Automatic Generation Control Scheme Research Articles

Ancillary services from wind and solar energy in modern power grids: A comprehensive review and simulation study

Renewable energy sources like wind and solar have increased demand for surplus power capacity. The demand is primarily fueled by the growing impact of forecasting errors associated with these intermittent energy sources. Implementing advanced control methods for automatic generation control (AGC) is essential to integrate wind and solar power with conventional generation sources to balance the power system and reduce reliance on traditional reserves. Therefore, this paper comprehensively overviews solar and wind energy integration in the AGC framework to provide optimal grid ancillary services. Initially, the paper presents an overview of the basic equations used to integrate reserve power from the photovoltaic (PV) system by employing the de-loading strategy. Subsequently, a comprehensive review is conducted on integrating the PV system in AGC strategies to provide grid ancillary services. The study also analyzes the contribution of wind power in AGC services using relevant equations and past practices. The paper presents a real-time dynamic control strategy to optimize the dispatch of the AGC unit by integrating the operating reserves from wind energy systems in conjunction with thermal power systems. The study simulates an 8-bus, 5-machine model using the Dig-SILENT Power Factory. The findings reveal that utilizing operating reserves from wind power can significantly reduce large-scale forecasting errors in massively renewable energy resources (RES) integrated power systems, thereby ensuring the necessary system operational security and reducing the reliance on traditional generating units.

Suppressing the Vortex Rope Oscillation and Pressure Fluctuations by the Air Admission in Propeller Hydro-Turbine Draft Tube

For the purpose of automatic generation control (AGC), a portion of the propeller hydro-turbine units in China is adjusted to operate within a restricted range of 75%-85% load using computer-controlled AGC strategies. In engineering applications, it has been observed that when a propeller hydro-turbine unit operates under off-design conditions, a large-scale vortex rope would occur in the draft tube, leading to significant pressure fluctuations. Injecting air into the draft tube to reduce the amplitude of pressure fluctuations is a common practice, but its effectiveness has not been proven on propeller hydro-turbine units. In this study, a CFD model of a propeller hydro-turbine was established, and 15 cases with different guide vane openings (GVO, between 31° and 45°) under unsteady conditions were calculated and studied. Two air admission measures were introduced to suppress the vortex rope oscillation in the draft tube and to mitigate pressure fluctuations. The reason for the additional energy loss due to air admission was then explained by the entropy production theory, and its value was quantified. This study points out that when injecting air, it is necessary to first consider whether the air will obstruct the flow in the draft tube. Finally, based on simulation and experimental data under various load conditions, pressure fluctuation analysis (based on fast Fourier transform, FFT) was conducted to assess the effectiveness of air admission measures. This study can provide an additional option for balancing unit efficiency and stability when scheduling units using an AGC strategy.

Automatic Generation Control Strategy for Integrated Energy System Based on Ubiquitous Power Internet of Things

The integrated energy system based on Ubiquitous Power Internet of Things has the characteristics of ubiquitous connection of everything, complex energy conversion mode and unbalanced supply-demand relationship. It brings strong random disturbance to the power grid, which deteriorates the comprehensive control performance of automatic generation control. Therefore, a novel deep reinforcement learning algorithm, namely collaborative learning actor-critic strategy, is proposed. It is oriented to different exploration horizons, has the advantage on experience sharing mechanism and can continuously coordinate the key behavioral strategies. Simulation tests are performed on the two-area integrated energy system and the four-area integrated energy system based on ubiquitous power Internet of Things. Comparative analyses show that the proposed algorithm can efficiently solve the problem of strong random disturbance, and has better convergence characteristic and generalization performance. Besides, it can realize the optimal cooperative control of multi-area integrated energy system efficiently.

