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.

Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids——An interview with Zhou Xiaoxin, chief scientist of "973 Program"

At the end of last year, the editors from Power and Electrical Engineers interviewed Zhou Xiaoxin on "Fundamental Research on Enhancing Operation Reliability for Large-Scale Interconnected Power Grids", a project of "973 Program". Mr. Zhou, the chief engineer of China Electric Power Research Institute(CEPRI) and an academician of Chinese Academy of Sciences, is the chief scientist in charge of this research project.

Double Pressure on Power Grids——The demand for electricity is dropping while the electricity price is not adjusted properly

Since October 2008,China's social consumption of electricity had,for the first time,grown negatively compared to the same period of the previous year,and in November the negative growth range further expanded. The major pressure faced by the electricity industry has now turned from the contradiction between coal and electricity to electricity quantity. This is undoubtedly a true and new test to electricity enterprises which get used to high growth but are now suffering great losses. The reform of electricity system has already been in great difficulties and now is getting into a more serious situation. In order to help readers improve their knowledge and understanding of the current tough situation faced by the electricity industry and discuss how to alleviate and get through the difficulty resulted from the economic crisis "encountered once every one hundred years" by joint efforts of all parties concerned,a Seminar on Crisis and Countermeasures for Electricity Industry was held on November 20,2008. Here are some extracts from the speeches of four experts.

Sustainable Investment Forecasting of Power Grids Based on theDeep Restricted Boltzmann Machine Optimized by the Lion Algorithm

This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the Lion algorithm(LA).Firstly,two factors including transmission and distribution price reform(TDPR)and 5G station construction were comprehensively incorporated into the consideration of influencing factors,and the fuzzy threshold method was used to screen out critical influencing factors.Then,the LA was used to optimize the parameters of the DRBM model to improve the model’s prediction accuracy,and the model was trained with the selected influencing factors and investment.Finally,the LA-DRBM model was used to predict the investment of a power grid enterprise,and the final prediction result was obtained by modifying the initial result with the modifying factors.The LA-DRBMmodel compensates for the deficiency of the singlemodel,and greatly improves the investment prediction accuracy of the power grid.In this study,a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model,and a comparison with the RBM,support vector machine(SVM),back propagation neural network(BPNN),and regression model was conducted to verify the superiority of the model.The conclusion indicates that the proposed model has a strong generalization ability and good robustness,is able to abstract the combination of low-level features into high-level features,and can improve the efficiency of the model’s calculations for investment prediction of power grid enterprises.

Agents for Smart Power Grids

The future of electricity systems will compose of small-scale generation and distribution where end-users will be active participants with localized energy management systems that are able to interact on a free energy market. Software agents will most likely control power assets and interact together to decide the best and safest configuration of the power grid system. This paper presents a design of agents that can be deployed in real-time with capabilities that include optimization of resources, intensive computation, and appropriate decision-making. Jordan 51-bus system has been used for simulation with a total generation capacity of 4050 MW of which 230 MW represents renewable energy. The economic analyses demonstrated the use of smart grid technologies with 2016 generation—load profiles for nominal liquified gas (NLG) prices and ±20% sensitivity analysis. The results have shown variations in the range of 1% in the price of MWh with smart grid technologies. These variations are mainly driven by the fact that agents shift power generation to renewable power plants to produce maximum power at peak hours. As a result, there is a positive economic impact in both NLG ± 20% sensitivity analysis, due to the fact that agents coordinate to better displace expensive thermal generation with renewable generation. It is evident that renewable resources compensate for power at peak times and provide economic benefits and savings.

Automatic Voltage Control of Differential Power Grids Based on Transfer Learning and Deep Reinforcement Learning

In terms of model-free voltage control methods,when the device or topology of the system changes,the model’s accuracy often decreases,so an adaptive model is needed to coordinate the changes of input.To overcome the defects of a model-free control method,this paper proposes an automatic voltage control(AVC)method for differential power grids based on transfer learning and deep reinforcement learning.First,when constructing the Markov game of AVC,both the magnitude and number of voltage deviations are taken into account in the reward.Then,an AVC method based on constrained multiagent deep reinforcement learning(DRL)is developed.To further improve learning efficiency,domain knowledge is used to reduce action space.Next,distribution adaptation transfer learning is introduced for the AVC transfer circumstance of systems with the same structure but distinct topological relations/parameters,which can perform well without any further training even if the structure changes.Moreover,for the AVC transfer circumstance of various power grids,parameter-based transfer learning is created,which enhances the target system’s training speed and effect.Finally,the method’s efficacy is tested using two IEEE systems and two real-world power grids.

