查看更多>>摘要：Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price volatility.The performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)chargers.Additionally,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)technology.Power electronic converters(PECs)have a construc-tive role in EV applications,both in charging EVs and in V2G.Hence,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV appli-cations.It examines PECs from the point of view of their classi-fications,configurations,control approaches,and future re-search prospects and their impacts on power quality.These can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC converters.To ad-dress the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV applications.Additionally,power-train converters have been modified for high-efficiency and reliability in EV applications.This paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.
查看更多>>摘要：Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnect-ed synchronous machines in power systems.The wide deploy-ment of phasor measurement units(PMUs)promotes the devel-opment of data-driven methods for RAS prediction.This paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data.The grid-informed adjacency matrix incorpo-rates the structural and electrical parameter information of the power grid.Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered.Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against mea-surement uncertainties of the TTEDNN model.Results show that the TTEDNN model performs best among existing deep learning models.Furthermore,the superior transfer learning ability from small-signal RAS conditions to transient RAS con-ditions has been proved.
查看更多>>摘要：This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units(PMUs)to assess and classify generating-side voltage trajecto-ries.The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block.In addition,the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operation-al state.Different types of short-term voltage dynamics are con-sidered in the proposed method.The fuzzy membership func-tions are determined using a systematic method that considers the current situation of the voltage trajectory.Finally,the volt-age status is categorized into four classes to determine appropri-ate remedial actions.The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.
查看更多>>摘要：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 opti-mum 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 opti-mization failure.This paper first proposes a deterministic WC 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 extreme-ly 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 WC,this paper pro-poses robust WC method based on convex deep learning inter-val power flow(DLIPF),which continues to adopt ICNN to es-tablish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this meth-od decreases the modeling and optimization difficulty of robust WC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.
查看更多>>摘要：A day-ahead voltage-stability-constrained network topology optimization(DVNTO)problem is proposed to find the day-ahead topology schemes with the minimum number of operations(including line switching and bus-bar splitting)while ensuring the sufficient hourly voltage stability margin and the engineering operation requirement of power systems.The AC continuation power flow and the uncertainty from both renew-able energy sources and loads are incorporated into the formu-lation.The proposed DVNTO problem is a stochastic,large-scale,nonlinear integer programming problem.To solve it trac-tably,a tailored three-stage solution methodology,including a scenario generation and reduction stage,a dynamic period par-tition stage,and a topology identification stage,is presented.First,to address the challenges posed by uncertainties,a novel problem-specified scenario reduction process is proposed to ob-tain the representative scenarios.Then,to obtain the minimum number of necessary operations to alter the network topologies for the next 24-hour horizon,a dynamic period partition strate-gy is presented to partition the hours into several periods ac-cording to the hourly voltage information based on the voltage stability problem.Finally,a topology identification stage is per-formed to identify the final network topology scheme.The effec-tiveness and robustness of the proposed three-stage solution methodology under different loading conditions and the effec-tiveness of the proposed partition strategy are evaluated on the IEEE 118-bus and 3120-bus power systems.
查看更多>>摘要：In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimi-zation methods.It has gradually been applied in the fields such as economic dispatch of power systems due to its strong self-learning and self-optimizing capabilities.However,existing eco-nomic scheduling methods based on RL ignore security risks that the agent may bring during exploration,which poses a risk of issuing instructions that threaten the safe operation of power system.Therefore,we propose an improved proximal policy op-timization algorithm for sequential security-constrained optimal power flow(SCOPF)based on expert knowledge and safety lay-er to determine active power dispatch strategy,voltage optimiza-tion scheme of the units,and charging/discharging dispatch of energy storage systems.The expert experience is introduced to improve the ability to enforce constraints such as power bal-ance in training process while guiding agent to effectively im-prove the utilization rate of renewable energy.Additionally,to avoid line overload,we add a safety layer at the end of the poli-cy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem.Simu-lation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.
