Research on Parallel Analysis of Power Grid Graph Data

With the UHV (Ultra High Voltage) power grid construction and the interconnection of regional power grids, the scale of power grids in China is increasing rapidly. At the same time, significant uncertainty and variability is being introduced into power grid operation with the integration of large-scale renewable energy in power systems. All of these pose an enormous challenge to the operation control of power systems in China. For a long time, online static security analysis, as an important part of EMS (Energy Management System), has been an effective tool for power grid operation. However, it is increasingly difficult for traditional static security analysis in serial computing mode to be online applied in bulk power grids in China. A new practical parallel approach for online static security analysis is put forward in this paper. A multithread parallelism is introduced into contingency screening, detailed contingency evaluation and decision support for reducing the execution time. By employing the multithread technology, the hardware resources of multi-processor/multi-core computer can be fully used and the program can be speeded up effectively. The performance of the parallel static security analysis is demonstrated by tests on two large-scale power systems. The test results show that the proposed method can be online applied in real bulk power grids.

With the complexity and increasing demand of the power grid, more efficient scheduling methods are needed. Reinforcement learning, as an artificial intelligence technology, provides adaptive decision-making solutions that can optimize resource allocation and job scheduling based on the actual state and task requirements of the power grid. The experimental results indicate that the system has achieved significant performance improvement in different power grid operation scenarios. In terms of resource utilization, due to the higher utilization rate of the algorithm in this paper compared to the other two algorithms, the scheduling success rate is correspondingly higher than the other two algorithms. The success rate of the algorithm in this paper can reach 90.24%, which is 22.18% and 22.72% higher than GA (Genetic Algorithm) and DL (Deep Learning). Finally, in the cost-effectiveness ratio experiment, it is evident that the cost-effectiveness ratio of the algorithm proposed in this paper is much higher than that of the other two algorithms. This demonstrates the potential advantages of reinforcement learning algorithms in power grid spatial job scheduling. Future research can further explore more complex power grid operation and maintenance scenarios and algorithm optimization to continuously improve the efficiency and reliability of power grid operation and maintenance. The construction of an automatic scheduling and visualization system for power grid spatial operations provides strong support for the sustainable development of the power grid industry and the integration of renewable energy.

152. Pain plan implementation decreases postoperative opioid use, hospital length of stay and clinic resource utilization for patients undergoing elective spine surgery

Power grid anomaly detection is a basic task of the power system, and the evaluation results are of great significance for the power grid to find out the potential dangers of operation as soon as possible and take timely measures to prevent them from happening. In recent years, with the rapid development of green energy and high technology, the power grid is developing towards the smart grid. A large number of measurement devices are deployed in the power grid, and the power grid operation data is collected, transmitted and analyzed in real time. The data-driven power big data analysis approach emerges as the times require. In this paper, a data-driven approach is proposed. It constructs a high-dimensional random matrix based on power system operating data, uses the sliding window method and the random matrix single-ring theorem to continuously analyze the high-dimensional matrix, and introduces the mean spectral radius (MSR) as a specific evaluation index to realize the power grid operation situation assessment. The simulation data test results of IEEE 39 bus system verifies the feasibility and correctness of the approach for evaluating the operation situation of the power grid. This developed approach can identify abnormal conditions at the early stage of system failure, and has great guiding significance for fault inspection of power grids.

In the high-voltage power grid of coal mine, the existing adaptive topology analysis algorithm based on the correlation matrix for coal mine high-voltage power grid can effectively identify the network topology and construct a network topology analysis model to provide a basis for subsequent short-circuit current calculation and setting calculation. However, the topology analysis algorithm based on the correlation matrix takes much time. As the scale of the high-voltage power grid network of the coal mine increases, the time consumption will increase sharply as well. However, the prior-to-first-served parallel topology analysis algorithm has been used to reduce the topology analysis time consumption to some extent. On this basis, this article proposes a parallel topology analysis algorithm based on the simulated annealing and optimizes the scheduling strategy of parallel topology analysis. The simulation results show that compared with the prior-to-first-served parallel topology analysis algorithm, the parallel annealing topology analysis algorithm can further reduce the time consumption and improve the computational efficiency. And when the number of available threads increases, its computational efficiency will improve more significantly.

