查看更多>>摘要：Emergency control is an essential means to help system maintain synchronism after fault clearance.Traditional"offline calculation,online matching"scheme faces significant challenges on adaptiveness and robustness problems.To ad-dress these challenges,this paper proposes a novel closed-loop framework of transient stability prediction(TSP)and emergency control based on Deep Belief Network(DBN).First,a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted,which takes into account accuracy and rapidity at the same time.Next,a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity.When impending instability of the system is foreseen,optimal emergency control strategy can be determined in time.Lastly,responses after emergency control are fed back to the TSP model.If prediction result is still unstable,an additional control strategy will be implemented.Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council(NPCC)140-bus system.Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.
查看更多>>摘要：With the development of the smart grid,the distri-bution system operation conditions become more complex and changeable.Furthermore,due to the influence of observation outliers and uncertain noise statistics,it is more difficult to grasp the dynamic operation characteristics of distribution system.In order to address these problems,by using projection statistics and the noise covariance updating technology based on the Sage-Husa noise estimator,for distribution power system with outliers and uncertain noise statistics,a robust adaptive cubature Kalman filter forecasting-aided state estimation method is proposed based on generalized-maximum likelihood type estimator.Furthermore,an adaptive strategy,which can enhance the filtering accuracy under normal conditions,is presented.In the simulation part,the branch parameters and node load parameters of the test system are appropriately modified to simulate the asymmetry of the three-phase branch parameters and the asymmetry of the three-phase loads.Finally,through simulation experiments on the improved test system,it is verified that the robust forecasting-aided state estimation method,presented in this paper,can effectively perceive the actual operating state of the distribution network in different simulation scenarios.
查看更多>>摘要：Due to the large number of submodules(SMs),and modular multilevel converters(MMCs)in high-voltage applica-tions,they are usually regulated by the nearest level modulation(NLM).Moreover,the large number of SMs causes a challenge for the fault diagnosis strategy(FDS).This paper proposes a currentless FDS for MMC with NLM.In FDS,the voltage sensor is relocated to measure the output voltage of the SM.To acquire the capacitor voltage and avoid increasing extra sensors,a capacitor voltage calculation method is proposed.Based on the measurement of output voltages,the faults can be detected and the number of different-type switch open-circuit faults can be confirmed from the numerous SMs in an arm,which narrows the scope of fault localization.Then,the faulty SMs and faulty switches in these SMs are further located without arm current according to the sorting of capacitor voltages in the voltage balancing algorithm.The FDS is independent of the arm current,which can reduce the communication cost in the hierarchical control system of MMC.Furthermore,the proposed FDS not only simplifies the identification of switch open-circuit faults by confirming the scope of faults,but also detects and locates multiple different-type faults in an arm.The effectiveness of the proposed strategy is verified by the simulation results.
查看更多>>摘要：In order to accurately assess the risk of the hybrid AC/DC network under fault,a risk assessment method for the hybrid AC/DC system based on the transient energy function is proposed.First,based on the energy transfer relationship of the hybrid AC/DC power system,the transient energy function model of the hybrid AC/DC system is established.Based on the operating data of the power grid,the energy function is used as an efficiency variable,and the efficiency variable is integrated into the prior risk probability calculation of nodes in the network,and a Bayesian network-based risk assessment model of hybrid AC/DC system is established.Considering the dynamic update model of network cascading failures,the clique tree propagation algorithm is used to dynamically calculate the posterior risk probability of the node to realize the dynamic assessment of the network risk.Finally,the improved IEEE-39 node hybrid AC/DC system is used as an example for analysis.The results show that the proposed model can not only effectively evaluate the overall safety of the network,but also has feasibility in predicting faults,which can provide a theoretical basis for the stability control of the hybrid AC/DC system.
查看更多>>摘要：Data-driven methods are widely recognized and generate conducive results for online transient stability assess-ment.However,the tedious and time-consuming process of sample collection is often overlooked.The functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode,thereby highlighting the importance of collection efficiency.As a means to achieve high sample collection efficiency following the operation mode change,we propose a novel instance-transfer method based on compression and matching strategy,which facilitates the direct acquisition of useful previous samples,used for creating the new sample base.Additionally,we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection,where features of analytical modeling with special significance are introduced into data-driven methods.At the same time,a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models,enabling fast and accurate post-disturbance transient stability assessment.As a paradigm,we consider a scheme for online critical clearing time estimation,where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part,respectively.Derived results validate the credible efficacy of the proposed method.
