System Frequency Regulation Research Articles

Frequency regulation of PV-reheat thermal power system via a novel hybrid educational competition optimizer with pattern search and cascaded PDN-PI controller

Maintaining a stable balance between generated power and load demand is a critical challenge in modern power systems, especially with the increasing integration of renewable energy sources like photovoltaic (PV) systems. This study introduces a novel hybrid educational competition optimizer with pattern search (hECO-PS) algorithm to optimally tune a cascaded proportional-derivative with filter and proportional-integral (PDN-PI) controller for load frequency control (LFC) in a two-area power system comprising a PV system and a reheat thermal power system. The proposed hECO-PS algorithm enhances both global exploration and local exploitation capabilities, resulting in superior convergence rates and solution accuracy. The controller's performance was evaluated under various scenarios, including a 10% step load change and solar radiation variations, demonstrating significant improvements in frequency regulation. The hECO-PS tuned PDN-PI controller achieved a minimum integral of time-weighted absolute error (ITAE) value of 0.4464, outperforming conventional methods like the modified whale optimization algorithm and sea horse algorithm, which yielded ITAE values of 2.6198 and 0.8598, respectively. Furthermore, the proposed controller reduced settling time by up to 46% and minimized overshoot by up to 40%. These results confirm the efficacy of the proposed approach in enhancing system stability and reliability under dynamic operating conditions, suggesting it as a promising solution for LFC in modern power systems with high renewable energy penetration.

The study of active support for energy storage system in system frequency regulation

Abstract As the penetration of new energy sources increases, the proportion of traditional energy units continues to decline, reducing the inertia and primary frequency regulation capability of the power system. Therefore, exploring the frequency support capabilities of energy storage systems with low inertia is crucial. Based on this, this paper first proposes an active frequency support method for energy storage system based on a Voltage Source Virtual Synchronous Generator (VSG), aiming to enhance system inertia and reduce response delay. Based on this issue, a parameter tuning optimization selection model based on the Transient Search Optimization (TSO) algorithm is proposed to determine the optimal power output and best frequency regulation parameters for the energy storage system. Finally, the model proposed in this paper is applied to the IEEE 14-bus system to verify its effectiveness.

Towards non-virtual inertia control of renewable energy for frequency regulation: Modeling, analysis and new control scheme

Currently, when renewable generation participates in frequency regulation, the traditional control method is to emulate synchronous generators through virtual inertia control. However, virtual inertia has a time delay, so essentially, it is a fast power response. Meanwhile, virtual inertia control is likely to be affected by frequency fluctuation since it responds to the derivative of frequency. Hence, it’s worth exploring non-virtual inertia control for renewable energy when participating in frequency regulation. For this reason, a novel two-segment droop control scheme for renewable energy frequency regulation is proposed in this research. Firstly, the extended system frequency regulation (SFR) model, which contains virtual inertia with time delay, is built and analytically solved by order decrement based on the Routh approximation method. Afterwards, according to the analytical solution, time delay affects the frequency response of renewable energy. It can also be analytically proved that the non-virtual inertia control, e.g., sole droop control, could replace virtual inertia under the same frequency deviation. Still, more energy may be needed for frequency regulation. Furthermore, a novel two-segment droop control is presented, and to analytically prove its ability to replace virtual inertia, the impulse function balancing principle and the integration by parts algorithm were adopted to address the initial conditions of the differential equation. Based on the analytical expression, it can be analytically proved that a lower frequency deviation can be obtained under the same frequency regulation energy. Accordingly, a parameter-setting method for two-segment droop control was proposed. Finally, the effectiveness of the proposed method is verified by using a two-area system frequency response model, and the results reveal that it can be used to replace virtual inertia and has better performance.

Improved frequency regulation of dual-area hybrid power system with the influence of energy storage devices

Improved frequency regulation of dual-area hybrid power system with the influence of energy storage devices

A NoisyNet deep reinforcement learning method for frequency regulation in power systems

AbstractThis study thoroughly investigates the NoisyNet Deep Deterministic Policy Gradient (DDPG) for frequency regulation. Compared with the conventional DDPG method, the suggested method can provide several benefits. First, the parameter noise will explore different strategies more thoroughly and can potentially discover better policies that it might miss if only action noise were used, which helps the actor achieve an optimal control strategy, resulting in enhanced dynamic response. Second, by employing the delayed policy update policy work with the proposed framework, the training process exhibits faster convergence, enabling rapid adaptation to changing disturbances. To substantiate its efficacy, the scheme is subjected to simulation tests on both an IEEE three‐area power system, an IEEE 39 bus power system, and an IEEE 68 bus system. A comprehensive performance comparison was performed against other DDPG‐based methods to validate and evaluate the performance of the proposed LFC scheme.

