Extreme day-ahead renewables scenario selection in power grid operations

In order to understand the impact of grid-connected multi-source small power transmission and distribution on the operation of power grid, this paper will carry out relevant research, mainly discuss the basic concept of grid-connected multi-source small power transmission and distribution, and then discuss its specific impact, and finally put forward countermeasures. The strategy in this paper can avoid the related influence, make the multi-source small power supply can be integrated into the large power grid smoothly, and ensure the stable operation of the large power grid.

In recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occurrence frequency in historical operation data is low, which affects the modeling accuracy for scenario generation. Meanwhile, extreme operation scenarios in the form of discrete temporal data lack corresponding modeling methods. Therefore, this paper proposes a definition and generation framework for extreme power grid operation scenarios triggered by extreme weather events. Extreme operation scenario expansion is realized based on the sequential Monte Carlo sampling method and the distribution shifting algorithm. To generate equipment failure scenarios in discrete temporal data form and extreme output scenarios in continuous temporal data form for renewable energy, a Gumbel-Softmax variational autoencoder and an extreme conditional generative adversarial network are respectively proposed. Numerical examples show that the proposed models can effectively overcome limitations related to insufficient historical extreme data and discrete extreme scenario training. Additionally, they can generate improved-quality equipment failure scenarios and renewable energy extreme output scenarios and provide scenario support for power grid planning and operation.

High-wattage demand-side appliances such as Plug-in Electric Vehicles (PEVs) are proliferating. As a result, information on the charging patterns of PEVs is becoming accessible via smartphone applications, which aggregate real-time availability and historical usage of public PEV charging stations. Moreover, information on the power grid infrastructure and operations has become increasingly available in technical documents and real-time dashboards of the utilities, affiliates, and the power grid operators. The research question that this study explores is: Can one combine high-wattage demand-side appliances with public information to launch cyberattacks on the power grid? To answer this question and report a proof of concept demonstration, the study scrapes data from public sources for Manhattan, NY, USA using the electric vehicle charging station smartphone application and the power grid data circulated by the U.S. Energy Information Administration, New York Independent System Operator, and the local utility in New York. It then designs a novel data-driven cyberattack strategy using state-feedback based partial eigenvalue relocation, which targets frequency stability of the power grid. The study establishes that while such an attack is not possible at the current penetration level of PEVs, it will be practical once the number of PEVs increases.

In order to accurately identify the risk behavior in the power grid operation, ensure the life safety of operators and the development of power enterprises, the power grid operation behavior control and automatic risk early warning platform based on AI+video technology is designed. The platform is composed of infrastructure layer, data resource layer, application support layer, platform management, and AI+video monitoring layer. The infrastructure layer is mainly responsible for collecting video of grid operation behavior, and considering the actual situation of grid operation, and accurately extracting the prospect target in the video image (i. e. monitoring target) under the jitter condition of the video camera. The data resource layer and the application support layer are responsible for selecting different power grid operation risk behavior judgment algorithms under different application scenarios; the platform management and AI+video monitoring layer are responsible for showing the monitoring and early warning results of the operation behavior of the power grid to users. The experimental results show that, under the condition of monitoring camera jitter, the prospect of grid behavior control and automatic risk warning platform based on AI+video technology is closest to the benchmark map, and the risk behavior identification results are consistent with the relevant standards of power grid operation in China. The area under the ROC curve has remained above 0.6, which can effectively identify the risk behavior in power grid operation, reduce the risk behavior of network operation and improve the safety of grid operation.

With the rapid development of power grid, a large number of applications of intelligent equipment, and there might be massive, multivariate and complex data during the power grid regulation and operation, resulting in that power grid security control is facing new opportunities and challenges. In this paper, we first applied the data analysis methods such as time series and clustering to make descriptive and predictive analysis of massive data in power system. In addition, we further discovered the data association and potential additional value based on cloud computing technology, realizing real time data information of power system with cloud computing technology and reaching platform integration of “Collection-Identification-Processing-Integration”. Secondly, in order to truly realize the power grid operation under risk control, controllability and long-term in control, based on the integrated platform we explored the establishment of a set “proactive prevention beforehand, active response in the process, and proper disposal afterwards” all-round power grid operation safety management and control mechanism. Finally, the feasibility of the proposed security management and control mechanism was verified by a typical case in Tianjin region.

At present, the measurement and Modeling Research of power system mainly focuses on generator, excitation system, prime mover and its regulation system, power load and so on. However, there is relatively little research on the measured modeling of dynamic reactive power compensation device. In the simulation calculation of power system, the dynamic reactive power compensation device usually adopts a highly simplified model, or even ignores it. The accuracy and applicability of the model are limited, which directly affects the reactive power voltage regulation capacity of renewable energy stations and the consumption of renewable energy. Acceptance. There are great differences in the characteristics of the actual equipment, which may lead to the inconsistency between the calculation results and the actual situation, the relevant power grid planning and operation will lose the basis, and the power grid operation will face great hidden dangers. In the field of field measurement and modeling of power system, the dynamic reactive power compensation device of renewable energy station lacks field measurement and modeling technology, which can not meet the requirements of accurate modeling of power system and safe and stable operation of large power grid The operation of power grid will face great hidden dangers. It is urgent to study the model and parameter technology of dynamic reactive power compensation device in renewable energy station.

