查看更多>>摘要：Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input mea-surements.It has already been shown that evaluating perfor-mance based only on the test dataset might not effectively indi-cate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthi-ness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is val-idated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.
查看更多>>摘要：This paper proposes a novel fault location method for overhead feeders,which is based on the direct load flow ap-proach.The method is developed in the phase domain to effec-tively deal with unbalanced network conditions,while it can al-so handle any type of distributed generation(DG)units without requiring equivalent models.By utilizing the line series parame-ters and synchronized or unsynchronized voltage and current phasor measurements taken from the sources,the method reli-ably identifies the most probable faulty sections.With the aid of an index,the exact faulty section among the multiple candi-dates is determined.Extensive simulation studies for the IEEE 123-bus test feeder demonstrate that the proposed method accu-rately estimates the fault position under numerous short-circuit conditions with varying pre-fault system loading conditions,fault resistances,and measurement errors.The proposed meth-od is promising for practical applications due to the limited number of required measurement devices as well as the short computation time.
查看更多>>摘要：The main goal of distribution network(DN)expan-sion planning is essentially to achieve minimal investment con-strained by specified reliability requirements.The reliability-constrained distribution network planning(RcDNP)problem can be cast as an instance of mixed-integer linear programming(MILP)which involves ultra-heavy computation burden espe-cially for large-scale DNs.In this paper,we propose a parallel computing based solution method for the RcDNP problem.The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination.Then,a parallelizable aug-mented Lagrangian algorithm with acceleration method is devel-oped to solve the coordination planning problems.The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem.Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition.Under mild conditions,the opti-mality and convergence of the proposed method are verified.Numerical tests show that the proposed method can significant-ly reduce the solution time and make the RcDNP applicable for real-world problems.
查看更多>>摘要：With the large-scale connection of 5G base stations(BSs)to the distribution networks(DNs),5G BSs are utilized as flexible loads to participate in the peak load regulation,where the BSs can be divided into base station groups(BSGs)to real-ize inter-district energy transfer.A Stackelberg game-based opti-mization framework is proposed,where the distribution net-work operator(DNO)works as a leader with dynamic pricing for multi-BSGs;while BSGs serve as followers with the ability of demand response to adjust their charging and discharging strategies in temporal dimension and load migration strategy in spatial dimension.Subsequently,the presence and uniqueness of the Stackelberg equilibrium(SE)are provided.Moreover,differ-ential evolution is adopted to reach the SE and the optimization problem in multi-BSGs is decomposed to solve the time-space coupling.Finally,through simulation of a practical system,the results show that the DNO operation profit is increased via cut-ting down the peak load and the operation costs for multi-BSGs are reduced,which reaches a win-win effect.
查看更多>>摘要：With the rapid increase in the installed capacity of renewable energy in modern power systems,the stable operation of power systems with considerable power electronic equipment requires further investigation.In converter-based islanded mi-crogrid(CIM)systems equipped with grid-following(GFL)and grid-forming(GFM)voltage-source converters(VSCs),it is chal-lenging to maintain stability due to the mutual coupling effects between different VSCs and the loss of voltage and frequency sup-port from the power system.In previous studies,quantitative transient stability analysis was primarily used to assess the active power loop of GFM-VSCs.However,frequency and voltage dy-namics are found to be strongly coupled,which strongly affects the estimation result of stability boundary.In addition,the vary-ing damping terms have not been fully captured.To bridge these gaps,this paper investigates the transient stability of CIM consid-ering reactive power loop dynamics and varying damping.First,an accuracy-enhanced nonlinear model of the CIM is derived based on the effects of reactive power loop and post-disturbance frequency jump phenomena.Considering these effects will elimi-nates the risk of misjudgment.The reactive power loop dynamics make the model coefficients be no longer constant and thus vary with the power angle.To evaluate quantitatively the effects of re-active power loop and varying damping on the transient stability of CIM,an iterative criterion based on the equal area criterion theory is proposed.In addition,the effects of parameters on the stable boundary of power system are analyzed,and the dynamic interaction mechanisms are revealed.Simulation and experiment results verify the merits of the proposed method.
查看更多>>摘要：The intermittency of renewable energy generation,variability of load demand,and stochasticity of market price bring about direct challenges to optimal energy management of microgrids.To cope with these different forms of operation un-certainties,an imitation learning based real-time decision-mak-ing solution for microgrid economic dispatch is proposed.In this solution,the optimal dispatch trajectories obtained by solv-ing the optimal problem using historical deterministic operation patterns are demonstrated as the expert samples for imitation learning.To improve the generalization performance of imita-tion learning and the expressive ability of uncertain variables,a hybrid model combining the unsupervised and supervised learn-ing is utilized.The denoising autoencoder based unsupervised learning model is adopted to enhance the feature extraction of operation patterns.Furthermore,the long short-term memory network based supervised learning model is used to efficiently characterize the mapping between the input space composed of the extracted operation patterns and system state variables and the output space composed of the optimal dispatch trajectories.The numerical simulation results demonstrate that under vari-ous operation uncertainties,the operation cost achieved by the proposed solution is close to the minimum theoretical value.Compared with the traditional model predictive control method and basic clone imitation learning method,the operation cost of the proposed solution is reduced by 6.3%and 2.8%,respective-ly,over a test period of three months.