查看更多>>摘要：The coordination of enrgy transition,fixed cost re-covery,and sufficient generation supply leads to a new challenge for a traditional capacity market mechanism.Moreover,in order to better match network expansion at the same time,it is crucial to redesign the capacity market mechanism considering system topology.In this paper,a novel capacity market mechanism is proposed considering spot market operations,network expansion,and energy transition,which can minimize the total cost of ca-pacity investment,network expansion,and generation operations,while satisfying the energy transition constraints and topology circumstances.Specifically,the capacity market mechanism co-ordinated with spot market operations is illustrated,in which the energy transition and network constraints are embedded.Then,a bi-level optimization model is established where the trade organizers minimize the total cost of both investment and operations,subject to the spot power market simultaneously minimizing the local dispatching costs.The numerical results of a test system show that more economical capacity portfolios can be obtained by constructing reasonable transmission lines,thereby obtaining a more optimal market cost.A detailed multi-scenario simulation is further analyzed to verify the effectiveness of the proposed market mechanism.
查看更多>>摘要：With development of integrated energy systems and energy markets,transactive energy has received increasing attention from society and academia,and realization of energy distribution and integrated demand response through market transactions has become a current research hotspot.Research on optimized operation of a distributed energy station as a regional energy supply center is of great significance for improving flexibility and reliability of the system.Based on retail-side energy trading market,this study first establishes a framework of combined electric and heating energy markets and analyses a double auction market mechanism model of interconnected distributed energy stations.This study establishes a mechanism model of energy market participants,and establishes the electric heating combined market-clearing model to maximize global surplus considering multi-energy storage.Finally,in the case study,a typical user energy consumption scenario in winter is selected,showing market-clearing results and demand response effects on a typical day.Impact of transmission line constraints,energy supply equipment capacity,and other factors on clearing results and global surplus are compared and analyzed,verifying the effects of the proposed method on improving global sur-plus,enhancing interests of market participants and realizing coordination and optimal allocation of both supply and demand resources through energy complementarity between regions.
查看更多>>摘要：The development of the Energy Internet has im-proved the efficiency of energy utilization and promoted sus-tainable development of power and energy systems.The multi-energy system modeling considering the dynamic process of transmission line is one of the key research points of Energy Internet operation control.Through the energy circuit theory,the lumped parameter model of natural gas pipelines is built and the dynamic characteristic parameters under the control instruction are extracted.Combined with dynamic characteristic parameters,the long short-term memory(LSTM)neural network is designed to fit the natural gas pipeline dynamic process into discrete linear time-varying(LTV)equations.Combined with the equations,an energy hub method is used to build a control model of industrial parks with multi-energy distribution system.Using the rolling optimal control strategy given in this paper,the model is solved by the Matlab-Yalmip solver and rolling control instructions of each energy conversion unit are obtained.Finally,the case study demonstrates that the LSTM neural network-based modeling method presented in this paper can accurately fit the dynamic process of a natural gas pipeline system.The rolling control model of the multi-energy system can improve the efficiency of energy utilization,exhibit the transmission line status constraints during the optimization control process and improve reliability of the multi-energy system operation.
查看更多>>摘要：Although the dead-time optimization design of res-onant converters has been widely researched,classical design methods focus more on achieving zero-voltage switching(ZVS)operation.The body diode loss is always ignored,which results in low-efficiency of the converter,especially,in energy router(ER).To deal with this problem,this paper proposes an adaptive dead-time modulation scheme for bidirectional LLC resonant convert-ers in ER.First,the power loss of the MOSFET is analyzed based on the dead-time.Then,a novel dead-time optimization modulation principle is proposed.It can eliminate the body diode loss of MOSFET compared with existing literature.Based on the optimization modulation principle,this paper proposes an adaptive dead-time modulation scheme.To this end,the converter adopting the scheme no longer needs to calculate dead-time,which simplifies the parameter design process.Meanwhile,this scheme enables dead-time to dynamically change with working conditions according to the dead-time optimization modulation principle.With these effects,the ZVS operation is achieved,and the body diode loss of MOSFET is also eliminated.Fur-thermore,a digital implementation method is designed to make the proposed modulation scheme have fast-transient response.Finally,experimental results show that the proposed dead-time modulation scheme enables converters to achieve ZVS operation in all working conditions,and has higher efficiency than classical dead-time design methods.
查看更多>>摘要：Short driving ranges and low braking energy re-covery efficiencies are two recognized technical bottlenecks to be overcome in electric vehicles.In this paper,a novel electromechanical-hydraulic coupling system is proposed and in-tegrated as a powertrain for electric vehicles,which can assist the electric vehicle to fully utilize its braking energy.The hydraulic regenerative braking force and electric regenerative braking force can provide all the braking needs using the medium and small braking intensities.Furthermore,an improved compound brake control strategy based on the braking force distribution is proposed and simulated.The results show that under the premise of ensuring braking stability,the electromechanical-hydraulic coupling driving electric vehicle can adapt to various working conditions with excellent energy-saving results.The hydraulic accumulator recovery efficiency is above 99％,and the state of charge consumption rate of the battery pack can be reduced by more than 9％.More importantly,the proposed hybrid power system can significantly improve the driving range and energy efficiency,as well as reduce the consumers'mileage anxiety in electric vehicles.
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