High proportion of new energy power system frequency and voltage support and regulation technology based on doubly fed induction generator

Abstract With the increasing proportion of new energy in power systems, issues regarding system voltage, inertia, and primary frequency regulation have become increasingly severe. In response to control and grid safety issues, a high proportion of new energy power system frequency and voltage support and regulation technology based on a doubly fed induction generator (DFIG) is proposed. The voltage-magnetic link equation of DFIG is theoretically derived, and the control principles of active and reactive power of DFIG are proposed. The support and regulation characteristics of improving the voltage of new energy substations are studied. Verification is conducted through large-scale grid simulation and on-site measurements.

Convex-Hull Pricing of Ancillary Services for Power System Frequency Regulation With Renewables and Carbon-Capture-Utilization-and-Storage Systems

Convex-Hull Pricing of Ancillary Services for Power System Frequency Regulation With Renewables and Carbon-Capture-Utilization-and-Storage Systems

Frequency Support Coordinated Control Strategy of Renewable Distributed Energy Resource Based on Digital Twins

The integration of high-penetration renewable energy sources is playing an increasingly important role in the frequency regulation of the power system. However, due to the varying output characteristics and response speeds of different renewable distributed energy resources (DERs), the overall response from the collective output of these distributed energy resources may not meet expected requirements and could have adverse effects on the grid. One drawback of traditional distributed coordinated control is its high communication requirements. This paper proposes using digital twin (DT) technology for the coordinated control of distributed energy resources, which can minimize the communication needs between various distributed energy resources while achieving coordinated control. Verification of the frequency support coordinated control strategy based on digital twin technology shows that it can effectively enhance the individual output characteristics and overall response of each distributed energy resource, providing effective support for grid frequency.

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.

Dual-layer control strategy based on economic characterization of lifetime state and frequency regulation limit partition of hybrid energy storage

Compared with a single type of energy storage, hybrid energy storage system (HESS) has a better performance in improving the frequency safety of the grid. However, the combination of hybrid energy storage with different resource characteristics and thermal power units will significantly increase the difficulty of coordinated control. In view of the life decay of battery energy storage system (BESS) and the insufficient frequency regulation capability of the system, this paper proposes a dual-layer control strategy based on the economic characterization of hybrid energy storage life state and the frequency regulation limit partition. Real-time state of charge (SOC) is used to establish dynamic equilibrium quotation and energy storage performance characterization as the upper-layer model. Meanwhile, the optimal life evaluation of energy storage is proposed, and the charging and discharging switching model of energy storage is constructed to limit the frequent switching of energy storage and extend its life. The lower-layer model constructs the limit standard of frequency regulation of flywheel energy storage system (FESS), introduces multi-objective constraints, proposes a hybrid energy storage operation scheme suitable for the whole scene, and uses “two rules” as the evaluation index to evaluate the frequency regulation effect of the proposed frequency regulation control model from the regulation rate, accuracy and response time. Finally, taking the actual data of a local thermal power unit as an example, the validity and economy of the frequency regulation effect of the proposed model are verified.

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.

Consumer Theory-Based Primary Frequency Regulation in Multi-Microgrid Systems within a P2P Energy Management Framework

This paper presents a novel primary frequency regulation strategy for multi-microgrid (MMG) systems, utilizing consumer theory within a peer-to-peer (P2P) energy management framework. By coordinating photovoltaic (PV) systems and energy storage systems (ESS), the proposed method ensures a rapid and effective response to frequency deviations. Unlike conventional approaches, this strategy minimizes the curtailment of renewable energy sources by prioritizing the use of ESS, allowing excess energy to charge the ESS for later use during under-frequency events. This not only enhances energy efficiency but also maximizes renewable energy utilization. Simulations demonstrate that the proposed scheme achieves lower frequency deviations and faster stabilization compared to traditional droop and virtual inertia methods. These results highlight the potential benefits of integrating consumer theory-based models into primary frequency regulation, significantly enhancing system stability and efficiency in power systems with high levels of renewable energy penetration.

A novel disturbance observer–based integral sliding mode control method for frequency regulation in power systems

A novel disturbance observer–based integral sliding mode control method for frequency regulation in power systems

A SSA-based CFFOPID drop deloaded tidal turbine controller using HVDC-link

In contemporary scenario, electric power companies have observed upsurge in penetration level of tidal power plants (TPPs) in the traditional electric power system framework. However, the tidal turbines offer less frequency assistance due to their lesser rotor mass. Hence, TPPs may be collaborated with conventional units like diesel engine generator (DEG) to confirm system frequency stability in multi-area micro-grid system. The DEG comprises of primary and proportional integral derivative (PID) secondary frequency controls. However, in TPPs, to advance the system frequency regulation, deloading control approach is suggested and a cascade fuzzy fractional order PID-ID with derivative filter (CFFOPID-IDF) droop controller is suggested in place of the conventional non-cascade controller droop in the deloaded region. The suggested controller gains are fetched exploiting Salps swarm algorithm. For further enhancement of the dynamic responses, a precise high voltage direct current (AHVDC) link with the inertia emulation-based control (INEC) scheme is adopted, which allows the utilization of the gathered energy from the capacitance of the HVDC interface for frequency regulation. It provides better results compared to conventional AC tie line interface having less undershoot (34 %/20.63 %/43.75 %) and settling time (20.45 %/59.09 %/16.83 %) for variation in area-1 frequency/area-2 frequency/tie line power, respectively. The recommended control scheme is evidenced superior over numerous existing control techniques and provides least cost function in contrast to other control techniques. Additionally, it offers a highly stable performance under variable load conditions.

