Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonli...Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf_(1) and 4.98s for Δf_(2),marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively.展开更多
Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy...Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.展开更多
The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment suc...The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.展开更多
A hybrid Stochastic Fractal Search plus Pattern Search （hSFS-PS） based cascade PI-PD controller is suggested in this paper for Automatic Generation Control （AGC） of thermal, hydro and gas power unit based power sy...A hybrid Stochastic Fractal Search plus Pattern Search （hSFS-PS） based cascade PI-PD controller is suggested in this paper for Automatic Generation Control （AGC） of thermal, hydro and gas power unit based power systems in presence of Plug in Electric Vehicles （PEV）. Firstly, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral （I） controller is optimized by Stochastic FractaI Search （SFS） algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, differential evolution and teaching learning based optimization techniques is demonstrated by comparing simulation results for the identical power system. To improve the system performance further, Pattern Search （PS） is subsequently employed. The study is further extended for different controllers like PI, PID, and cascaded PI-PD controller and the superiority of cascade PI-PD controller over conventional controllers is demonstrated. Then, cascade PI- PD controller parameters of AGC searched using the proposed hSFS-PS algorithm in presence of plug in electric vehicles. The study is also extended to an interconnected power system. It is seen from the comparative analysis that hSFS-PS tuned PI-PD controller in single and multi-area with multi sources improves the system frequency stability in complicated situations. Lastly, a three area interconnected system with PEVs with dissimilar cascade PI-PD controller in each area is considered and proposed hSFS- PS algorithm is used to tune the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay.展开更多
The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative ...The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power grids.The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples.This can iteratively update the agents through buffer replay and real-time data.Thus the environmental data can be fully used to enhance the learning speed of the agents.This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance.Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm.The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.展开更多
Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-a...Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-attacks are identified and cordoned off,they may lead to black-out and result in national security issues.This paper proposes an optimal two-stage Kalman filter(OTS-KF)for simultaneous state and cyber-attack estimation in automatic generation control(AGC)system.Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics.Five types of cyber-attacks,i.e.,false data injection(FDI),data replay attack,denial of service(DoS),scaling,and ramp attacks,are injected into the measurements and estimated using OTS-KF.As the load variations of each area are seldom available,OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system.The proposed technique is validated on the benchmark two-area,three-area,and five-area power system models.The simulation results under various test conditions demonstrate the efficacy of the proposed filter.展开更多
The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the...The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the consensus Qlearning(MSGP-CQ)strategy is proposed in this paper,which is an automatic generation control(AGC)for distributed energy incorporating multiple-step greedy attribute and multiple-level allocation strategy.The convergence speed and learning efficiency in the MSGP algorithm are accelerated through the predictive multiple-step iteration updating in the proposed strategy,and the CQ algorithm is adopted with collaborative consensus and selflearning characteristics to enhance the adaptability of the power allocation strategy under the strong stochastic disturbances and obtain the total power commands in the power grid and the dynamic optimal allocations of the unit power.The simulations of the improved IEEE two-area load-frequency control(LFC)power system and the interconnected system model of intelligent distribution network(IDN)groups incorporating a large amount of distributed energy show that the proposed strategy can achieve the optimal coordinated control and power allocation in the power grid.The algorithm MSGP-CQ has stronger robustness and faster dynamic optimization speed and can reduce generation costs.Meanwhile it can also solve the strong stochastic disturbance caused by large-scale distributed energy access to the grid compared with some existing intelligent algorithms.展开更多