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.

Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicators inpower grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault powergrids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient.The tedious training process of the reinforcement learning model can be conducted offline, so the model showssatisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Consideringthat the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correctionframework is proposed to modify the learning model. In the framework, the action of load shedding is correctedaccording to sensitivity analysis results under a small discrete increment so as to match the constraints of line flowlimits. The results of case studies indicate that the proposed method is practical for fast and safe power grid loadtransfer.

An overview of the reliability metrics for power grids and telecommunication networks

Power grids deliver energy,and telecommunication networks transmit information.These two facilities are critical to human society.In this study,we conduct a comprehensive overview of the development of reliability metrics for power grids and telecommunication networks.The main purpose of this review is to promote and support the formulation of communication network reliability metrics with reference to the development of power grid reliability.We classify the metrics of power grid into the reliability of power distribution and generation/transmis-sion and the metrics of telecommunication network into connectivity-based,performance-based,and state-based metrics.Then,we exhibit and discuss the difference between the situations of the reliability metrics of the two systems.To conclude this study,we conceive a few topics for future research and development for telecommunication network reliability metrics.

Characteristic Analysis of UHVAC/DC Hybrid Power Grids and Construction of Power System Protection

Strong DC coupling with weak AC and large-scale renewable energy integration are the two significant characteristics of ultra-high-voltage AC/DC(UHVAC/DC)hybrid power grids in China.Strong coupling between AC and DC grids and the different integration performance of renewable energy sources have profoundly changed the stability characteristics of the power system.The traditional stability control system is inadequate for the stability control of UHVAC/DC power grids.This paper analyzes the requirements for constructing an integrated defense system in a UHVAC/DC hybrid power grid(i.e.power system protection).The definition,connotation,and designing principles of power system protection are put forward.The relationship between the power system protection and the traditional three-defense lines is investigated.The design principles,general hardware structure and main functions of a power system protection are presented.Key problems and technologies are specified in the construction of the power system protection.

TOPOLOGY AND CASCADING LINE OUTAGES IN POWER GRIDS

Motivated by the small world network research of Watts ＆ Strogatz, this paper studies relationships between topology and cascading line outages in electric power grids. Cascading line outages are a type of cascading collapse that can occur in power grids when the transmission network is congested. It is characterized by a self-sustaining sequence of line outages followed by grid breakup, which generally leads to widespread blackout. The main findings of this work are twofold： On one hand, the work suggests that topologies with more disorder in their interconnection topology tend to be robust with respect to cascading line outages in the sense of being able to support greater generation and demand levels than more regularly interconnected topologies. On the other hand, the work suggests that topologies with more disorder tend to be more fragile in that should a cascade get started, they tend to break apart after fewer outages than more regularly interconnected topologies. Thus, as has been observed in other complex networks, there appears to be a tradeoff between robustness and fragility. These results were established using synthetically generated power grid topologies and verified using the IEEE 57 bus and 188 bus power grid test cases.

Pragmatic multi-stage simulated annealing for optimal placement of synchrophasor measurement units in smart power grids

Conventional power grids across the globe are reforming to smart power grids with cutting edge technologies in real time monitoring and control methods. Advanced real time monitoring is facilitated by incorpor- ating synchrophasor measurement units such as phasor measurement units （PMUs） to the power grid monitoring system. Several physical and economic constraints limit the deployment of PMUs in smart power grids. This paper proposes a pragmatic multi-stage simulated annealing （PMSSA） methodology for finding the optimal locations in the smart power grid for installing PMUs in conjunction with existing conventional measurement units （CMUs） to achieve a complete observability of the grid. The proposed PMSSA is much faster than the conventional simulated annealing （SA） approach as it utilizes controlled uphill and downhill movements during various stages of optimiza- tion. Moreover, the method of integrating practical phasor measurement unit （PMU） placement conditions like PMU channel limits and redundant placement can be easily handled. The efficacy of the proposed methodology has been validated through simulation studies in IEEE standard bus systems and practical regional Indian power grids.