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.

Electric power has become an essential part of daily life: we plug our electronic devices in, switch our lights on, and expect to have power. As the availability of power is usually taken for granted in modern societies, we mostly feel annoyed at its absence and perceive the importance of power during outages which have severe effects on the public order. Blackouts have had disastrous consequences for many countries and they continue to occur frequently. Such examples demonstrate the necessity for careful analysis and planning of power grids, to ultimately increase the reliability of power grids. The power grids have evolved due to economic, environmental and human-caused factors. In addition to the contingency analysis, nowadays, the operation and planning of power grids are facing many other challenges (such as demand growth, targeted attacks, cascading failures, and renewable energy integration). Thus, many questions arise, including: which buses (nodes) to connect with a new line (link)? What are the impacts of malicious attacks on power grids? How may an initial failure result in a cascade of failures? How to prepare for the integration of renewable energy? Answering such questions requires developing new concepts and tools for analysing and planning of power grids. Power grids are one of the largest and the most complex man-made systems on earth. The complex nature of power grids and its underlying structure make it possible to analyse power grids relying on network science. The applications of network science on power grids have shown the promising potential to capture the interdependencies between components and to understand the collective emergent behaviour of complex power grids. This thesis is motivated by the increasing need of reliable power grids and the merits of network science on the investigation of power grids. In this context, relying on network science, we model and analyse the power grid and its near-future challenges in terms of line removals/additions, malicious attacks, cascading failures, and renewable integration.

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.

When the power grid is disturbed to a certain degree, it may be extremely deteriorated. At this time, corresponding control measures need to be taken to eliminate or reduce the risks. For an unsafe normal state, the system can be restored to a safe state through preventive control. This paper proposes a risk-based power grid operational control strategy by using the optimal power flow method to solve the problem of power grid operational control based on risk parameters with the goal of minimizing the power grid operating risks. Under the condition of satisfying steady-state operational constraints, by adjusting the control measures that can be used in the system, such as active power generation, the safe operational state of the system with the least operational risk is realized. To avoid the poor effectiveness of alternating current power flow of large power grid, linearized power flow model is employed. Simulation results using an IEEE 118-node system and East China Power Grid system. The proposed operational control strategy can achieve the effect of eliminating or reducing operating risks, which provide effective decision-making for the dispatch operators to deal with risks.

The now ubiquitous use of information technology poses a crucial challenge to the cyber security of power grid operations, one that has aroused serious concerns from both industry and academia. The state-of-the-art research either focuses on the vulnerability assessment of particular types of components or concentrates on the prevention and mitigation of cyber attacks from the power grid’s perspective. Complete causal chains connecting component vulnerabilities to cyber attacks causing malicious system-wide effects are unclear, which hinders cyber-attack prevention and the consolidation of affected components. To bridge this research gap, this survey aims to study two issues that need further investigation. On one hand, the causal relationship between power grid component vulnerabilities and resulting cyber attacks has not been thoroughly explored. On the other hand, the evolvement of cyber attacks, from initial attempt to resulting serious consequences has not been resolved. To study these two issues, we first analyze various stakeholders and associated information flows in diverse applications in power grid operation. Second, we summarize the root cause of cyber attacks in the vulnerability of communication protocols. Third, a multi-stage model is proposed to describe the cyber vulnerabilities, which reveals potential attacks and their evolvement on power grid components at each stage, assesses the associated impact on the entire power grid, and elucidates possible countermeasures. With the above efforts, this survey establishes a complete causal chain from component-wise vulnerability to system-wide impact assessment for enhancing cyber security, and potential research directions for enhancing power grid cyber security are identified.

Deep neural network (DNN) has become the leading technology to realize Artificial Intelligence (AI). As DNN models become larger and more complex, so do datasets. Being able to efficiently train DNNs in parallel has become a crucial need. Data Parallelism (DP) is the widest-used solution today to accelerate DNN training but could be inefficient when processing DNNs with large-size parameters. Hybrid Parallelism (HP), which applies different parallel strategies on different parts of DNNs, is more efficient but requires advanced configurations. Not all AI researchers are experts in parallel computing, thus automating the configuration of HP strategies is very desirable for all AI frameworks. We propose a parallel semantics analysis method, which can analyze the trade-offs among different kinds of parallelisms and systematically choose the HP strategies with good training time performance. We demonstrated experimentally 260% speedup when applying our method compared to using a conventional DP approach. With our proposal, AI researchers would be able to focus more on AI algorithm research without being disturbed by parallel analysis and engineering concerns.

