RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

Low-Carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value

The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness.Based on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator.Then,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both considered.To achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear relationships.Subsequently,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and feasibility.Finally,the simulation verification based on the IEEE 33-bus DN is conducted.The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond.Meanwhile,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

A comprehensive review on the development of data-driven methods for wind power prediction and AGC performance evaluation in wind–thermal bundled power systems

The wind–thermal bundled power system achieves energy complementarity and optimized scheduling, which is an important way to build a new type of energy system. For the safe and stable operation of the wind–thermal bundled power system, accurate data-driven analysis is necessary to maintain real-time balance between electricity supply and demand. By summarizing the development and characteristics of wind–thermal bundled power system in China and different countries, current research in this field can be clearly defined in two aspects: short-term wind power prediction for wind farms and performance evaluation of automatic generation control (AGC) for thermal power generation units. For short-term wind power prediction, it is recommended to focus on historical data preprocessing and artificial intelligence methods. The technical characteristics of different data-driven wind power prediction methods have been compared in detail. For performance evaluation of AGC units, a comprehensive analysis was conducted on current evaluation methods, including the “permitted-band” and “regulation mileage” methods, as well as the issue of evaluation failure in traditional evaluation methods in practical engineering. Finally, the relative optimal dynamic performance of AGC units was discussed and the future trend of data-driven research in wind–thermal bundled power system was summarized.

Partitioning Calculation Method of Short-Circuit Current for High Proportion DG Access to Distribution Network

Aiming at the problemthat the traditional short-circuit current calculationmethod is not applicable to Distributed Generation(DG)accessing the distribution network,the paper proposes a short-circuit current partitioning calculation method considering the degree of voltage drop at the grid-connected point of DG.Firstly,the output characteristics of DG in the process of low voltage ride through are analyzed,and the equivalent output model of DG in the fault state is obtained.Secondly,by studying the network voltage distribution law after fault in distribution networks under different DG penetration rates,the degree of voltage drop at the grid-connected point of DG is used as a partition index to partition the distribution network.Then,iterative computation is performed within each partition,and data are transferred between partitions through split nodes to realize the fast partition calculation of short-circuit current for high proportion DG access to distribution network,which solves the problems of long iteration time and large calculation error of traditional short-circuit current.Finally,a 62-node real distribution network model containing a high proportion of DG access is constructed onMATLAB/Simulink,and the simulation verifies the effectiveness of the short-circuit current partitioning calculation method proposed in the paper,and its calculation speed is improved by 48.35%compared with the global iteration method.

BP-LMS-based BDS-3 power system positioning method

The BeiDou-3 navigation satellite system(BDS-3)provides a full-domain high-precision positioning service for the power system to ensure safe and stable operation.However,BDS-3 power system positioning faces certain challenges,such as complex electromagnetic interference and incomplete error elimination.Herein,a back propagation neural network-improved least mean square(BP-LMS)adaptive filtering method is proposed for the BDS-3 full-domain and high-precision power system positioning,which utilizes the loss function to update the weight of the BP hidden layer,computes the pseudo compensation range,and eliminates the impact of electromagnetic interference to enhance the accuracy of power system positioning.Simulation results confirm the superior performance of BP-LMS in positioning accuracy and error elimination.Compared with LMS and normalized least mean square(NLMS),the filtering error of the proposed BP-LMS adaptive filtering method is decreased by 57.14%and 51.38%,respectively.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

The Reliability Evaluation Method Study of Power System Communication Networks in Case of Ice Storm

This paper is divided into two cases to study the communication transmission equipment reliability in the state of the ice storm, according to the huge losses of power system communication caused by the ice storm. For the nodes or links which are not affected by the ice storm, we use the calculation with “the mean time between failures (MTBF)” and “the mean time to repair” (MTTR) to put forward the calculation methods;for the OPGW cable which influenced greater in ice storm, we use the fiber excess length and the elongation of fiber optic cable. It obtains all the paths of the network through improved adjacency matrix method, and then it uses binary decision diagram to obtain the overall reliability of the network. By testing the network nodes and links using “N-1” inspection, the key nodes and key links can be obtained. Finally, considering the importance degree of network transmission business, the reliability evaluation method of power system communication network based on the risk theory in the case of the ice storm has been put forward, and the example to verify that the method can provide the basis for the reliability assessment of the power system communication in the case of the ice storm has been given.