As synchronous generators(SGs)are gradually displaced by renewable energy sources(RESs),the frequency stability of power systems deteriorates because RESs,represented by utility-scale solar and wind power sources,do n...As synchronous generators(SGs)are gradually displaced by renewable energy sources(RESs),the frequency stability of power systems deteriorates because RESs,represented by utility-scale solar and wind power sources,do not provide the inertial response,primary frequency response,secondary frequency response,and tertiary frequency regulation.As a result,the remaining SGs may not be sufficient to maintain the power balance and frequency stability.The concept and control strategies of virtual synchronous generators(VSGs)enable the inverter-based wind and solar power sources to emulate the outer characteristics of traditional SGs and participate in the active power and frequency control of power systems.This paper focuses on the automatic generation control(AGC)with virtual synchronous renewables(VSRs).First,the VSR strategy that enables the RESs to participate in AGC is introduced.Second,based on the interval representation of uncertainty,the output of RES is transformed into two portions,i.e.,the dispatchable portion and the stochastic portion.In the dispatchable portion,the RESs can participate in AGC jointly with SGs.Accordingly,a security-constrained economic dispatch(SCED)model is built considering the RESs operating in VSR mode.Third,the solution strategy that employs the slack variables to acquire deterministic constraints is introduced.Finally,the proposed SCED model is solved based on the 6-bus and 39-bus systems.The results show that,compared with the maximum power point tracking(MPPT)mode,VSRs can participate in the active power and frequency control jointly with SGs,increase the maximum penetration level of RESs,and decrease the operating cost.展开更多
Newly proposed power system control methodologies combine economic dispatch(ED) and automatic generation control(AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a...Newly proposed power system control methodologies combine economic dispatch(ED) and automatic generation control(AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a real power system is subjected to continuous demand disturbance and system constraints due to the input saturation, communication delays and unmeasurable feed-forward load disturbances. Therefore, optimizing the dynamic response under practical conditions is equally important. This paper proposes a state constrained distributed model predictive control(SCDMPC)scheme for the optimal frequency regulation of an interconnected power system under actual operation conditions, which exist due to the governor saturation, generation rate constraints(GRCs), communication delays, and unmeasured feed-forward load disturbances. In addition, it proposes an algorithm to handle the solution infeasibility within the SCDMPC scheme, when the input and state constraints are conflicting. The proposed SCDMPC scheme is then tested with numerical studies on a three-area interconnected network. The results show that the proposed scheme gives better control and cost performance for both steady state and dynamic state in comparison to the traditional distributed model predictive control(MPC) schemes.展开更多
Particle swarm optimization(PSO)is one of the popular stochastic optimization based on swarm intelligence algorithm.This simple and promising algorithm has applications in many research fields.In PSO,each particle can...Particle swarm optimization(PSO)is one of the popular stochastic optimization based on swarm intelligence algorithm.This simple and promising algorithm has applications in many research fields.In PSO,each particle can adjust its‘flying’according to its own flying experience and its companions’flying experience.This paper proposes a new PSO variant,called the statistically tracked PSO,which uses group statistical characteristics to update the velocity of the particle after certain iterations,thus avoiding localminima and helping particles to explore global optimum with an improved convergence.The performance of the proposed algorithm is tested on a deregulated automatic generation control problem in power systems and encouraging results are obtained.展开更多
This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three di...This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1 + PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1 + PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.展开更多
In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically ...In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge(SoC)of energy storage units,this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units.First,a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process.Next,a control framework based on variable integral coefficients is proposed within the secondary frequency regulation model,along with an objective function that simultaneously considers both Automatic Generation Control(AGC)command tracking performance and SoC recovery requirements of energy storage units.Finally,a gradient descent optimization method is used to dynamically adjust the gain of the energy storage integral controller,allowingmultiple energy storage units to respond in real-time to AGC instructions and SoC variations.Simulation results confirmthe effectiveness of the proposedmethod.Compared to traditional strategies,the proposed approach takes into account the SoCdiscrepancies amongmultiple energy storage units and the duration of system net power imbalances.It successfully implements secondary frequency regulation while achieving dynamic power allocation among the units.展开更多
This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form,Quasi Oppositional Sine Cosine Algorithm,to automatic generation control of a multipl...This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form,Quasi Oppositional Sine Cosine Algorithm,to automatic generation control of a multiple-source-based interconnected power system that consists of thermal,gas,and hydro power plants.The Proportional-Integral-Derivative controller,which is utilized for automated generation control in an interconnected hybrid power systemwith aDClink connecting two regions,has been tuned using the proposed optimization technique.An Electric Vehicle is taken into consideration only as an electrical load.The Quasi Oppositional Sine Cosinemethod’s performance and efficacy have been compared to the Sine Cosine Algorithm and optimal output feedback controller tuning performance.Applying the QOSCA optimization technique,which has only been shown in this study in the context of an LFC research thus far,makes this paper unique.The main objective has been used to assess and compare the dynamic performances of the recommended controller along with QOSCA optimisation technic.The resilience of the controller is examined using two different system parameters:B(frequency bias parameter)and R(governor speed regulation).The sensitivity analysis results demonstrate the high reliability of the QOSCA algorithm-based controller.Once optimal controller gains are established for nominal conditions,step load perturbations up to±10%&±25%in the nominal values of the systemparameters and operational load condition do not require adjustment of the controller.Ultimately,a scenario is examined whereby EVs are used for area 1,and a single PID controller is used rather than three.