A Fully Non-Interactive Automatic Generation Control Strategy for Multi-Area Interconnected Power Systems

Frequency is an important index for the stable and secure operation of the power system. In the conventional methods, an inappropriate value of the frequency bias coefficient will lead to over-biasing or under-biasing of generators, causing unintended cross-regional interaction. This paper proposes a fully non-interactive automatic generation control strategy for power systems. Through the derivation of deviation of frequency and tie-line active power under disturbance, it’s found that the disturbance can be located from the deviation of frequency and active power of tie-line. Based on this, the conventional AGC control model is modified. With the proposed method, only the AGC units in disturbed areas respond to disturbances, and the AGC units in other areas will not act. Thus, the unintended cross-regional interaction in the conventional AGC control method is avoided. The proposed method only needs minor modification to the conventional AGC control model and hence is easy to implement for onsite application. The effectiveness of the proposed strategy is confirmed via simulation on the Kundur four-machine two-area system.

Automatic Generation Control Strategy Based on Deep Forest

As the scale of the power system continues to expand and the energy situation changes dramatically, the existing automatic generation control (AGC) strategy needs to be optimized and improved. The current grid AGC mainly adopts closed-loop PI control. By learning an excellent data set that incorporates the characteristics of PI control and DFT control, this paper proposes a real-time AGC strategy based on the deep forest network. The strategy selects the controller with better control performance in each assessment period as the controller for that assessment cycle for power deviation regulation studies. Simulation results show that the strategy can achieve real-time AGC regulation with a lower number of actions and outperform any of the learned strategies.

SSA‐optimized cascade optimal‐PIDN controller for multi‐area power system with RFB under deregulated environment

AbstractIn today's rapidly evolving interconnected electric power system network, ancillary service such as automatic generation control (AGC) plays a critical and important role in safeguarding the system's power supply quality by establishing an equilibrium between generation and load demanded plus losses. A power system with a robust and smart AGC strategy is obligatory for better power quality and system security. Cascade control schemes cope up better with nonlinearity by tuning the gain of controller of power system amid continuous load changing. Therefore, in this article, a supplementary control approach that is, cascade optimal controller‐proportional integral with derivative filter (COC‐PIDN) is suggested for AGC performance advancement of the power systems under deregulated environment. The OC designed consuming full state feedback control approach acts as master controller and PIDN plays the role of slave controller. The nature inspired salp swarm algorithm is employed to adjust the PIDN parameters. Initially, the efficacy of the COC‐PIDN control‐method is explored on a two‐area restructured reheat thermal system and then to validate its excellence and extensibility, the study is protracted to restructured two‐area thermal‐hydro system. Extensive analysis of the findings establishes that the dynamic performance of the COC‐PIDN controller is more valuable than the OC and COC‐PID approaches. The results of COC‐PIDN get further improved in presence of redox flow battery. Finally, the sensitivity analysis as well the acquired results with the COC‐PIDN controller with system nonlinearities demonstrate its robustness.

An AGC Dynamic Optimization Method Based on Proximal Policy Optimization

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.

Distributed cooperative automatic generation control and multi-event triggered mechanisms co-design for networked wind-integrated power systems

The participation of wind energy brings new challenges to the networked power systems. Now, more network nodes compete for limited communication resources. Considering the utilization efficiency of network transmission resources, a novel multi-event triggered mechanisms based distributed cooperative automatic generation control scheme is investigated for networked wind-integrated power systems (NWIPSs). The multi-event triggered mechanisms based distributed wind-integrated power systems modeling method is proposed. By utilizing the Lyapunov-Krasovskii functional method, the sufficient conditions for system asymptotical stability are obtained. Concerning the previous event-triggered method the proposed multi-event triggered method widespread considered the coupling relationship between interconnected subsystems and make full use of each subsystem state information. The numerical example shows that the proposed scheme is feasible and the design of multi-event triggered mechanisms based distributed cooperative automatic generation controller provides less conservatism and stronger robustness.

Study on AGC strategy involving the frequency control of HVDC power regulation

The asynchronous interconnection between the Southwest China power grid (SCPG) and Central China power grid (CCPG) reduces the inertia of rotation of SCPG to 1/5, which leads to a prominent risk of frequency stability. Moreover, in order to suppress the ultra-low frequency oscillations, the parameters of hydropower governors were optimized, which further worsen the frequency regulation capability of the system. Therefore, the frequency control (FC) of high voltage direct current (HVDC) line along with automatic generation control (AGC) are designed to improve the frequency restoration after a large power disturbance. However, the characteristic of FC regulation is contradictory to the subsequent AGC regulation. In order to improve the post-contingency frequency response of power system, an improved AGC strategy is developed. The proposed strategy involves the FC regulation power and distribute the control power according to the characteristics of different generator units. Case study on the actual model of SCPG proves the effectiveness of the strategy.