Preventive Control for Power System Transient Security Based on XGBoost and DCOPF with Consideration of Model Interpretability

This paper proposes a new approach for online power system transient security assessment(TSA)and preventive control based on XGBoost and DC optimal power flow(DCOPF).The novelty of this proposal is that it applies the XGBoost and data selection method based on the 1-norm distance in local feature importance evaluation which can provide a certain model interpretability.The method of SMOTE+ENN is adopted for data rebalancing.The contingency-oriented XGBoost model is trained with databases generated by time domain simulations to represent the transient security constraint in the DCOPF model,which has a relatively fast speed of calculation.The transient security constrained generation rescheduling is implemented with the differential evolution algorithm,which is utilized to optimize the rescheduled generation in the preventive control.Feasibility and effectiveness of the proposed approach are demonstrated on an IEEE 39-bus test system and a 500-bus operational model for South Carolina,USA.

Demand-side Management Based on Model Predictive Control in Distribution Network for Smoothing Distributed Photovoltaic Power Fluctuations

With the rapid increase of distributed photovoltaic(PV) power integrating into the distribution network(DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demandside needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling(PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment.Thus, a demand-side management strategy based on model predictive control(MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally,the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.

Multi-energy Storage System Model Based on Electricity Heat and Hydrogen Coordinated Optimization for Power Grid Flexibility

Based on decreasing the flexibility of the power grid through the integration of large-scale renewable energy,a multi-energy storage system architectural model and its coor-dination operational strategy with the same flexibility as in the pumped storage power station and battery energy storage system(BESS)are studied.According to the new energy fluctuation characteristics and the different peak valley parameters in the power grid,this paper proposes a electricity heat hydrogen multi-energy storage system(EHH-MESS)and its coordination and optimization operational model to reduce the curtailment of wind power and photovoltaic(PV)to the power grid and improve the flexibility of the power grid.Finally,this paper studied the simulation model of an energy storage optimization control strategy after the multi-energy storage system is connected to the distribution networks,and analyzed three operational modes of the multi-energy storage system.The simulation results show that the EHH-MESS proposed in this paper has a better power grid regulation flexibility and economy,and can be used to replace the battery energy storage system based on MATLAB.

Distributionally Robust Optimal Reactive Power Dispatch with Wasserstein Distance in Active Distribution Network

The uncertainties from renewable energy sources(RESs)will not only introduce significant influences to active power dispatch,but also bring great challenges to the analysis of optimal reactive power dispatch(ORPD).To address the influence of high penetration of RES integrated into active distribution networks,a distributionally robust chance constraint(DRCC)-based ORPD model considering discrete reactive power compensators is proposed in this paper.The proposed ORPD model combines a second-order cone programming(SOCP)-based model at the nominal operation mode and a linear power flow(LPF)model to reflect the system response under certainties.Then,a distributionally robust optimization(WDRO)method with Wasserstein distance is utilized to solve the proposed DRCC-based ORPD model.The WDRO method is data-driven due to the reason that the ambiguity set is constructed by the available historical data without any assumption on the specific probability distribution of the uncertainties.And the more data is available,the smaller the ambiguity would be.Numerical results on IEEE 30-bus and 123-bus systems and comparisons with the other three-benchmark approaches demonstrate the accuracy and effectiveness of the proposed model and method.

Transient Stability Analysis of Large-scale Power Systems:A Survey

Transient stability analysis is a key problem in power system operation and planning.This paper aims at giving a comprehensive review on the modeling ideas and analysis methods for transient stability of large-scale power systems.For model construction,the general modeling of traditional power systems and special modeling for renewable generations and high-voltage direct-current transmissions are introduced.For transient stability analysis,Lyapunov based methods and non-Lyapunov based methods are thoroughly reviewed.In Lya-punov based methods,we focus on the energy function method,the sum-of-squares based method and decentralized stability analysis methods.Meanwhile,in non-Lyapunov based methods,the time-domain simulation,extended equal-area criterion and data-driven based methods are considered.The basic working principles,features and recent research progresses of all the above-mentioned methods are described in detail.In particular,their performances on several aspects,such as computational speed,conservativeness of stability region estimation or stability margin calculation,and adaptability to various types of system models,are mentioned.Finally,a brief discussion of potential directions for future research on transient stability analysis of large-scale power systems is included.Index Terms-connective stability,large-scale system analysis,Lyapunov functions,power systems,transient stability analysis.