展开更多
In this paper, a fractional order proportional integral derivative （FOPID） controller for multiarea automatic generation control （AGC） scheme has been designed. FOPID controller has five parameters and provides tw...In this paper, a fractional order proportional integral derivative （FOPID） controller for multiarea automatic generation control （AGC） scheme has been designed. FOPID controller has five parameters and provides two additional degrees of flexibility in comparison to a proportional integral derivative （PID） controller. The optimal values of parameters of FOPID controller have been determined using Big Bang Big Crunch （BBBC） search algorithm. The designed controller regulates real power output of generators to achieve the best dynamic response of frequency and tie-line power on a load perturbation. The complete scheme for designing of the controllers has been developed and demonstrated on multiarea deregulated power system. The performance of the designed FOPID controllers has been compared with the optimally tuned PID controllers. It is observed from the results that the FOPID controller shows a considerable improvement in the performance as compared to the conventional PID controller.展开更多
Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid...Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid planning and construction, and will make a heavy impact on the safe and reliable operation of power systems. To deal with the diff iculties of large scale wind power dispatch, this paper presents a new automatic generation control (AGC) scheme that involves the participation of wind farms. The scheme is based on ultra-short-term wind power forecast. The author establishes a generation output distribution optimization mode for the power system with wind farms and verif ies the feasibility of the scheme by an example.展开更多
The significant increase in the proportion of renewable energy sources(RESs)has elevated risks of extreme ramp events and frequency instability in power systems.In recent years,frequency stability events have occurred...The significant increase in the proportion of renewable energy sources(RESs)has elevated risks of extreme ramp events and frequency instability in power systems.In recent years,frequency stability events have occurred in several countries/regions worldwide due to flexibility deficiencies.Generation flexibility has emerged as a critical factor influencing the frequency stability of power systems.This paper proposes a domain of attraction(DOA)-based quantitative method to assess the frequency stability region of power systems with a high proportion of RESs,considering generation flexibility constraints.First,ramp rate is adopted as the core indicator to characterize generation flexibility within automatic generation control(AGC)timescale,through which a nonlinear AGC model with rate saturation constraints is established.Second,the concept of DOA is introduced to define the stability region of the nonlinear AGC.Third,a quadratic Lyapunov-based estimation method is employed to quantitatively analyze the DOA of the nonlinear AGC at different generation flexibility levels.Simulation results demonstrate that increased generation flexibility expands the estimated DOA of the nonlinear AGC,whereas generation flexibility deficiency induces AGC instability.Moreover,state trajectory and time-domain simulation verify that the proposed estimation method accurately represents the stability region of the nonlinear AGC.展开更多
Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this r...Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this regard,AGC system based on fuzzy logic,i.e.,so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system.Apart from that,simultaneous coordination scheme based on particle swarm optimization(PSO)along with real coded genetic algorithm(RCGA)is suggested to coordinate FLAGCs of the all areas.To clarify the high efficiency of aforementioned strategy,two different interconnected multi-area power systems,i.e.,three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies.The potency of this strategy has been thoroughly dealt with by considering the step load perturbation(SLP)in both the under study power systems.To sum up,the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC(CAGC)in each power system in order to damp out the power system oscillations.展开更多
The large-scale popularization of electric vehicles(EVs)brings the potential for grid frequency regulation.Considering the characteristics of fast response and adjustment of EVs,two control strategies of automatic gen...The large-scale popularization of electric vehicles(EVs)brings the potential for grid frequency regulation.Considering the characteristics of fast response and adjustment of EVs,two control strategies of automatic generation control(AGC)with EVs are proposed responding to two high frequency regulating signals extracted from area control error(ACE)and area regulation requirement(ARR)by a digital filter,respectively.In order to dispatch regulation task to EVs,the capacity of regulation is calculated based on maximum V2G power and the present V2G power of EVs.Finally,simulations based on a two-area interconnected power system show that the proposed approaches can significantly suppress frequency deviation and reduce the active power output of traditional generation units.展开更多
Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic genera...Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control(AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems(FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator(TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC(HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization(MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.展开更多
This paper investigates automatic generation control(AGC)of a realistic hybrid four-control area system with a dis-tinct arrangement of thermal units,gas units and additional power generation.A proportional-integral-d...This paper investigates automatic generation control(AGC)of a realistic hybrid four-control area system with a dis-tinct arrangement of thermal units,gas units and additional power generation.A proportional-integral-double deriva-tive cascaded with proportional-integral(PIDD-PI)controller is employed as secondary controller in each control area for robust restructured AGC considering bilateral transactions and contract violations.The Harris Hawks algorithm is used to determine the optimal controller gains and system parameters under several scenarios.Electric vehicle(EV)aggregators are employed in each area to participate fully along with thermal and gas units to compensate for the unscheduled system demand in the local area.A comparison of non-cascaded controllers such as PI-PD,PD-PID and the proposed PIDD-PI proves the superiority of the last.The effect of the decline in inertia is closely examined because of the sudden outage of a generating unit while at the same time considering the change in area frequency response characteristics and area control error.EV fleets make significant contributions to improving the system dynamics dur-ing system inertia loss.The use of EVs in the presence of a wind energy-supported grid can provide a stable efficacy to the power grid.Numerous simulations with higher load demands,stochastic communication delays in presence of the WTG plant,and violations in system loadings and changes in gas turbine time constants in the absence of WTG demonstrate the robustness of the proposed control approach.展开更多
基金supported in part by the Science and Technology Innovation Program of Hunan Province under Grant 2022RC4028in part by the National Natural Science Foundation of China under Grant 62473204+3 种基金in part by the Chunhui Program Collaborative Scientific Research Project under Grant 202202004in part by the Natural Science Foundation of Nanjing University of Posts and Telecommunications under Grants NY221082,NY222144,and NY223075in part by the Huali Program for Excellent Talents in Nanjing University of Posts and Telecommunicationsin part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX24_1215.
文摘Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf_(1) and 4.98s for Δf_(2),marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively.
基金supported in part by the National Natural Science Foundation of China under Grant 61873130in part by the Chunhui Program Collaborative Scientific Research Project under Grant 202202004+4 种基金in part by the Foundation of the Key Laboratory of Industrial Internet of Things and Networked Control of the Ministry of Education of China under Grant 2021FF01in part by the Natural Science Foundation of Nanjing University of Posts and Telecommunications under Grant NY221082,Grant NY222144,and Grant NY223075in part by the Huali Program for Excellent Talents in Nanjing University of Posts and Telecommunicationsin part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grantin part by the Fundamental Research Funds for the Central Universities under WUT:104972024KFYjc0072.
文摘Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.
基金supported by Theoretical study of power system synergistic dispatch National Science Foundation of China(51477091).
文摘The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.
文摘A hybrid Stochastic Fractal Search plus Pattern Search （hSFS-PS） based cascade PI-PD controller is suggested in this paper for Automatic Generation Control （AGC） of thermal, hydro and gas power unit based power systems in presence of Plug in Electric Vehicles （PEV）. Firstly, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral （I） controller is optimized by Stochastic FractaI Search （SFS） algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, differential evolution and teaching learning based optimization techniques is demonstrated by comparing simulation results for the identical power system. To improve the system performance further, Pattern Search （PS） is subsequently employed. The study is further extended for different controllers like PI, PID, and cascaded PI-PD controller and the superiority of cascade PI-PD controller over conventional controllers is demonstrated. Then, cascade PI- PD controller parameters of AGC searched using the proposed hSFS-PS algorithm in presence of plug in electric vehicles. The study is also extended to an interconnected power system. It is seen from the comparative analysis that hSFS-PS tuned PI-PD controller in single and multi-area with multi sources improves the system frequency stability in complicated situations. Lastly, a three area interconnected system with PEVs with dissimilar cascade PI-PD controller in each area is considered and proposed hSFS- PS algorithm is used to tune the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay.
