A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Multi-Criteria Decision-Making for Power Grid Construction Project Investment Ranking Based on the Prospect Theory Improved by Rewarding Good and Punishing Bad Linear Transformation

Using the improved prospect theory with the linear transformations of rewarding good and punishing bad(RGPBIT),a new investment ranking model for power grid construction projects(PGCPs)is proposed.Given the uncertainty of each index value under the market environment,fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones.Taking into account decision-maker’s subjective risk attitudes,a multi-criteria decision-making(MCDM)method based on improved prospect theory is proposed.First,the[−1,1]RGPBIT operator is proposed to normalize the original data,to obtain the best andworst schemes of PGCPs.Furthermore,the correlation coefficients between interval/fuzzy numbers and the best/worst schemes are defined and introduced to the prospect theory to improve its value function and loss function,and the positive and negative prospect value matrices of the project are obtained.Then,the optimization model with the maximum comprehensive prospect value is constructed,the optimal attribute weight is determined,and the PGCPs are ranked accordingly.Taking four PGCPs of the IEEERTS-79 node system as examples,an illustration of the feasibility and effectiveness of the proposed method is provided.

Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

With the rapid development of the economy,the scale of the power grid is expanding.The number of power equipment that constitutes the power grid has been very large,which makes the state data of power equipment grow explosively.These multi-source heterogeneous data have data differences,which lead to data variation in the process of transmission and preservation,thus forming the bad information of incomplete data.Therefore,the research on data integrity has become an urgent task.This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system.According to the characteristics and data sources of the massive data generated by power equipment,the fuzzy mining model of power equipment data is established,and the data is divided into numerical and non-numerical data based on numerical data.Take the text data of power equipment defects as the mining material.Then,the Apriori algorithm based on an array is used to mine deeply.The strong association rules in incomplete data of power equipment are obtained and analyzed.From the change trend of NRMSE metrics and classification accuracy,most of the filling methods combined with the two frameworks in this method usually show a relatively stable filling trend,and will not fluctuate greatly with the growth of the missing rate.The experimental results show that the proposed algorithm model can effectively improve the filling effect of the existing filling methods on most data sets,and the filling effect fluctuates greatly with the increase of the missing rate,that is,with the increase of the missing rate,the improvement effect of the model for the existing filling methods is higher than 4.3%.Through the incomplete data clustering technology studied in this paper,a more innovative state assessment of smart grid reliability operation is carried out,which has good research value and reference significance.

Energy Management and Capacity Optimization of Photovoltaic, Energy Storage System, Flexible Building Power System Considering Combined Benefit

Building structures themselves are one of the key areas of urban energy consumption,therefore,are a major source of greenhouse gas emissions.With this understood,the carbon trading market is gradually expanding to the building sector to control greenhouse gas emissions.Hence,to balance the interests of the environment and the building users,this paper proposes an optimal operation scheme for the photovoltaic,energy storage system,and flexible building power system(PEFB),considering the combined benefit of building.Based on the model of conventional photovoltaic(PV)and energy storage system(ESS),the mathematical optimization model of the system is proposed by taking the combined benefit of the building to the economy,society,and environment as the optimization objective,taking the near-zero energy consumption and carbon emission limitation of the building as the main constraints.The optimized operation strategy in this paper can give optimal results by making a trade-off between the users’costs and the combined benefits of the building.The efficiency and effectiveness of the proposed methods are verified by simulated experiments.

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.

Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention

In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection.

Lightweight Intrusion Detection Using Reservoir Computing

The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.

Research on the Influence Factors and Coordinated Control Strategies between Unit and Grid for Isolated Power System

As the existing coordinated control strategies between grid and unit have limitations in isolated power system, this paper introduces new coordinated control strategies which can improve the stability of isolated system operation. This paper analyzes the power grid side and unit side influence factors on the isolated power system. The dynamic models which are suitable for islanding operation are applied to simulate and analyze the stability and dynamic characteristics of the isolated power system under the conditions of different load disturbances and governor parameters. With considering the differences of frequency characteristics between the interconnected and isolated power system, the adjusting and optimization methods of under frequency load shedding are proposed to meet the frequency stability requirements simultaneously in the two cases. Not only proper control strategies of the power plant but the settings of their parameters are suggested to improve the operation stability of the isolated power system. To confirm the correctness and effectiveness of the method mentioned above, the isolated system operation test was conducted under the real power system condition, and the results show that the proposed coordinated control strategies can greatly improve stability of the isolated power system.

