This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced...This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.展开更多
The power systems economic and safety operation considering large-scale wind power penetration are now facing great challenges, which are based on reliable power supply and predictable load demands in the past. A roll...The power systems economic and safety operation considering large-scale wind power penetration are now facing great challenges, which are based on reliable power supply and predictable load demands in the past. A rolling generation dispatch model based on ultra-short-term wind power forecast was proposed. In generation dispatch process, the model rolling correct not only the conventional units power output but also the power from wind farm, simultaneously. Second order Markov chain model was utilized to modify wind power prediction error state (WPPES) and update forecast results of wind power over the remaining dispatch periods. The prime-dual affine scaling interior point method was used to solve the proposed model that taken into account the constraints of multi-periods power balance, unit output adjustment, up spinning reserve and down spinning reserve.展开更多
According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are ins...According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are inserted into IEEE-RTS79 reliability system for risk assessment. By the probabilistic load flow calculated by Monte Carlo simulation method, the probability of the accident is derived, and bus voltage and branch power flow overload risk index are defined in this paper. The results show that this method can realize the modeling of the correlation of wind power output, and the risk index can identify the weakness of the system, which can provide reference for the operation and maintenance personnel.展开更多
Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load ...Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load shedding as the severity of an accident consequence to identify the critical lines in power system. Taking IEEE24-RTS as an example, the simulation results verify the correctness and effectiveness of the proposed index.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
The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasin...The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasingly incompetent for this task because of the slow online calculation speed. Deep reinforcement learning(DRL) has recently been recognized as an effective alternative as it transfers the computational pressure to the off-line training and the online calculation timescale reaches milliseconds. However, its slow offline training speed still limits its application to VVC. To overcome this issue, this paper proposes a simplified DRL method that simplifies and improves the training operations in DRL, avoiding invalid explorations and slow reward calculation speed. Given the problem that the DRL network parameters of original topology are not applicable to the other new topologies, side-tuning transfer learning(TL) is introduced to reduce the number of parameters needed to be updated in the TL process. Test results based on IEEE 30-bus and 118-bus systems prove the correctness and rapidity of the proposed method, as well as their strong applicability for large-scale control variables.展开更多
To achieve economical compensation for the huge-capacity negative sequence currents generated by high-speed railway load, an electromagnetic hybrid compensation system(EHCS) and control strategy is proposed.The EHCS i...To achieve economical compensation for the huge-capacity negative sequence currents generated by high-speed railway load, an electromagnetic hybrid compensation system(EHCS) and control strategy is proposed.The EHCS is made up of a small-capacity railway static power conditioner(RPC) and a large-capacity magnetic static var compensator(MSVC). Compared with traditional compensation methods, the EHCS makes full use of the SVC’s advantages of economy and reliability and of RPC’s advantages of technical capability and flexibility. Based on the idea of injecting a negative sequence, the compensation principle of the EHCS is analyzed in detail. Then the minimum installation capacity of an EHCS is theoretically deduced. Furthermore, a constraint optimization compensation strategy that meets national standards, which reduces compensation capacity further, is proposed. An experimental platform based on a digital signal processor(DSP) and a programmable logic controller(PLC) is built to verify the analysis. Simulated and experimental results are given to demonstrate the effectiveness and feasibility of the proposed method.展开更多
A key infrastructure component of phasor-based wide-area monitoring and control systems(WAMCS)for multienergy systems is the requirement that the practical network communication should not only be reliable but also su...A key infrastructure component of phasor-based wide-area monitoring and control systems(WAMCS)for multienergy systems is the requirement that the practical network communication should not only be reliable but also sufficiently effective to ensure real time monitoring and fast control.However,the rise in the deployment of phasor measurement units(PMUs)has increased the effective attack surface available to attackers and wide area measurement system(WAMS)applications.Such applications have strict and stringent delay request,e.g.end to end delay as well as delay variation between measurements from different PMUs.In order to solve this problem,this paper proposed that the communication network hierarchy is an effective method for evaluating latency by considering the dynamic characteristics of the PMU data stream in the WAMS.Compared with the existing methods,where the upper bound of delay was given,the proposed method is approximated to the real latency in order to enhance the performance of the controller by considering the delay compensation.In this paper,a three-layer hierarchical distributed topology structure of the WAMS communication network was therefore constructed.Using the dynamic characteristics of time-division grading and sampling intervals with the PMU data stream of the WAMS communication network,the network calculus algorithm was exploited to assess the latency of the dynamic PMU data stream for a 50 Hz power system.Finally,an OPNET-based three-layer communication network simulation model was established.In order to demonstrate the effectiveness of the proposed method,the results from a simulation controller are presented.展开更多
Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existin...Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization failure.This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation speed.To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.展开更多
基金supported by the National Key R&D Program of China(No.2021ZD0112700).
文摘This paper develops an advanced framework for the operational optimization of integrated multi-energy systems that encompass electricity,gas,and heating networks.Introducing a cutting-edge stochastic gradient-enhanced distributionally robust optimization approach,this study integrates deep learning models,especially generative adversarial networks,to adeptly handle the inherent variability and uncertainties of renewable energy and fluctuating consumer demands.The effectiveness of this framework is rigorously tested through detailed simulations mirroring real-world urban energy consumption,renewable energy production,and market price fluctuations over an annual period.The results reveal substantial improvements in the resilience and efficiency of the grid,achieving a reduction in power distribution losses by 15%and enhancing voltage stability by 20%,markedly outperforming conventional systems.Additionally,the framework facilitates up to 25%in cost reductions during peak demand periods,significantly lowering operational costs.The adoption of stochastic gradients further refines the framework’s ability to continually adjust to real-time changes in environmental and market conditions,ensuring stable grid operations and fostering active consumer engagement in demand-side management.This strategy not only aligns with contem-porary sustainable energy practices but also provides scalable and robust solutions to pressing challenges in modern power network management.