基金supported by the National Natural Sci-ence Foundation of China(No.52277108)Guangdong Provincial Department of Science and Technology(No.2022A0505020015).
文摘The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power grids.The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples.This can iteratively update the agents through buffer replay and real-time data.Thus the environmental data can be fully used to enhance the learning speed of the agents.This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance.Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm.The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.
文摘Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-attacks are identified and cordoned off,they may lead to black-out and result in national security issues.This paper proposes an optimal two-stage Kalman filter(OTS-KF)for simultaneous state and cyber-attack estimation in automatic generation control(AGC)system.Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics.Five types of cyber-attacks,i.e.,false data injection(FDI),data replay attack,denial of service(DoS),scaling,and ramp attacks,are injected into the measurements and estimated using OTS-KF.As the load variations of each area are seldom available,OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system.The proposed technique is validated on the benchmark two-area,three-area,and five-area power system models.The simulation results under various test conditions demonstrate the efficacy of the proposed filter.
基金This work was supported in part by the National Natural Science Foundation of China(No.51707102).
文摘The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the consensus Qlearning(MSGP-CQ)strategy is proposed in this paper,which is an automatic generation control(AGC)for distributed energy incorporating multiple-step greedy attribute and multiple-level allocation strategy.The convergence speed and learning efficiency in the MSGP algorithm are accelerated through the predictive multiple-step iteration updating in the proposed strategy,and the CQ algorithm is adopted with collaborative consensus and selflearning characteristics to enhance the adaptability of the power allocation strategy under the strong stochastic disturbances and obtain the total power commands in the power grid and the dynamic optimal allocations of the unit power.The simulations of the improved IEEE two-area load-frequency control(LFC)power system and the interconnected system model of intelligent distribution network(IDN)groups incorporating a large amount of distributed energy show that the proposed strategy can achieve the optimal coordinated control and power allocation in the power grid.The algorithm MSGP-CQ has stronger robustness and faster dynamic optimization speed and can reduce generation costs.Meanwhile it can also solve the strong stochastic disturbance caused by large-scale distributed energy access to the grid compared with some existing intelligent algorithms.
基金supported by the Research and Application of Key Technologies of Flexible Power Supply System Under Various Emergency Scenarios(No.5442PD210001)。
文摘As synchronous generators(SGs)are gradually displaced by renewable energy sources(RESs),the frequency stability of power systems deteriorates because RESs,represented by utility-scale solar and wind power sources,do not provide the inertial response,primary frequency response,secondary frequency response,and tertiary frequency regulation.As a result,the remaining SGs may not be sufficient to maintain the power balance and frequency stability.The concept and control strategies of virtual synchronous generators(VSGs)enable the inverter-based wind and solar power sources to emulate the outer characteristics of traditional SGs and participate in the active power and frequency control of power systems.This paper focuses on the automatic generation control(AGC)with virtual synchronous renewables(VSRs).First,the VSR strategy that enables the RESs to participate in AGC is introduced.Second,based on the interval representation of uncertainty,the output of RES is transformed into two portions,i.e.,the dispatchable portion and the stochastic portion.In the dispatchable portion,the RESs can participate in AGC jointly with SGs.Accordingly,a security-constrained economic dispatch(SCED)model is built considering the RESs operating in VSR mode.Third,the solution strategy that employs the slack variables to acquire deterministic constraints is introduced.Finally,the proposed SCED model is solved based on the 6-bus and 39-bus systems.The results show that,compared with the maximum power point tracking(MPPT)mode,VSRs can participate in the active power and frequency control jointly with SGs,increase the maximum penetration level of RESs,and decrease the operating cost.