Recent Strategies for Automatic Generation Control of Power Systems with Diverse Energy Sources

This paper reveals Automatic Generation Control (AGC) strategies of power systems including diverse type power generating sources and comprehensive literature review is also presented. These diverse type energy sources considered conventional power sources like thermal, diesel, nuclear, etc. and Renewable Energy Sources (RESs). RESs are solar energy, wind energy, hydro energy, etc. A variety of AGC schemes based on hard, soft and artificial intelligent computation techniques is presented here. The benefits and their limitations of these energy generating units are also taken in this article. In the present scenario, deregulation, smart micro-grid and grid concept is also utilized with interconnection of the considered energy generating sources. The literature of this review article fulfills the gap of recent and previous decades research work and provides future exploration in AGC techniques.

Recent Strategies for Automatic Generation Control of Power Systems With Diverse Energy Sources

This paper reveals Automatic Generation Control (AGC) strategies of power systems including diverse type power generating sources and comprehensive literature review is also presented. These diverse type energy sources considered conventional power sources like thermal, diesel, nuclear, etc. and Renewable Energy Sources (RESs). RESs are solar energy, wind energy, hydro energy, etc. A variety of AGC schemes based on hard, soft and artificial intelligent computation techniques is presented here. The benefits and their limitations of these energy generating units are also taken in this article. In the present scenario, deregulation, smart micro-grid and grid concept is also utilized with interconnection of the considered energy generating sources. The literature of this review article fulfills the gap of recent and previous decades research work and provides future exploration in AGC techniques.

The multiagent cooperative AGC strategy for multiregional energy interconnection

<p indent=0mm>Traditional reinforcement learning cannot effectively solve the strong random disturbance problem caused by large-scale new energy access; therefore, the response speed of AGC (automatic generation control) becomes slower and the performance becomes poorer. This study considers the double Q learning that can solve the problem of overestimation of the value of state-action pairs in the reinforcement learning algorithm based on the Q framework as the fulcrum. It integrates a grey wolf optimization algorithm that can quickly search for the optimal solution in the unknown search space. Additionally, it proposes a multiagent cooperative AGC strategy for multiregional energy interconnection, namely, the GWDQ strategy, to quickly obtain the multiregional cooperative optimal solution in the AGC process. The two-area integrated energy system model, such as the hybrid-electric gas turbine system, CCHP and other forms of energy, and the northeast power grid model with a multiregional energy interconnection, are simulated. The results showed that the proposed strategy has a strong learning ability. Besides, the convergence speed and control performance are superior to that of the traditional reinforcement learning algorithm. The proposed strategy can quickly obtain the multiregional collaborative optimal solution in the AGC process.

Data-Driven Resilient Automatic Generation Control Against False Data Injection Attacks

With the advancement of communication technologies and the development of the smart grid, today's physical power systems present an ever-growing dependency on cyber resources. It increases cyber vulnerabilities, causing safety and system stability concerns. In this article, a data-driven resilient automatic generation control (AGC) scheme is proposed under a false data injection attack (FDIA). The key idea is to identify the relationship between AGC signals and system operational conditions. This is achieved by the proposed regression-based FDIA signal predictions, including sequence-to-point prediction and the long short-term memory network-based prediction. It allows us to reconstruct the AGC control signals without being affected by FDIAs and to attenuate attacks in the closed control loop, thus alleviating the impact of FDIA on system performance. Numerical results carried out on the benchmark systems validate the effectiveness of the proposed method.