Mercury Emission From Coal-fired Power Plants in China

燃煤是最大的人为汞排放源之一，燃煤电厂的汞排放控制问题也越来越为人们所关注。该文将目前公布的中国电厂的汞排放数据进行筛选汇总，得到18个电厂的汞排放数据，通过对比和分析，总结出了符合我国燃煤电厂特点的一般性汞排放规律。结果表明：与美国煤种相比，我国的燃煤具有低氯、高灰的特性，这不利于汞的排放控制；循环流化床燃烧可能会因燃尽率的不同而较煤粉炉燃烧易获得较高的Hgp；空气污染控制设备的使用能够有效地减少汞排放，选择性催化还原＋静电除尘器／布袋除尘器＋石灰石-石膏湿法脱硫（SCR＋ESP／FF＋WFGD）组合的平均脱汞率可达71．48％；SCR的大规模应用可能会将汞大量转移到飞灰和脱硫石膏中，带来二次污染的问题。

Distributed Harmonic Power Sharing with Voltage Distortion Suppression in Islanded Microgrids Considering Non-linear Loads

In contemporary power grids or microgrids,harmonic distortion has emerged as one of the critical power quality issues for utility power grids,which has escalated especially due to the high penetration of power-electronic-converter-interfaced distributed generation(DG).This paper first illustrates the prevalent dispute revolving around the harmonic power sharing and distortion restraint,and subsequently proposes a consensusbased framework that facilitates an accurate sharing of harmonics among multi-DGs connected in parallel,with an effective suppression of the output voltage distortion.Compared with the majority of existing studies addressing the issue of voltage harmonics at the point of common coupling(PCC),our method primarily emphasizes on the output voltage distortion since the power quality requirement for certain local critical loads is often known to be high.With the help of adaptive regulation,the overall distortion produced at the output terminals of DGs can be retained within an acceptable range.The working principle of the proposed control method,which is not only easy to implement but also independent of model parameters,is further described in detail.Employing the small-signal dynamic model,the system stability and robustness are analyzed.The hardware-in-the-loop(HIL)simulations aid in determining the outcome of the proposed strategy in microgrid control.

基于FCOS的智慧工地异常行为二阶段检测算法

对于智慧工地作业人员异常行为的检测,现有的经典目标检测算法无法到达理想的检测效果,准确率较低。为此,提出一种基于FCOS(全卷积单阶段目标检测)的二阶段检测算法来实现智慧工地异常行为检测。该算法主要包括两个级联网络,首先通过FCOS对作业人员及异常行为标志物进行识别定位,再使用MLP(多层感知器)完成异常行为的检测分类。最后以相关项目现场采集的12977张样本图片作为数据集,对检测算法进行实验验证。结果表明,该算法在对各类异常行为的检测中均表现优异,而且检测实时性好、计算复杂度低、模型参数少,在实际项目的部署及应用方面具有明显优势。

The User-defined Modeling Method of Power System Components Based on RTDS-CBuilder

This paper puts forward a method to design the user-defined component based on the user-defined modeling environment CBuilder of RTDS simulator. And also develops the user-defined component model with algorithm described by C language, visual graphics appearance, and the component function. And it generates the dynamic link library which has the same execution efficiency as that of the included model of RTDS. This paper takes the IEEE type EXST1 static excitation system as an example to build the user-defined component. The closed-loop tests on the user-defined component and the included one of RTDS are performed to examine the accuracy of the proposed method. By comparison, the test results show that the external characteristics of the user-defined component and the included model of RTDS are basically the same in the initialization process, the step process of the terminal voltage reference value and the case of the large disturbance.