文摘Newly proposed power system control methodologies combine economic dispatch(ED) and automatic generation control(AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a real power system is subjected to continuous demand disturbance and system constraints due to the input saturation, communication delays and unmeasurable feed-forward load disturbances. Therefore, optimizing the dynamic response under practical conditions is equally important. This paper proposes a state constrained distributed model predictive control(SCDMPC)scheme for the optimal frequency regulation of an interconnected power system under actual operation conditions, which exist due to the governor saturation, generation rate constraints(GRCs), communication delays, and unmeasured feed-forward load disturbances. In addition, it proposes an algorithm to handle the solution infeasibility within the SCDMPC scheme, when the input and state constraints are conflicting. The proposed SCDMPC scheme is then tested with numerical studies on a three-area interconnected network. The results show that the proposed scheme gives better control and cost performance for both steady state and dynamic state in comparison to the traditional distributed model predictive control(MPC) schemes.
文摘Particle swarm optimization(PSO)is one of the popular stochastic optimization based on swarm intelligence algorithm.This simple and promising algorithm has applications in many research fields.In PSO,each particle can adjust its‘flying’according to its own flying experience and its companions’flying experience.This paper proposes a new PSO variant,called the statistically tracked PSO,which uses group statistical characteristics to update the velocity of the particle after certain iterations,thus avoiding localminima and helping particles to explore global optimum with an improved convergence.The performance of the proposed algorithm is tested on a deregulated automatic generation control problem in power systems and encouraging results are obtained.
文摘This paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1 + PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1 + PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.
文摘In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge(SoC)of energy storage units,this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units.First,a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process.Next,a control framework based on variable integral coefficients is proposed within the secondary frequency regulation model,along with an objective function that simultaneously considers both Automatic Generation Control(AGC)command tracking performance and SoC recovery requirements of energy storage units.Finally,a gradient descent optimization method is used to dynamically adjust the gain of the energy storage integral controller,allowingmultiple energy storage units to respond in real-time to AGC instructions and SoC variations.Simulation results confirmthe effectiveness of the proposedmethod.Compared to traditional strategies,the proposed approach takes into account the SoCdiscrepancies amongmultiple energy storage units and the duration of system net power imbalances.It successfully implements secondary frequency regulation while achieving dynamic power allocation among the units.
文摘This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form,Quasi Oppositional Sine Cosine Algorithm,to automatic generation control of a multiple-source-based interconnected power system that consists of thermal,gas,and hydro power plants.The Proportional-Integral-Derivative controller,which is utilized for automated generation control in an interconnected hybrid power systemwith aDClink connecting two regions,has been tuned using the proposed optimization technique.An Electric Vehicle is taken into consideration only as an electrical load.The Quasi Oppositional Sine Cosinemethod’s performance and efficacy have been compared to the Sine Cosine Algorithm and optimal output feedback controller tuning performance.Applying the QOSCA optimization technique,which has only been shown in this study in the context of an LFC research thus far,makes this paper unique.The main objective has been used to assess and compare the dynamic performances of the recommended controller along with QOSCA optimisation technic.The resilience of the controller is examined using two different system parameters:B(frequency bias parameter)and R(governor speed regulation).The sensitivity analysis results demonstrate the high reliability of the QOSCA algorithm-based controller.Once optimal controller gains are established for nominal conditions,step load perturbations up to±10%&±25%in the nominal values of the systemparameters and operational load condition do not require adjustment of the controller.Ultimately,a scenario is examined whereby EVs are used for area 1,and a single PID controller is used rather than three.
文摘In this paper, a fractional order proportional integral derivative （FOPID） controller for multiarea automatic generation control （AGC） scheme has been designed. FOPID controller has five parameters and provides two additional degrees of flexibility in comparison to a proportional integral derivative （PID） controller. The optimal values of parameters of FOPID controller have been determined using Big Bang Big Crunch （BBBC） search algorithm. The designed controller regulates real power output of generators to achieve the best dynamic response of frequency and tie-line power on a load perturbation. The complete scheme for designing of the controllers has been developed and demonstrated on multiarea deregulated power system. The performance of the designed FOPID controllers has been compared with the optimally tuned PID controllers. It is observed from the results that the FOPID controller shows a considerable improvement in the performance as compared to the conventional PID controller.