Frequency stability of new energy power systems based on VSG adaptive energy storage coordinated control strategy

A self-adaptive energy storage coordination control strategy based on virtual synchronous machine technology was studied and designed to address the oscillation problem caused by new energy units. By simulating the characteristics of synchronous generators, the inertia level of the new energy power system was enhanced, and frequency stability optimization was achieved. This strategy is integrated with the frequency response model of the new energy power system to improve the system's frequency regulation capability and achieve more stable and efficient operation. From the results, the damping of the system increased, the oscillation frequency decreased after a duration of about 15 s, and the system stability improved by 76.09%. The proposed strategy based on virtual synchronous generator adaptive energy storage coordination control strategy was improved by 83.25%. In addition, the proposed strategy has improved stability indicators and system completion efficiency by 40.57% and 22.21% respectively, both of which are better than the comparative strategies. As a result, this strategy significantly enhances the frequency regulation capability of the system, which has a positive effect on achieving efficient operation of the new energy power system and maintaining the stability of the power system.

Evaluation of primary frequency regulation in Dominican Republic mine using scooptrams and electric trucks as battery storage systems

The use of electric vehicles in the mining sector aligns with the growing demand for tools that reduce energy consumption in productive processes. This study investigates how electric mining vehicles with vehicle-to-grid (V2G) technology can be integrated into power grids to improve system stability and frequency regulation. In the context of mining operations and the provision of auxiliary services (such as primary frequency regulation) to an interconnected electrical system, a regulatory framework that enables V2G integration should be established. This issue is addressed through a case study involving DIgSILENT PowerFactory simulations. The goal is to evaluate the productive process of a mine and the contribution of V2G technology to the primary frequency regulation of the electrical system under different scooptram integration parameters and battery electric trucks. This study also explores the challenges of V2G implementation in the absence of a regulatory framework. Different configurations of electric scooptrams and shovel that can affect frequency regulation are assessed. The obtained optimal configuration involves two truck loaders and three shovel loaders; the capacity of the current electrical infrastructure can support this configuration without affecting the capacity of the mine's electrical system. The simulations verify that applying V2G technology to mining vehicles helps stabilise the frequency of electrical systems and reduces the probability of component failure.

Quantum model prediction for frequency regulation of novel power systems which includes a high proportion of energy storage

As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge–discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional–integral–derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy.

Real-time V2G simulator for grid system frequency and voltage regulation with fault ride-through capabilities

Electric vehicles (EVs) have become one of the promising power grid ancillary services. They can help reduce the intermittent of renewable energy sources by supporting active power and frequency control services. Additionally, bi-directional chargers included into EV batteries can supply/consume reactive power for voltage regulation. To improve voltage quality, a unique control approach is devised to offer fault ride-through. In this study, real-time charging optimization schemes based on particle swarm optimization are developed to coordinate the EVs charging/discharging power with the grid power dispatches depending on the charging priority and preferential electricity prices. Five different car user profiles including different initial state of charge and plug in state have been depicted. Different optimum issues are carried out with comprehensive simulations employing hardware in the loop via real-time simulation for a modified IEEE 14-bus test system. Where different frequency and voltage controls, management strategies, variant fault scenarios, benefits, and challenges for power system regulation with EVs are illustrated. According to the simulation results, the proposed optimized charging strategies reduce the total expenses of EV parking station by 40 % compared to other cases, hence maximizing EV owner satisfaction. The net load curve is smoothed and reduced by 11 %, particularly during peak period.

A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.

An optimal operation strategy of wind farm for frequency regulation reserve considering wake effects

When wind farms (WFs) participate in power system frequency regulation, deloaded control can increase the stored rotational kinetic energy in the wind turbines (WTs), thereby enhancing their frequency support capability. However, due to the wake effects and deloaded control of turbines, this method can also lead to increased power losses. An optimal operation strategy has been proposed to balance power generation and frequency regulation reserve in WFs, taking wake effects into account. Initially, a WF deloaded control model that considers wake effects, and an adaptive combined frequency control model based on kinetic energy reserve, are established for participation in power system frequency regulation. Subsequently, the Covariance Matrix Adaptation Evolution Strategy (CMAES) is utilized to determine the optimal operation allocation of maximum WF output power within the FLORIS wake model, meeting frequency regulation reserve requirements. A comparative analysis of the optimal allocation results under various wind conditions is conducted to test the frequency support capabilities. The results indicate that the proposed strategy can achieve varying levels of frequency regulation reserve, reduce wake losses to maximize output power under deloaded control, and improve frequency support capability.