文摘The power systems economic and safety operation considering large-scale wind power penetration are now facing great challenges, which are based on reliable power supply and predictable load demands in the past. A rolling generation dispatch model based on ultra-short-term wind power forecast was proposed. In generation dispatch process, the model rolling correct not only the conventional units power output but also the power from wind farm, simultaneously. Second order Markov chain model was utilized to modify wind power prediction error state (WPPES) and update forecast results of wind power over the remaining dispatch periods. The prime-dual affine scaling interior point method was used to solve the proposed model that taken into account the constraints of multi-periods power balance, unit output adjustment, up spinning reserve and down spinning reserve.
文摘According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are inserted into IEEE-RTS79 reliability system for risk assessment. By the probabilistic load flow calculated by Monte Carlo simulation method, the probability of the accident is derived, and bus voltage and branch power flow overload risk index are defined in this paper. The results show that this method can realize the modeling of the correlation of wind power output, and the risk index can identify the weakness of the system, which can provide reference for the operation and maintenance personnel.
基金Technology Major Project of China Southern Power Grid Co.,Ltd.(GZ2014-2-0049).
文摘Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load shedding as the severity of an accident consequence to identify the critical lines in power system. Taking IEEE24-RTS as an example, the simulation results verify the correctness and effectiveness of the proposed index.
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The high penetration and uncertainty of distributed energies force the upgrade of volt-var control(VVC) to smooth the voltage and var fluctuations faster. Traditional mathematical or heuristic algorithms are increasingly incompetent for this task because of the slow online calculation speed. Deep reinforcement learning(DRL) has recently been recognized as an effective alternative as it transfers the computational pressure to the off-line training and the online calculation timescale reaches milliseconds. However, its slow offline training speed still limits its application to VVC. To overcome this issue, this paper proposes a simplified DRL method that simplifies and improves the training operations in DRL, avoiding invalid explorations and slow reward calculation speed. Given the problem that the DRL network parameters of original topology are not applicable to the other new topologies, side-tuning transfer learning(TL) is introduced to reduce the number of parameters needed to be updated in the TL process. Test results based on IEEE 30-bus and 118-bus systems prove the correctness and rapidity of the proposed method, as well as their strong applicability for large-scale control variables.
基金supported by National Key Technology Support Program(No.2013BAA02B00)National Natural Science Foundation of China(No.50807041)+3 种基金Asia Pacific Economic Cooperation FundHubei province science and technology support program(No.2014BAA013)the Fundamental Research Funds for the Central Universities(No.2042014kf0233)the Fundamental Research Funds for the Central Universities(No.2014207020202)
文摘To achieve economical compensation for the huge-capacity negative sequence currents generated by high-speed railway load, an electromagnetic hybrid compensation system(EHCS) and control strategy is proposed.The EHCS is made up of a small-capacity railway static power conditioner(RPC) and a large-capacity magnetic static var compensator(MSVC). Compared with traditional compensation methods, the EHCS makes full use of the SVC’s advantages of economy and reliability and of RPC’s advantages of technical capability and flexibility. Based on the idea of injecting a negative sequence, the compensation principle of the EHCS is analyzed in detail. Then the minimum installation capacity of an EHCS is theoretically deduced. Furthermore, a constraint optimization compensation strategy that meets national standards, which reduces compensation capacity further, is proposed. An experimental platform based on a digital signal processor(DSP) and a programmable logic controller(PLC) is built to verify the analysis. Simulated and experimental results are given to demonstrate the effectiveness and feasibility of the proposed method.
基金This work was supported by National Key R&D Program of China(No.2017YFB0902200)Science and Technology Project of State Grid Corporation of China(No.5228001700CW,No.5227221600KW).
文摘A key infrastructure component of phasor-based wide-area monitoring and control systems(WAMCS)for multienergy systems is the requirement that the practical network communication should not only be reliable but also sufficiently effective to ensure real time monitoring and fast control.However,the rise in the deployment of phasor measurement units(PMUs)has increased the effective attack surface available to attackers and wide area measurement system(WAMS)applications.Such applications have strict and stringent delay request,e.g.end to end delay as well as delay variation between measurements from different PMUs.In order to solve this problem,this paper proposed that the communication network hierarchy is an effective method for evaluating latency by considering the dynamic characteristics of the PMU data stream in the WAMS.Compared with the existing methods,where the upper bound of delay was given,the proposed method is approximated to the real latency in order to enhance the performance of the controller by considering the delay compensation.In this paper,a three-layer hierarchical distributed topology structure of the WAMS communication network was therefore constructed.Using the dynamic characteristics of time-division grading and sampling intervals with the PMU data stream of the WAMS communication network,the network calculus algorithm was exploited to assess the latency of the dynamic PMU data stream for a 50 Hz power system.Finally,an OPNET-based three-layer communication network simulation model was established.In order to demonstrate the effectiveness of the proposed method,the results from a simulation controller are presented.
文摘Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization failure.This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation speed.To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.
