A Wind Power Prediction Framework for Distributed Power Grids

To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.

Benefits of High-voltage SiC-based Power Electronics in Medium-voltage Power-distribution Grids

Medium-voltage(MV)power electronics equipment has been increasingly applied m distnbution grids,and high-voltage(HV)silicon carbide(SiC)power semiconductors have attracted considerable attention in recent years.This paper first overviews the development and status of HV SiC power semiconductors.Then,MV power-converter applications in distribution grids are summarized and the benefits of HV SiC in these applications are presented.Microgrids,including conventional and asynchronous microgrids,that can fully demonstrate the benefits of HV SiC power semiconductors are selected to investigate the benefits of HV SiC in detail,including converter-level benefits and system-level benefits.Finally,an asynchronous microgrid power-conditioning system(PCS)prototype using a 10 kV SiC MOSFET is presented.

Harmonic Resonance Analysis of Shale Gas Distribution Network with Phased Load

Based on the background of achieving carbon peaking and carbon neutrality, the development and application of new high-power compressors, electric grid drilling RIGS and electric fracturing pump system provide new equipment support for the electric, green and intelligent development of shale gas fields in China. However, the harmonic pollution of shale gas grid becomes more serious due to the converter and frequency conversion device in the system, which easily causes harmonic resonance problem. Therefore, the harmonic resonance of shale gas grid is comprehensively analyzed and treated. Firstly, the working mechanism of compressor, electric drilling RIGS of the harmonic impedance model of electric fracturing pump system is established. Secondly, the main research methods of harmonic resonance analysis are introduced, and the basic principle of modal analysis is explained. Modal analysis method was used to analyze. Finally, harmonic resonance is suppressed. The results show that there may be multiple resonant frequency points in the distribution network changes, but these changes are relatively clear;if the original resonant frequency point of the resonant loop does not exist, the resonant frequency point disappears. The optimal configuration strategy of passive filter can effectively suppress harmonic resonance of distribution network in shale gas field.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

The Importance of Computational Grids in Hydraulic Numerical Models

The importance of computational grids in hydraulic numerical models is studied by numerical simulation of jet flow in a rectangular duct which is linked with a fixed width inlet and a different width outlet using a standard k-epsilon turbulence model. The computational results show the numerical solutions may not be reasonable because of the incorrect computational grid and each numerical model bass grid-independent solution. The computational grid has a definitive effect on the accuracy and stability of the computational solution, which must be divided well according to the simulated geometry and physical characters of hydraulic problems. The main guidelines about the formation of computational grid in such aspects as node distribution, smoothness and skewness of grid, have been given.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

A Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation

Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.

Smart Transformer/Large Flexible Transformer

Solid-state transformer-based smart transformer(ST)can provide the dc connectivity and advanced services to improve the grid performance and to increase the penetration of the power electronics interfaced resources(e.g.,distributed generators and electric vehicle charging stations)in modern electricity distribution grids.Since the ST is a new and effective paradigm of the electricity grid evolution to well understand the ST,this paper systematically presents the basic architecture and the typical control schemes of the ST and then the advanced services that ST can provide to improve the electricity grids performances in terms of the power flow control,power quality improvement,active damping and active contribution to improve distribution grid resilience by means of enabling autonomous microgrids operation as well as launching a restoration procedure following a general blackout.

Fast Cycle Structure Detection for Power Grids Based on Graph Computing

The cycle structure in a power grid may lower the stability of the network;thus,it is of great significance to accu-rately and timely detect cycles in power grid networks.However,detecting possible cycles in a large-scale network can be highly time consuming and computationally intensive.In addition,since the power grid's topology changes over time,cycles can appear and disappear,and it can be difficult to monitor them in real time.In traditional computing systems,cycle detection requires considerable computational resources,making real-time cycle detection in large-scale power grids an impossible task.Graph computing has shown excellent performance in many areas and has solved many practical graph-related problems,such as power flow calculation and state estimation.In this article,a cycle detection method,the Paton method,is implemented and optimized on a graph computing platform.Two cases are used to test its performance in an actual power grid topology scenario.The results show that the graph computing-based Paton method reduces the time consumption by at least 60%compared to that of other methods.