查看更多>>摘要：In an autonomous droop-based microgrid,the sys-tem voltage and frequency(VaF)are subject to deviations as load changes.Despite the existence of various control methods aimed at correcting system frequency deviations at the second-ary control level without any communication network,the chal-lenges associated with these methods and their abilities to simul-taneously restore microgrid VaF have not been fully investigat-ed.In this paper,a multi-input multi-output(MIMO)model ref-erence adaptive controller(MRAC)is proposed to achieve VaF restoration while accurate power sharing among distributed generators(DGs)is maintained.The proposed MRAC,without any communication network,is designed based on two meth-ods:droop-based and inertia-based methods.For the microgrid,the suggested design procedure is started by defining a model reference in which the control objectives,such as the desired settling time,the maximum tolerable overshoot,and steady-state error,are considered.Then,a feedback-feedforward con-troller is established,of which the gains are adaptively tuned by some rules derived from the Lyapunov stability theory.Through some simulations in MATLAB/SimPowerSystem Tool-box,the proposed MRAC demonstrates satisfactory perfor-mance.
查看更多>>摘要：The unbalanced state of charge(SOC)of distribut-ed energy storage systems(DESSs)in autonomous DC mi-crogrid causes energy storage units(ESUs)to terminate opera-tion due to overcharge or overdischarge,which severely affects the power quality.In this paper,a fuzzy droop control for SOC balance and stability analysis of DC microgrid with DESSs is proposed to achieve SOC balance in ESUs while maintaining a stable DC bus voltage.First,the charge and discharge modes of ESUs are determined based on the power supply requirements of the DC microgrid.One-dimensional fuzzy logic is then ap-plied to establish the relationship between SOC and the droop coefficient Rd in the aforementioned two modes.In addition,when integrated with voltage-current double closed-loop con-trol,SOC balance in different ESUs is realized.To improve the balance speed and precision,an exponential acceleration factor is added to the input variable of the fuzzy controller.Finally,based on the average model of converter,the system-level stabil-ity of microgrid is analyzed.MATLAB/Simulink simulation re-sults verify the effectiveness and rationality of the proposed method.
查看更多>>摘要：Networked microgrids(NMGs)are critical in the accommodation of distributed renewable energy.However,the existing centralized state estimation(SE)cannot meet the de-mands of NMGs in distributed energy management.The cur-rent estimator is also not robust against bad data.This study in-troduces the concepts of relative error to construct an improved robust SE(IRSE)optimization model with mixed-integer nonlin-ear programming(MINLP)that overcomes the disadvantage of inaccurate results derived from different measurements when the same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimization model,the number of binary variables is reduced based on the projection statistics and normalized residual methods,which ef-fectively avoid the problem of slow convergence or divergence of the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier meth-od(ADMM)distribution algorithm based on outer approxima-tion(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE re-sults that communicate only the boundary coupling information with neighbors.Numerical tests show that the proposed algo-rithm effectively detects bad data,obtains more accurate SE re-sults,and ensures the protection of private information in all microgrids.
Abdullah Azhar Al-ObaidiMohammed Zaki El-SharafyHany E.Z.FaragSaifullah Shafiq...
1227-1238页
查看更多>>摘要：Adopting high penetration levels of electric vehicles(EVs)necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on pow-er distribution networks.Currently,most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links.Such techniques are far from realization due to ① the lack of utility-grade communica-tion systems in many cases such as secondary(low-voltage)pow-er distribution systems to which EVs are connected,rural ar-eas,remote communities,and islands,and ② existing fears and concerns about the data privacy of EV users and cyber-physical security.For these cases,appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements.Accordingly,this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded mi-crogrids.The proposed strategy is autonomous,as it is based on the measurement of system frequency and local bus voltages.The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed genera-tors(DGs)are in short supply by allocating more system capaci-ty to the EVs with less charging in the past.Furthermore,a novel communication-less EV load shedding scheme is incorpo-rated into the management strategy to provide relief to the mi-crogrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages.Numerical simulations demonstrate the superiority of the pro-posed strategy over the state-of-the-art controllers in modulat-ing the EV charging demand to counteract microgrid instability.
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