Application of a New Fusion of Flower Pollinated With Pathfinder Algorithm for AGC of Multi-Source Interconnected Power System

As the world's population grows and energy demand increases, it is necessary to increase the scale of the electrical system, which is more complicated. Consequently, adopting automatic generation control (AGC) scheme to meet the demand becomes inevitable. In this article, the fusion of flower pollinated algorithm (FPA) and pathfinder algorithm (PFA), named hereafter as hFPAPFA, is proposed to achieve maximum control efficiency by combining the exploitation of FPA with the exploration capacity of PFA. The proposed hFPAPFA is meant to regulate two unequal multi-area interconnected power system with different generating units such as thermal, hydro, wind power and diesel plants. The proposed control scheme aims to achieve this by using the new algorithm to optimize the fractional-order set-point weighted PID (FOSWPID) parameters under time domain-based fitness functions namely, integral time square error (ITSE) and integral time absolute error (ITAE) while simultaneously minimizing the power losses. Employing the same interconnected power systems, a comparative study with some recent approaches in renowned journals is conducted. The performance of the proposed method is observed under diverse load conditions scenarios. Moreover, three nonlinearities including boiler dynamics, the governor dead band (GDB) and generation rate constraints (GRC) are further integrated into the system from a pragmatic context. Finally, sensitivity tests involving various parameter changes and the introduction of random step load perturbations are carried out. From the results, the proposed approach outperformed other approaches under different load condition scenarios, incorporation of nonlinearities and random load perturbation, demonstrating the proposed technique's efficacy and reliability.

Graphic RCRA‐PIDA tuning based on maximum sensitivity for automatic generation control of thermal and hydro power systems

The frequency control is the mandatory task in a modern power system because of load demand variation and the integration of renewable energy sources (RESs). The main objective of automatic generation control (AGC) scheme is to maintain the balance between generation and load demand. This study proposes a graphical robust characteristic ratio assignment (RCRA) approach for PIDA controller tuning based on maximum sensitivity ( M s ) for AGC. The basic feature of this graphical approach is that characteristic ratios ( γ i ) in RCRA scheme are calculated in terms of M s . The important part of this design is that the analytical expressions for coefficient gains of PIDA controller are derived in terms of γ i and system parameters. Here, the non-reheat steam-turbine and hydro-turbine based single and multi-area power systems are considered for validation of the proposed control scheme. The critical issue in controller design for a hydro-turbine based power system is the non-minimum phase behaviour. The robustness and performance of RCRA-PIDA controller are examined under system non-linearities, parametric uncertainty and disturbances. Further, the performance of proposed approach is evaluated in the presence of RESs. Finally, it is observed that the proposed control approach performs better in comparison to the recently published control schemes.

ANN Created Real Time Load Pattern Base Frequency Normalization Studies of Linked Electric Power System

In most of the automatic generation control (AGC) studies proposed so far, area control error (ACE) signal is derived considering fixed step size load disturbance which does not represent the real time operating condition of power system adequately and may cause sometimes the over regulation of the power system. Therefore, a very short-term load forecasting (STLF) using artificial neural network (ANN) is proposed to obtain a load disturbance pattern to derive an effective AGC scheme. Further, real time load data of a particular month are collected from a 220 kV substation and are used to perform STLF. The predicted hourly load is used to determine future load estimates considering a 10 minute interval basis. The ACE signal is derived accordingly. The model predictive control (MPC) based AGC scheme is designed to counter the upcoming load variations very effectively. A two-area power system having thermal power plants and interconnected via parallel AC/DC tie-lines is considered for the investigations. Furthermore, the dynamic performance of the designed control strategy is also evaluated considering the governor dead-band and generation rate constraint (GRC).

Integrating layered recurrent ANN with robust control strategy for diverse operating conditions of AGC of the power system

This study presents the structural, operational and control aspects of doubly fed induction generator (DFIG) based wind integrated power systems. The automatic generation control (AGC) of a meshed power system including DFIG-based wind turbines has been framed and investigations under various system perturbation are presented. The two-area system consisting of non-reheat thermal turbines with DFIG and interconnected through parallel AC/DC tie-lines is considered for the study. The system non-idealities such as governor lag and generation rate constraints are taken into consideration. An AGC strategy using a layered recurrent artificial neural network (ANN) is proposed in this work. The gains of the proposed AGC are obtained by effectively training the ANN using a set of reliable data obtained from a widespread range of operating system conditions using robust control strategy. The study also incorporates the design of AGC for the power system using the fuzzy logic concept and other AGC actions such as integral (I), proportional–integral (PI) and proportional–integral–derivative (PID) calculated via the means of particle swarm optimization (PSO). The results obtained with the proposed ANN created AGC are linked and demonstrated their superiority over fuzzy logic PI and traditional PSO-based I/PI/PID AGC strategies under numerous system operating conditions.