Distributed continuation power flow method for integrated transmission and active distribution network

As the integration of distributed generations(DGs)transforms the traditional distribution network into the active distribution network,voltage stability assessments(VSA)of transmission grid and distribution grid are not suitable to be studied separately.This paper presents a distributed continuation power flow method for VSA of global transmission and distribution grid.Two different parameterization schemes are adopted to guarantee the coherence of load growth in transmission and distribution grids.In the correction step,the boundary bus voltage,load parameter and equivalent power are communicated between the transmission and distribution control centers to realize the distributed computation of load margin.The optimal multiplier technique is used to improve the convergence of the proposed method.The three-phase unbalanced characteristic of distribution networks and the reactive capability limits of DGs are considered.Simulation results on two integrated transmission and distribution test systems show that the proposed method is effective.

Approaches for Optimal Planning of Energy Storage Units in Distribution Network and Their Impacts on System Resiliency

In the recent decade,a significant increase in the penetration level of renewable energy sources(RESs)into the distribution grid is evident due to the world’s shift towards clean energy and to increase the reliability or inboard manner resiliency of electrical distribution system.RES based microgrids are the most favorable option available,especially to enhance resiliency.However,the integration of RES over the distribution grid would hamper the grid stability due to its stochastic nature under normal conditions.During extreme weather conditions,RES behavior is completely uncertain.Hence there is a need to eliminate the adverse effects caused by the RES and make the distribution grid more reliable and stable under normal and resilient conditions.To address these issues,many researchers proposed several methods to place energy storage units(ESUs)and microgrids(RES integrated),which can support critical loads at an optimal location in the distribution system during normal and extreme conditions,respectively.The aim of this article is to consolidate and review the research towards various approaches to formulate the problem(optimal location,allocation,and operation of ESU and microgrids to face regular and extreme weather condition)and tools to solve it for enhanced system flexibility and resiliency.Based on the review,a generalized methodology has been designed to adapt the inputs and address both conditions.At the end of the review,future aspects for ESU to strengthen resistance and resiliency of its own are presented,which can be helpful to further improve the reliability and resiliency of the distribution system.

A novel PLC channel modeling method and channel characteristic analysis of a smart distribution grid

Power line carrier(PLC)technology plays an increasingly important role in the realization of cost-effective communication in a smart distribution grid.No current channel modeling method is universally applicable to more complex topologies that may emerge in smart grids,such as ring and mesh topologies.This paper presents a novel PLC channel modeling method based on the information node concept,and the universality and feasibility of the proposed method are demonstrated with applications in modeling networks with ring and mesh topologies.The factors that affect the channel characteristics of the networks and the laws that govern their behaviors for different types of topologies are analyzed.The validity and effectiveness of the proposed method are proven using simulation and laboratory tests.This paper provides the necessary theoretical basis and technical means to design the PLC modulation method for smart distribution grids.

Possible role of power-to-vehicle and vehicle-to-grid asstorages and flexible loads in the German 110 kV distributiongrid

The sectoral coupling of road traffic (in form of E-Mobility) and electrical energy supply (known as power-to-vehicle (P2V), vehicle-to-grid (V2G) is discussed as one of the possible development concepts for the flexible system integration of renewable energy sources (RES) and the support of the objectives of the German energy transition (aka. Energiewende). It is obvious that E-mobility, which shall produce as few emissions as possible, should be based on the exclusive use of renewable energies. At the same time, the E-mobility can help to reduce the negative effects of the grid integration of RES to the distribution grids. However, this assumes that the electric vehicles are smart integrated to the grids where they charge, meaning that they must be able to communicate and be controllable. Because per se unplanned and uncontrollable charging processes are harmful for the grid operation, especially if they occur frequently and unexpected in similar time periods, the effects can hardly be controlled and can lead to serious technical problems in practical grid operation. This paper provides an insight into the current development of E-mobility in Germany. The insight will be matched with the German development of the RES. By the combination of both sectors, the possible role of the E-mobility for the distribution grid will be depicted, which can have positive and negative aspects.

