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.

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.

An Approach for Cost-Efficient Grid Integration of Distributed Renewable Energy Sources

We describe a specific approach to capacity man a ge ment for distribution grids. Based on simulations, it has been found that by curtailing a maximum of 5% of the yearly energy production on a per-generator basis, distribution grid connection capacity can be doubled. We also present the setting and fi rst results of a fi eld test for validating the approach in a rural distribution grid in northern Germany.

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.

Grid tie inverter energy stabilizing in smart distribution grid with energy storage

With the growing deployment of smart distribution grid,it has become urgent to investigate the smart distribution grid behavior during transient faults and improve the system stability.The feasibility of segmenting large power grids and multiple smart distribution grids interconnections using energy storage technology for improving the system dynamic stability was studied.The segmentation validity of the large power grids and smart distribution grid inverter output interconnections power system using energy storage technology was proved in terms of theoretical analysis.Then,the influences of the energy storage device location and capacity on the proposed method were discussed in detail.The conclusion is obtained that the ESD optimal locations are allocated at the tie line terminal buses in the interconnected grid,respectively.The effectiveness of the proposed method was verified by simulations in an actual power system.

Combining Market-Based Control with Distribution Grid Constraints when Coordinating Electric Vehicle Charging

The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.

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.

Research on IPv6 Transition Evolvement and Security Architecture of Smart Distribution Grid Data Communication System

Smart distribution grid needs data communication systems as a support to complete their important functions. The smart distribution grid of the data and information are increasingly adopting internet protocol and Ethernet technology. The IP addresses are more and more important for the smart distribution grid equipment. The current IPv4 protocol occupies a dominant position; therefore, the challenges of the evolution to IPv6 and network security are faced by data communication systems of the smart distribution grid. The importance of data communications network and its main bearer of business were described. The data communications network from IPv4 to IPv6 evolution of the five processes and four stages of the transition were analyzed. The smart distribution grid data communications network security and types of their offensive and defensive were discussed. And the data communications network security architecture was established. It covers three dimensions, the security level, the communications network security engineering and the communications network security management. The security architecture safeguards the evolution to IPv6 for the smart distribution grid data communication systems.

Connecting Wind Turbine Generator to Distribution Power Grid--A Preload Flow Calculation Stage

A distribution grid is generally characterized by a high R/X （resistance/reactance） ratio and it is radial in nature. By design, a distribution grid system is not an active network, and it is normally designed in such a way that power flows from transmission system via distribution system to consumers. But in a situation when wind turbines are connected to the distribution grid, the power source will change from one source to two sources, in this case, network is said to be active. This may probably have an impact on the distribution grid to whenever the wind turbine is connected. The best way to know the impact of wind turbine on the distribution grid in question is by carrying out load flow analysis on that system with and without the connection of wind turbines. Two major fundamental calculations： the steady-state voltage variation at the PCC （point of common coupling） and the calculation of short-circuit power of the grid system at the POC （point of connection） are necessary before carrying out the load flow study on the distribution grid. This paper, therefore, considers these pre-load flow calculations that are necessary before carrying out load flow study on the test distribution grid. These calculations are carded out on a test distribution system.

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.

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.

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.

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.

A cognitive spectrum allocation scheme for data transmission in smart distribution grids

As the communication needs in the smart distribution grid continue to rise,using existing resources to meet this growing demand poses a significant challenge.This paper researches on spectrum allocation strategies utilizing cognitive radio(CR)technology.We consider a model containing strong time-sensitive and regular communication service requirements such as distribution terminal communication services,which can be seen as a user with primary data(PD)and weak time-sensitive services such as power quality monitoring,which can be seen as a user with secondary data(SD).To fit the diversity of services in smart distribution grids(SDGs),we formulate an optimization problem with two indicators,including the sum of SD transmission rates and the maximum latency of them.Then,we analyze the two convex sub-problems and utilize convex optimization methods to obtain the optimal power and frequency bandwidth allocation for the users with SD.The simulation results indicate that,when the available transmission power of SD is low,Maximization of Transmission Sum Rate(MTSR)achieves lower maximum transmit time.Conversely,when the available transmission power is high,the performance of Minimization of the Maximum Latency(MML)is better,compared with MTSR.

A calculation method for a power user's CIC under specific conditions in smart distribution grid

This paper presents a practical method for calculating a power user’s customer interruption costs(CIC)under specific conditions.This novel method has been developed,based on the CIC results predicted by Lawrence Berkeley National Laboratory(LBNL),so that the key factors,such as customer type,customer size,interruption occurrence time and interruption duration can be considered.As compared to the LBNL method,the method proposed here is easy to understand and easy to execute with an acceptable error.It lays a solid foundation for further investigation of distributed generators and demand response in assessing reliability value of smart distribution grid(SDG).The effectiveness of the proposed method is confirmed through the assessment of RBTS-Bus2.

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.

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.

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.

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.