The integration of renewable energy into the grid has led to problems such as low utilization rate of energy storage resources (“underutilization after construction”) and insufficient system stability. This paper studied the co-evolution mechanism of power market entities empowered by shared energy storage. Based on the interaction among power generation enterprises, power grid operators, and government regulatory agencies, this paper constructed a three-party evolutionary game model. The model introduced a dynamic reward and punishment mechanism as well as a random interference mechanism, which makes it more in line with the actual situation. The stability conditions of the game players were analyzed by using stochastic differential equations, and the influences of key parameters and incentive mechanisms on the stability of the game players were investigated through numerical simulation. The main research results showed the following: (1) The benefits of shared energy storage and opportunistic gains had a significant impact on the strategic choices of power generation companies and grid operators. (2) The regulatory efficiency had significantly promoted the long-term stable maintenance of the system. (3) Dynamic incentives were superior to static incentives in promoting cooperation, while the deterrent effect of static penalties is stronger than that of dynamic penalties. (4) The increase in the intensity of random disturbances led to strategy oscillation. This study suggested that the government implement gradient-based dynamic incentives, maintain strict static penalties to curb opportunism, and enhance regulatory robustness against uncertainty. This research provided theoretical and practical inspirations for optimizing energy storage incentive policies and promoting multi-subject coordination in the power market.

Affected by the north of the Jianghuai cyclone on November 17,2020, a rare rain and snow freezing weather occurred in Jilin Province, which is the largest and widest range of rainstorm, snowstorm, freezing rain and gale disaster weather since its own meteorological records. This paper provides a reference and basis for future power grid planning, design and operation, and maintenance of disaster prevention and mitigation by analyzing the causes of this weathering process and its impact on power grid operation.

Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.

With the rapid development of China’s economy, the power network specifications are expanding and the network structure is becoming more and more complex. Power grid dispatching is the key to ensure the safe and stable operation of power grid. Power grid dispatch log is an important data source to reflect the operation of power grid and an important means to monitor the daily operation of power grid. Network dispatching log classification is an important application of log text analysis and mining. At present, there are many methods for network dispatching log classification, including naive bayesian method, support vector machine, neural network model and so on. However, no matter what classification method is used, scheduling log text needs to be preprocessed and converted into vector form before model training and classification. At present, the research of word vector mainly focuses on the Internet, while the feature extraction of power grid dispatch log from word vector generation is less. In this paper, a method of extracting log word vectors from power grid dispatching based on bidirectional LSTM combined dictionary is proposed. Firstly, the original log is preprocessed according to the lexicon, and word segmentation is performed on the original log by means of bidirectional LSTM combined with dictionary to obtain word segmentation results. Then, every word is transformed into a word vector through the skip-gram model. Finally, the generated word vector is used to classify the power grid dispatch logs.

Aiming at the influence of the access of high-proportion clean energy into the power grid, a comprehensive regulation model for such multiple energy as hydropower, thermal power, time-shiftable load, battery energy storage, electric thermal storage and nuclear power as well as the tie line and frequency was proposed. Through combing the operational characteristics, the operation process of power grid was divided into normal regulation domain, abnormal regulation domain and emergency control domain, and the unconventional peak regulation of thermal power unit was classified into the abnormal regulation domain. The flexible adjustment precaution calculation and monitoring method for the power was established, which could provide the basis for the monitoring and control of the operation of power grid. The results show that the characteristics of each stage in the operation of power grid are different, and the required adjustment variables are also different. Similarly, the characteristics of each operation variable are different. Therefore, the division of three operational domains is in line with the actual operation of power grid.

Distributed energy resources on distribution networks: A systematic review of modelling, simulation, metrics, and impacts

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

As the most likely applicable fault current limiting device in the future, saturated iron-core superconducting fault current limiter (SISFCL) has many superior attributes. In a SISFCL, DC excitation system plays a key role during the operation in power grid. It not only outputs dc excitation current to make iron core saturated, but also absorbs magnetic energy stored in superconducting coil and suppresses the reverse voltage, a disaster to superconducting coil, high speed switch IGBTs and other circuit elements, to a safe level instantly when a fault happens. Field suppression unit is mainly used to achieve the second function mentioned above. This paper introduces SISFCL device and its DC excitation system briefly, then analyzes and discusses field suppression unit in detail. This work bases on research and manufacturing of 220 kV/300 MVA SISFCL, which has been developed and put into operation in power grid in China.

At present, the evaluation of power grid operations is still in the qualitative stage, which is lack of quantitative evaluation and analysis methods, and cannot meet the requirements of modern power grid development and the needs of the lean management of Power Grid Corp. Based on the characteristics of the power grid operations, constructing the power distribution network operation monitoring platform based on big data technology is an effective way to achieve quantitative evaluation of operational efficiency, power supply capacity, and region monitoring. This paper introduces the construction and application of big data based power grid network operation monitoring platform, including the system architecture of the power distribution network operation monitoring platform, the evaluation model of power supply capacity and operation efficiency of distribution network, functions of data access and quality control, power grid operation online visual monitoring analysis, and operation monitoring and control cooperation. Actual operation of the system shows that the power grid network operation monitoring platform provides a quantitative evaluation and decision basis for power grid planning, construction and production operations, makes full use of the existing data value, provides decision support for the business sector, and further enhances the strategic decision-making and operation monitoring center operation control and risk prevention ability. At the same time, the power operation monitoring platform has also played a good reference role for the construction of other power operation monitoring platform.