Contribution of GCSC to regulate the frequency in multi-area power systems considering time delays: A new control outline based on fractional order controllers

This paper addresses the application of the gate controlled series capacitor (GCSC) in coordination with automatic generation control (AGC) for providing better dynamic responses in interconnected multi-area power systems. A novel controller scheme called Hybrid controller for modeling the GCSC is proposed. The proposed control model consists of a fractional-order proportional-integral-derivative (FOPID) controller and a tilt-integral-derivative (TID) one. Optimized coefficients of the controllers are obtained by applying the Sine-Cosine algorithm (SCA). The delay in communication links as a common feature in wide area control of power systems may cause instability problem. So, a robust stability criterion is presented to estimate the maximum delay time which allows an AGC scheme embedded with the GCSC damping controller to retain stable. Comprehensive discussions are presented on a deregulated two-area power system and a traditional three-area one. Simulation results confirm that the proposed Hybrid controller is capable to achieve better dynamic responses and provide better coordination between the GCSC and AGC in comparison to the other coordinated controllers including PID-based GCSC-AGC, TID-based GCSC-AGC, and FOPID-based GCSC-AGC ones. Eventually, the sensitivity analysis is accomplished to assess the robustness property of the proposed controller against the variations of the parameters.

Flow Shop Providing Frequency Regulation Service in Electricity Market

Electricity cost is one of main production costs for flow shops. Providing frequency regulation services can help electric loads reduce their electricity costs. Previous studies mostly focus on automatic generation control (AGC) strategies for other types of electric loads, such as air conditioners, EVs or battery storage. In this paper, we find flow shops competent to follow regulation signals and avoid interrupts of processing with the help of scheduling optimization. This finding may be an aid for flow shops by availing regulation services to the market and making a profit. Hence, we propose an AGC strategy for optimizing flow shop scheduling, without affecting the operation. To formulate the bidding strategy for flow shops in regulation market, we considered as many relevant factors as possible, including the regulation performance and yield of flow shops, constraints on load power, regulation reserve capacity and machines operation, inventory of each semi-finished product, AGC strategy—as well as the coupling between the bids in both energy market and regulation market. Our case study shows the potential of the methodology proposed in this paper to cut down the electric cost of flow shops and supplies of performance-qualified frequency regulation service.

Multi-Area Automatic Generation Control Scheme Considering Frequency Quality in Southwest China Grid: Challenges and Solutions

Southwest China Grid (SCG) is one of the hydropower-rich power grids around the world. SCG has recently been into asynchronous operation mode, it is expected to face challenges in low inertia and frequency stability issues caused by the hydraulic turbine governing system (HTGS), which brings profound changes to the validity and stability for the multi-area automatic generation control (AGC) power systems. This article analyzes the frequency control problems of SCG according to the operation data from asynchronous operation tests. To overcome the large overshoot and reverse regulation for external disturbances, frequency-based segment values of frequency bias for areas under TBC mode are developed considering multi-deadband effects, and a switching method of control modes for supporting large-disturbance recovery is proposed. Based on the complex and nonlinear of AGC systems, a fuzzy PD+I controller is proposed to reduce the excessively large overshoot. Furthermore, the rule for tuning D gain is established by the sign of change of area control error (ACE), and flexible I-part is applied to the control process based on the relationship between ACE and frequency. Then, we also provide filter-based control strategies with adaptive parameters to effectively coordinate hydropower and thermal power generators. The filter coefficients are computed considering various delays within AGC systems. Some case studies and actual operating conditions demonstrate the efficiency and accuracy of the proposed ideas compared with current control strategies.