Study on Approach of Static Security Assessment Accounting for Electro-thermal Coupling

A static security assessment approach considering electro-thermal coupling of transmission lines is proposed in this paper. Combined with the dynamic thermal rating technology and energy forecasting, the approach can track both the electrical variables and transmission lines’ temperature varying trajectory under anticipated contingencies. Accordingly, it identifies the serious contingencies by transmission lines’ temperature violation rather than its power flow, in this case the time margin of temperature rising under each serious contingency can be provided to operators as warning information and some unnecessary security control can also be avoided. Finally, numerical simulations are carried out to testify the validity of the proposed approach.

Impact analysis of human factors on power system operation reliability

Along with the improvement of electrical equipment reliability,people’s unsafe behaviors and human errors have become one of main sources of risks in power systems.However,there is no comprehensive study on human factors and human reliability analysis in power systems.In allusion to this situation,this paper attempts to analyze the impact of human factors on power system reliability.First,this paper introduces current situation of human factors in power systems and the latest research progress in this field.Several analysis methods are proposed according to specified situations,and these methods are verified by some power system practical cases.On this base,this paper illustrates how human factors affect power system operation reliability from 2 typical aspects:imperfect maintenance caused by human errors,and impact of human factors on emergency dispatch operation and power system cascading failure.Finally,based on information decision and action in crew(IDAC),a novel dispatcher training evaluation simulation system(DTESS)is established,which can incorporate all influencing factors.Once fully developed,DTESS can be used to simulate dispatchers’response when encountering an initial event,and improve power system dispatching reliability.

Generation maintenance scheduling based on multiple objectives and their relationship analysis

In a market environment of power systems, each producer pursues its maximal profit while the independent system operator is in charge of the system reliability and the minimization of the total generation cost when generating the generation maintenance scheduling(GMS). Thus, the GMS is inherently a multi-objective optimization problem as its objectives usually conflict with each other. This paper proposes a multi-objective GMS model in a market environment which includes three types of objectives, i.e., each producer's profit, the system reliability, and the total generation cost. The GMS model has been solved by the group search optimizer with multiple producers(GSOMP) on two test systems. The simulation results show that the model is well solved by the GSOMP with a set of evenly distributed Pareto-optimal solutions obtained. The simulation results also illustrate that one producer's profit conflicts with another one's, that the total generation cost does not conflict with the profit of the producer possessing the cheapest units while the total generation cost conflicts with the other producers' profits, and that the reliability objective conflicts with the other objectives.

Smart Power Substation Development in China

Over the past decade,China has undertaken substation intellectualization of more than 2000 substation operations in three stages.Much experience has been achieved during this developmental period.In this paper,the status of smart substation development in China is systematically presented,as well as future directions of the smart substation,such as adoption of the hierarchical protection and control system.

Research on the Impact of Distributed Generation on Relay Protection and Its Standards

Distributed generation(DG) will change characteristics of power flow and faults of traditional distribution network as well as the network operation mode, especially the operation of relay protection and automatic circuit breaker re-closing. In this paper, the characteristics and influences of several types of distributed generation and connection pattern, relay protection and automatic circuit breaker re-closing are analyzed. Standards of relay protection when distributed generation access to distribution network are discussed as well.

Analysis of Protective Relay Allocation and Operation in 220 kV and Above AC Systems

This paper provides a comprehensive summary of the scale, allocation and operation situation of relay protection equipment of the 220 kV and above AC systems run by the State Grid Corporation of China in 2012 and 2013. It also analyzes the changing trend of the relevant indices of relay protection during the five years from 2009 to 2013. At the same time, it analyzes in detail two typical relay malfunction events in 2012 and 2013.

Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart grid.However,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on PMUs.To address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are presented.Two general formulations for PAD estimation are then established.To simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of PADs.The latter is used by second-order Taylor series expansion to estimate PADs accurately.To reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is adopted.Results using both simulated and field data verify the effectiveness and robustness of the proposed methods.