Large-scale power grid will inevitably become a new trend in the development of power systems, thus the power grid structure will become increasingly complex. The traditional topology analysis method can not meet the requirements of real-time. Therefore a parallel topology analysis preconditioning strategy based on community discovery algorithm was proposed. This strategy effectively utilized the maximum edge betweenness to achieve the topological division of the power grid, which laid the foundation for the parallel topology formation mechanism between the multiple regions of the power grid. On the basis of topological division, the regional grid can be parallel and asynchronous to form the topology, thus according to certain communication and coordination rules, the construction of the global power grid topology can be completed through the distributed computing, which can significantly improve the efficiency of topology analysis.

Due to the rapid advances of integrated circuit technology, the size of power distribution network (power grid) is becoming larger and larger. There are usually multi-million nodes on a power grid. Analyzing these huge power grids has become very expensive in terms of both time and memory. This paper presents an efficient parallel implementation of the Additive Schwarz Method (ASM) for IR-drop analysis of large-scale power grid. Based on distributed memory system, a new data storage method is proposed to overcome memory bottleneck of traditional methods. Techniques including overlapping in multiple layer and irregular power grid, via detection and grouping are utilized to accelerate the simulation. Moreover, a new communication strategy exhibiting minimum communication overhead is proposed. The proposed method is very accurate in the final solution, with the maximum error less than 0.1mv. Experimental results on industrial medium size benchmarks show that the proposed method achieves more than 110X speedup over a state-of-the-art direct LU solver. The proposed approach can easily solve very large-scale benchmarks, while LU solver fails to obtain the solution because of system memory limitation. It is the first time reported in literature that IR-drop analysis of power grid with over 190M nodes is successfully solved within 5 minutes.

Wind turbines are increasingly connected to the bulk power grid through electronic devices. Compared with the dynamic characteristics of traditional power system, transient characteristics are dramatically changed. Recognizing the transient characteristics of large-scale wind power connected to the bulk power grid and proposing practical and feasible transient control strategies for wind turbines are important to the safe and stable operation of current power grid. This paper focuses on the interactive characteristics of double-fed induction generator (DFIG) coupled to the power grid of receiving-end. A DFIG-based wind farm can be reduced to a single DFIG wind turbine by simplifying it, transient dynamics of the wind turbine, such as output fault current characteristics, under different fault levels of power grid are analyzed. Moreover, according to the fault ride through (FRT) standard of wind farms, FRT control strategies of DFIG wind turbines under different fault levels of power grid are presented to form a coordinated fault ride-through control scheme. Finally, a case study was conducted with MATLAB/Simulink. The simulation results verified the effectiveness and feasibility of the interactive characteristics analysis and the proposed FRT control scheme of DIFG-based wind farms connected to the receiving power grid.

Due to the weak support of renewable energy power generation equipment, the voltage support capacity after connecting to the power grid is difficult to meet the needs of safe operation of the power grid. Especially when large-scale renewable energy units, DC transmission and other power equipment are connected to the power system, the problem of transient overvoltage has become the main factor restricting the transmission of renewable energy. By configuring the condenser, the power grid voltage support strength can be effectively improved and the output limit of renewable energy can be improved. However, in the actual production and operation of power grid, there is not only a lack of quantitative analysis and research on improving the transmission capacity of renewable energy, but also a lack of technical and economic analysis methods for the configuration of condenser. Therefore, based on the quantitative evaluation of MRSCR on the power grid voltage support strength, through the joint calculation of PSD-BPA software and production simulation model, this study quantitatively analyzes the impact on the renewable energy consumption capacity after the distributed dimmer is connected to the system, and quantitatively evaluates the technical economy of configuring the dimmer in the renewable energy collection station by constructing the technical economy model of the dimmer. The results show that the condenser can not only improve the transmission capacity of renewable energy, but also have good economic benefits.