequency and mimic the inertia of synchronous generator. However, the description of its power angle dynamics under large disturbances is still lacking up to date, which may pose great challenges to stable operation and economic configuration of grid-connected converters in the future power system. To overcome the aforementioned obstacle, a Krylov–Bogoliubov–Mitropolsky (KBM) asymptotic method with the perturbation theory is presented first in this article to derive an analytical solution for power angle trajectory under large disturbances. Considering that the antiderivative problem is usually unsolvable due to the complicated perturbation function, a tailored integral method is further proposed to address this problem and simplify the final result, which facilitates the KBM asymptotic method to the analysis and calculation of power angle trajectory. As a result, an explicit and accurate time-domain expression is obtained, and it reveals the quantitative relationships between control parameters and dynamic characteristics, which may provide a good potential for system transient stability analysis and GFM converter design. In the end, the effectiveness and accuracy of the proposed method are verified by both simulations and control-hardware-in-loop experiments.

Multi‐objective salp swarm algorithm‐based fractional order fuzzy precompensated PDPI controller for frequency regulation of hybrid power system

AbstractSignificant number of renewable sources when integrated with distributed generations and energy storage components form a sustainable power system called hybrid power system (HPS). But the stochastic nature of the sources and continuous variation of load cause serious issues in the power systems like frequency instability. Our work proposes a novel approach for load frequency control (LFC) of the proposed system. For the work, both frequency deviation and control signal are used to give the input signal to energy storage components. A new fuzzy‐based controller called fractional order fuzzy precompensated PDPI (FO‐FPPDPI) controller for LFC of the mentioned HPS is suggested in this paper. The controller is optimized by a nature inspired multi‐objective salp swarm algorithm (MSSA). The comparison of the proposed approach with the classical PID controller and Fuzzy PID (FPID) controller is carried out by taking several system operating conditions. Robustness of the FO‐FPPDPI controller is also validated for system parameter variations and the superiority of the proposed control strategy over PID and FPID controllers is validated for all the situations. Finally, the control strategy is realized in OPAL‐RT to measure its fidelity with the simulation results.

Modified FOPID Controller for Frequency Regulation of a Hybrid Interconnected System of Conventional and Renewable Energy Sources

In this article, a fractional-order proportional-integral-differential (FOPID) controller and its modified structure, called a MFOPID controller, are presented. To guarantee optimal system performance, the gains of the proposed FOPID and MFOPID controllers are well-tuned, employing the Jellyfish Search Optimizer (JSO), a novel and highly effective bioinspired metaheuristic approach. The proposed controllers are assessed in a hybrid system with two domains, where each domain contains a hybrid of conventional (gas, reheat, and hydro) and renewable generation sources (solar and wind). For a more realistic analysis, the presented system model includes practical limitations with nonlinear characteristics, such as governor dead zone/band (GDZ/GDB), boiler dynamics, generation rate limitation/constraint (GRL/GRC), system uncertainties, communication time delay (CTD), and load changes. The suggested methodology outperforms some newly developed heuristic techniques, including fitness-dependent optimizer (FDO), sine-cosine algorithm (SCA), and firefly algorithm (FA), for the interconnected power system (PS) of two regions with multiple generating units. Furthermore, the proposed MFOPID controller is compared with JSO-tuned PID/FOPID and PI controllers to ascertain its superiority. The results signify that the presented control method and its parametric optimization significantly outperforms the other control strategies with respect to minimum undershoot and peak overshoot, settling times, and ITSE in the system’s dynamic response. The sensitivity analysis outcomes imply that the proposed JSO-MFOPID control method is very reliable and can effectively stabilize the load frequency and interconnection line in a multi-area network with interconnected PS.

A grid forming control for wind energy conversion systems

This paper presents an effective and practical structure to improve voltage and frequency regulation in grid-forming control of self-excited induction generator-based wind energy conversion systems. In the proposed control structure, in addition to a fixed parallel excitation capacitor, a small wound rotor synchronous generator is also used as a reactive power compensator. The excitation voltage of the small synchronous generator is controlled in such a way that by injecting the required amount of reactive power, this generator maintains the balance between the inductive and the capacitive reactive powers in the isolated system, and thus, improves the system voltage regulation. In addition, an active power control is proposed in which the speed of the prime mover of the self-excited squirrel cage induction generator is adjusted in such a way that the active power of the small synchronous generator is maintained at zero value. The proposed power controller, by creating the balance between the generated and the consumed active powers, improves the frequency regulation of the wind energy conversion system. The validity and effectiveness of the proposed structure are demonstrated through both simulation studies and experimental implementation. The obtained results confirm that the poor regulation of voltage and frequency, which is one of the major drawbacks of self-excited induction generator-based wind energy conversion systems, has been significantly mitigated by using the proposed method.

Frequency Regulation of Electric Vehicle Aggregator Considering User Requirements with Limited Data Collection

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.

A combined day-ahead and intraday optimal scheduling strategy considering a joint frequency regulation reserve scheme among wind, photovoltaic, and thermal power

Under the premise of establishing a certain reserve power for frequency regulation, a new energy power plant (NEPP) transformed by frequency regulation control can participate in system frequency regulation. Considering the problem of cooperation between multiple NEPPs for reserve power for frequency regulation, this article presents a joint frequency regulation reserve scheme spanning wind, photovoltaic, and thermal power. A group of NEPPs composed of all NEPPs in the system works in the state of dynamic stepped output derating and participates in system frequency regulation together with thermal power units. Based on the joint frequency regulation reserve scheme and considering that the accuracy of new energy forecast directly affects the frequency regulation effect of NEPPs, we propose a combined day-ahead and intraday scheduling strategy considering the joint frequency regulation reserve scheme and derive the relevant scheduling model. The improved IEEE RTS 24-bus system is used as the test system for calculation. The results of the calculations show that the proposed strategy can optimize the participation scheme of frequency regulation of new energy, reduce the power and economic losses of new energy caused by participation in frequency regulation, and improve the ability of the power system to accommodate the output power of new energy under the premise of ensuring that the steady-state frequency deviation does not exceed the allowable frequency limits.

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.