文摘Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid planning and construction, and will make a heavy impact on the safe and reliable operation of power systems. To deal with the diff iculties of large scale wind power dispatch, this paper presents a new automatic generation control (AGC) scheme that involves the participation of wind farms. The scheme is based on ultra-short-term wind power forecast. The author establishes a generation output distribution optimization mode for the power system with wind farms and verif ies the feasibility of the scheme by an example.
基金supported in part by Science and Technology Project of State Grid Corporation of China(No.5100-202336015A-1-1-ZN)。
文摘The significant increase in the proportion of renewable energy sources(RESs)has elevated risks of extreme ramp events and frequency instability in power systems.In recent years,frequency stability events have occurred in several countries/regions worldwide due to flexibility deficiencies.Generation flexibility has emerged as a critical factor influencing the frequency stability of power systems.This paper proposes a domain of attraction(DOA)-based quantitative method to assess the frequency stability region of power systems with a high proportion of RESs,considering generation flexibility constraints.First,ramp rate is adopted as the core indicator to characterize generation flexibility within automatic generation control(AGC)timescale,through which a nonlinear AGC model with rate saturation constraints is established.Second,the concept of DOA is introduced to define the stability region of the nonlinear AGC.Third,a quadratic Lyapunov-based estimation method is employed to quantitatively analyze the DOA of the nonlinear AGC at different generation flexibility levels.Simulation results demonstrate that increased generation flexibility expands the estimated DOA of the nonlinear AGC,whereas generation flexibility deficiency induces AGC instability.Moreover,state trajectory and time-domain simulation verify that the proposed estimation method accurately represents the stability region of the nonlinear AGC.
文摘Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this regard,AGC system based on fuzzy logic,i.e.,so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system.Apart from that,simultaneous coordination scheme based on particle swarm optimization(PSO)along with real coded genetic algorithm(RCGA)is suggested to coordinate FLAGCs of the all areas.To clarify the high efficiency of aforementioned strategy,two different interconnected multi-area power systems,i.e.,three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies.The potency of this strategy has been thoroughly dealt with by considering the step load perturbation(SLP)in both the under study power systems.To sum up,the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC(CAGC)in each power system in order to damp out the power system oscillations.
基金This work was supported by Sino-US international Science and Technology Cooperation Project(Grant No.2016YFE0105300).
文摘The large-scale popularization of electric vehicles(EVs)brings the potential for grid frequency regulation.Considering the characteristics of fast response and adjustment of EVs,two control strategies of automatic generation control(AGC)with EVs are proposed responding to two high frequency regulating signals extracted from area control error(ACE)and area regulation requirement(ARR)by a digital filter,respectively.In order to dispatch regulation task to EVs,the capacity of regulation is calculated based on maximum V2G power and the present V2G power of EVs.Finally,simulations based on a two-area interconnected power system show that the proposed approaches can significantly suppress frequency deviation and reduce the active power output of traditional generation units.
文摘Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control(AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems(FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator(TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC(HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization(MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.
文摘This paper investigates automatic generation control(AGC)of a realistic hybrid four-control area system with a dis-tinct arrangement of thermal units,gas units and additional power generation.A proportional-integral-double deriva-tive cascaded with proportional-integral(PIDD-PI)controller is employed as secondary controller in each control area for robust restructured AGC considering bilateral transactions and contract violations.The Harris Hawks algorithm is used to determine the optimal controller gains and system parameters under several scenarios.Electric vehicle(EV)aggregators are employed in each area to participate fully along with thermal and gas units to compensate for the unscheduled system demand in the local area.A comparison of non-cascaded controllers such as PI-PD,PD-PID and the proposed PIDD-PI proves the superiority of the last.The effect of the decline in inertia is closely examined because of the sudden outage of a generating unit while at the same time considering the change in area frequency response characteristics and area control error.EV fleets make significant contributions to improving the system dynamics dur-ing system inertia loss.The use of EVs in the presence of a wind energy-supported grid can provide a stable efficacy to the power grid.Numerous simulations with higher load demands,stochastic communication delays in presence of the WTG plant,and violations in system loadings and changes in gas turbine time constants in the absence of WTG demonstrate the robustness of the proposed control approach.