Linear diophantine uncertain linguistic-based prospect theory approach for performance evaluation of islanded microgrid-system scenarios

Renewable-energy-based hybrid microgrids can aid in achieving one of the United Nations Sustainable Development Goals,i.e.‘Affordable and clean energy’.However,experts may be faced with the challenge of selecting the best one for the electrification of an area.To avoid the challenge and realize the ultimate goal of the United Nations,the present study,therefore,proposes a novel pros-pect theory-based decision-making approach to help experts in opting for the best microgrid scenario.The proposed decision-making framework considers the risk appetite of the decision-maker,a quintessential aspect of the process.Linear diophantine uncertain lin-guistic sets are used to model the linguistic evaluations from the experts.The information from different experts is aggregated using a linear diophantine uncertain linguistic power Einstein-weighted geometric operator.Finally,the prospect-theory-based TOmada de Decisao Interativa Multicriterio approach is employed to evaluate the performance of the available microgrid scenarios and hence opt for the best microgrid scenario.The proposed framework has been used to evaluate the performance of seven possible microgrid scenarios and hence select the best one that can be implemented for rural electrification of a remote village in Assam,India.The microgrid scenario consisting of a photovoltaic-wind turbine-fuel cell-battery converter(MG_(3))has been revealed to be the best scen-ario among the seven considered microgrid scenarios.The validity of the obtained ranking results has been adjudged through a com-prehensive evaluation regarding the attenuation factor and the weights of the criteria.Moreover,previous case studies have also been solved using the proposed methodology and the results reveal a good correlation between the obtained ranking results.

Optimal cost and feasible design for gridconnected microgrid on campus area using the robust-intelligence method

In this paper,a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost.The campus microgrid needs to be optimized with further investigation,especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply.A modified combination of genetic algorithm and particle swarm optimization(MGAPSO)is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system.Then,a sustainable energy-management system is also defined to analyse the characteristics of the microgrid.The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods.The MGAPSO optimization reduces the system cost by up to 11.99%compared with the conventional methods.In the rest of the paper,the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics.Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

Use of solar PV inverters during night-time for voltage regulation and stability of the utility grid

Photovoltaic(PV)inverters are vital components for future smart grids.Although the popularity of PV-generator installations is high,their effective performance remains low.Certain inverters are designed to operate in volt-ampere reactive(VAR)mode during the night.Yet,this approach is ineffective due to the consumption of active power from the grid(as internal losses)and the regulation necessity of the direct-current(DC)bus.This paper will demonstrate the operation of a PV inverter in reactive power-injection mode when solar energy is unavailable.The primary focus is on the design of the inverter controller with respect to the synchronous rotating frame control method.The proposed novel method enables an inverter to inject the required level of reactive power to regulate the voltage levels of the utility grid within specified limits.In the process,the inverter does not absorb active power from the grid for its internal operation.The presented model has the ability to inject≤2 kVAR of reactive power at zero power factor without absorbing active power from the grid.Simulation and hardware models of the inverter were developed and tested for different reactive loads in which the hardware model represented the real-world application.The reactive power injection of the two models ran at zero power factor and produced the expected outcomes for their corresponding independent reactive loads.Henceforth,it was evident that the proposed method can enhance the efficiency of an inverter and ensure the stability of the utility grid to which it is connected.

A review on non-isolated low-power DC-DC converter topologies with high output gain for solar photovoltaic system applications

The major challenges of the high-gain DC-DC boost converters are high-voltage stress on the switch,extreme duty ratio operation,diode reverse-recovery and converter efficiency problems.There are many topologies of high-gain converters that have been widely developed to overcome those problems,especially for solar photovoltaic(PV)power-system applications.In this paper,20 high-gain and low-power DC-DC converter topologies are selected from many topologies of available literature.Then,seven prospective topologies with conversion ratios of>15 are thoroughly reviewed and compared.The selected topologies are:(i)voltage-multiplier cell,(ii)voltage doubler,(iii)coupled inductor,(iv)converter with a coupled inductor and switch capacitor,(v)converter with a switched inductor and switched capacitor,(vi)cascading techniques and(vii)voltage-lift techniques.Each topology has its advantages and disadvantages.A comparison of the seven topologies is provided in terms of the number of components,hardware complexity,maximum converter efficiency and voltage stress on the switch.These are presented in detail.So,in the future,it will be easier for researchers and policymakers to choose the right converter topologies and build them into solar PV systems based on their needs.

Automatic Generation Control in a Distributed Power Grid Based on Multi-step Reinforcement Learning

The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power grids.The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples.This can iteratively update the agents through buffer replay and real-time data.Thus the environmental data can be fully used to enhance the learning speed of the agents.This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance.Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm.The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.