查看更多>>摘要：The increasing penetration of renewable energy sources(RESs)brings great challenges to the frequency security of power systems.The traditional frequency-constrained unit commitment(FCUC)analyzes frequency by simplifying the av-erage system frequency and ignoring numerous induction ma-chines(IMs)in load,which may underestimate the risk and in-crease the operational cost.In this paper,we consider a multi-area frequency response(MAFR)model to capture the frequen-cy dynamics in the unit scheduling problem,in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations.A multi-area FCUC(MFCUC)is formulated as mixed-integer nonlinear pro-gramming(MINLP)on the basis of the MAFR model.Then,we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently.The original MINLP is decomposed into a master problem and subproblems.The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution.Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs.Moreover,simulation results verify the ability of the proposed MAFR model to reflect regional frequency secu-rity and the available inertia of IMs in unit scheduling.
查看更多>>摘要：With the large-scale integration of renewable energy,the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable ener-gy generation.Simultaneously,in a market-oriented operation mode,the power dispatching control center aims to reduce the overall power purchase cost while ensuring the security of the power system.Therefore,a security-constrained transmission maintenance optimization model considering generation and op-erational risk costs is proposed herein.This model is built on dou-ble-layer optimization framework,where the upper-layer model is used for maintenance and generation planning,and the lower-layer model is primarily used to address the operational security risk arising from the random prediction error and N-1 transmis-sion failure.Correspondingly,a generation-maintenance iterative algorithm based on a defined cost feedback is included to in-crease solution efficiency.Generation cost is determined using long-term security-constrained unit commitment,and the opera-tional risk cost is obtained using a double-layer N-1 risk assess-ment model.An electrical correlation coupling coefficient is pro-posed for the solution process to avoid maintenance of associated equipment simultaneously,thereby improving model convergence efficiency.The IEEE 118-bus system is used as a test case for illus-tration,and test results suggest that the proposed model and algo-rithm can reduce the total cost of transmission maintenance and system operation while effectively improving the solution efficien-cy of the joint optimization model.
Mohammad Kazem BakhshizadehSujay GhoshGuangya Yang?ukasz Kocewiak...
782-790页
查看更多>>摘要：As the proportion of converter-interfaced renew-able energy resources in the power system is increasing,the strength of the power grid at the connection point of wind tur-bine generators(WTGs)is gradually weakening.Existing re-search has shown that when connected with the weak grid,the stability of the traditional grid-following controlled converters will deteriorate,and they are prone to unstable phenomena such as oscillation.Due to the limitations of linear analysis that cannot sufficiently capture the stability phenomena,transient stability must be investigated.So far,standalone time-domain simulations or analytical Lyapunov stability criteria have been used to investigate transient stability.However,the time-domain simulations have proven to be computationally too heavy,while analytical methods are difficult to formulate for larger systems,require many modelling assumptions,and are often conserva-tive in estimating the stability boundary.This paper proposes and demonstrates an innovative approach to estimating the transient stability boundary via combining the linear Lyapunov function and the reverse-time trajectory technique.The pro-posed methodology eliminates the need of time-consuming simu-lations and the conservative nature of Lyapunov functions.This study brings out the clear distinction between the stability boundaries with different post-fault active current ramp rate controls.At the same time,it provides a new perspective on crit-ical clearing time for wind turbine systems.The stability bound-ary is verified using time-domain simulation studies.
查看更多>>摘要：With the increasing wind power penetration in the power system,the auxiliary frequency control(AFC)of wind farm(WF)has been widely used.The traditional system fre-quency response(SFR)model is not suitable for the wind pow-er generation system due to its poor accuracy and applicability.In this paper,a piecewise reduced-order frequency response(P-ROFR)model is proposed,and an optimized auxiliary frequen-cy control(O-AFC)scheme of WF based on the P-ROFR model is proposed.Firstly,a full-order frequency response model con-sidering the change in operating point of wind turbine is estab-lished to improve the applicability.In order to simplify the full-order model,a P-ROFR model with second-order structure and high accuracy at each frequency response stage is proposed.Based on the proposed P-ROFR model,the relationship be-tween the frequency response indexes and the auxiliary frequen-cy controller coefficients is expressed explicitly.Then,an O-AFC scheme with the derived explicit expression as the optimi-zation objective is proposed in order to improve the frequency support capability on the premise of ensuring the full release of the rotor kinetic energy and the full use of the effect of time de-lay on frequency regulation.Finally,the effectiveness of the pro-posed P-ROFR model and the performance of the proposed O-AFC scheme are verified by simulation studies.
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