Analysis of Solar Direct-Driven Organic Rankine Cycle Powered Vapor Compression Cooling System Combined with Electric Motor for Office Building Air-Conditioning

Solar energy powered organic Rankine cycle vapor compression cycle(ORC-VCC)is a good alternative to convert solar heat into a cooling effect.In this study,an ORC-VCC system driven by solar energy combined with electric motor is proposed to ensure smooth operation under the conditions that solar radiation is unstable and discontinuous,and an office building located in Guangzhou,China is selected as a case study.The results show that beam solar radiation and generation temperature have considerable effects on the system performance.There is an optimal generation temperature at which the system achieves optimum performance.Also,as a key indicator,the cooling power per square meter collector should be considered in the hybrid solar cooling system in design process.Compared to the vapor compression cooling system,the hybrid cooling system can save almost 68.23%of electricity consumption.

基于Springcolud的智能电网平台的设计与实现

为解决智能电网中运行缓慢、电网运行滞后的问题,设计了智能电网平台系统,系统包括智能电网运行、探索数据挖掘平台和智能电网平台的应用三大模块。首先采用数据挖掘技术,实现了对智能电网平台数据的分析与处理;然后设计了智能电网的可视化平台,采用Intranet技术、多媒体技术和开发主流技术,构建了智能电网的可视化平台,实现了智能电网平台的可视化;最后利用数字地表模型和交互式遗传算法的改进算法,实现了智能电网的任务调度。试验表明,研究的智能电网平台在试验次数增加的同时完成任务的总时间波动最小,在20 s~25 s之间波动。

Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.

Accurate time synchronization of power reference station based on BD3 system

A Beidou 3(BD3)system-based power reference station can provide high-precision time synchronization for power distribution systems by sending synchronization data packets to devices in a multi-hop routing fashion.However,optimizing route selection to reduce both time synchronization error and delay is a challenging problem.In this paper,we establish a software-defined network-enabled power reference station time synchronization framework based on BD3.Then,we formulate the joint problem to minimize cumulative synchronization error and delay through multi-hop route selection optimization.A back propagation(BP)neural network-improved intelligent time synchronization route selection algorithm named BP-RS is proposed to learn the optimal route selection,which uses a BP neural network to dynamically adjust the exploration factor to achieve rapid convergence.Simulation results show the superior performance of BP-RS in synchronization delay,synchronization error,and adaptability with changing routing topologies.

Evaluating the Derivative Value of Smart Grid Investment under Dual Carbon Target: A Hybrid Multi-Criteria Decision-Making Analysis

With the goal of“carbon peaking and carbon neutralization”,it is an inevitable trend for investing smart grid to promote the large-scale grid connection of renewable energy.Smart grid investment has a significant driving effect(derivative value),and evaluating this value can help to more accurately grasp the external effects of smart grid investment and support the realization of industrial linkage value with power grid investment as the core.Therefore,by analyzing the characterization of the derivative value of smart grid driven by investment,this paper constructs the evaluation index system of the derivative value of smart grid investment including 11 indicators.Then,the hybrid evaluation model of the derivative value of smart grid investment is developed based on anti-entropy weight(AEW),level based weight assessment(LBWA),and measurement alternatives and ranking according to the compromise solution(MARCOS)techniques.The results of case analysis show that for SG investment,the value of sustainable development can better reflect its derivative value,and when smart grid performs poorly in promoting renewable energy consumption,improving primary energy efficiency,and improving its own fault resistance,the driving force of its investment for future sustainable development will decline significantly,making the grid investment lack derivative value.In addition,smart grid investment needs to pay attention to the economy of investment,which is an important guarantee to ensure that the power grid has sufficient and stable sources of investment funds.Finally,compared with three comparison models,the proposed hybrid multi-criteria decision-making(MCDM)model can better improve the decision-making efficiency on the premise of ensuring robustness.

支撑新型配电网数字化规划的图形⁃模型⁃数据融合关键技术

配电网规划领域期盼实现智能规划,其愿景在于实现无人或少人干预的全自动规划。在数字化转型的背景下,新型配电网规划将面临图形多样化、场景碎片化、数据规模化三大挑战。文中从图形-模型-数据融合的角度提出三大关键技术:基于电气图纸识别和拓扑智能分析的图形-模型融合技术、基于知识驱动的负荷/新能源推演分析和智能决策的模型-数据融合技术、基于多模态数据融合和多时空数据联动的图形-数据融合技术,尝试打破理论研究与数字化工程的壁垒。最后,对未来新型配电网数字化规划的发展进行思考和展望,为实现“以机为主,人机协同”的大闭环模式提供借鉴。