Learning-based Data Analytics:Moving Towards Transparent Power Grids

this paper,we present the learning-based data analytics moving towards transparent power grids and provide some possible extensions including machine learning,big data analytics,and knowledge transferring.The closed loops of data and knowledge are illustrated and the challenges for establishing the closed loops are discussed.General ideas and recent developments in supervised learning,unsupervised learning,and reinforcement learning are presented together with extensions for power system applications.Furthermore,much emphasis is placed on privacypreserving data analysis,transfer of knowledge,machine learning for causal inference,scalability and flexibility of data analytics,and efficiency and reliability of computation.Existing integrated solutions in the industry featuring the Industrial Internet and the digital grid are also introduced.

A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

The power grid operation process is complex,and many operation process data involve national security,business secrets,and user privacy.Meanwhile,labeled datasets may exist in many different operation platforms,but they cannot be directly shared since power grid data is highly privacysensitive.How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid.Therefore,this paper proposes federated learning named entity recognition method for the power grid field,aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security requirements.We decompose the named entity recognition(NER)model FLAT(Chinese NER Using Flat-Lattice Transformer)in each platform into a global part and a local part.The local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled data.The global part is learned across different operation platforms to capture the shared NER knowledge.Its local gradients fromdifferent platforms are aggregated to update the global model,which is further delivered to each platform to update their global part.Experiments on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.

DC active power filter based on model predictive control for DC bus overvoltage suppression of accelerator grid power supply

The China Fusion Engineering Test Reactor plans to build a 200 k V/25 A acceleration grid power supply(AGPS)for the negative-ion-based neutral beam injector prototype system.The AGPS uses a rectifier-inverter-isolated step-up structure.There is a DC bus between the rectifier and the inverter.In order to limit DC bus voltage ripple and transient fluctuations,a large number of capacitors are used,which degrades the reliability of the power supply and occupies a large amount of space.This work finds that due to the difference in the turn-off time of the rectifier and the inverter,the capacitance mainly depends on the rectifier current when the inverter is turned off.On this basis,an active power filter(APF)scheme is proposed to absorb the current.To enhance the dynamic response ability of the APF,model predictive control is adopted.In this paper,the circuit structure of the APF is introduced,the prediction model is deduced,the corresponding control strategy and signal detection method are proposed,and the simulation and experimental results show that APF can track the transient current of the DC bus and reduce the voltage fluctuation significantly.

Improved resilience measure for component recovery priority in power grids

Given the complexity of power grids,the failure of any component may cause large-scale economic losses.Consequently,the quick recovery of power grids after disasters has become a new research direction.Considering the severity of power grid disasters,an improved power grid resilience measure and its corresponding importance measures are proposed.The recovery priority of failed components after a disaster is determined according to the influence of the failed components on the power grid resilience.Finally,based on the data from the 2019 Power Yearbook of each city in Shandong Province,China,the power grid resilience after a disaster is analyzed for two situations,namely,partial components failure and failure of all components.Result shows that the recovery priorities of components with different importance measures vary.The resilience evaluations under different repair conditions prove the feasibility of the proposed method.

What blockchain can do for power grids?

The aim of this work is to provide an up-to-date comprehensive review of the peer-reviewed articles,the research projects and the entrepreneurial efforts that consider the utilization of blockchain technology in the energy sector in general and the power grid in particular.Through our review study we systematically classify existing applications of blockchain technology in the energy sector according to their field of activity.The comprehensive and holistic picture provided aims to contribute to the body of knowledge of the applicability of blockchain technology within the energy sector and pave the way for further research in this field.

Impact of interconnections and renewable energy integration on the Philippine-Sabah Power Grid systems

This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah,Malaysia,as part of the Association of Southeast Asian Nations(ASEAN)Power Grid.This study focuses on modeling and evaluating the dynamic performance of the interconnected system,considering the high penetration of renewable sources.Power flow,small signal stability,and transient stability analyses were conducted to assess the ability of the proposed linked power system models to withstand small and large disturbances,utilizing the Power Systems Analysis Toolbox(PSAT)software in MATLAB.All components used in the model are documented in the PSAT library.Currently,there is a lack of publicly available studies regarding the implementation of this specific system.Additionally,the study investigates the behavior of a system with a high penetration of renewable energy sources.Based on the findings,this study concludes that a system is generally stable when interconnection is realized,given its appropriate location and dynamic component parameters.Furthermore,the critical eigenvalues of the system also exhibited improvement as the renewable energy sources were augmented.

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.