Xiaochuan Luo

The increasing penetration of inverter-based resources introduces new dynamic challenges to modern power grids, such as sub- and super-synchronous oscillations and other faster dynamics. These dynamics are typically fast in nature and are difficult to accurately model and analyze using standard transient stability (TS) methods, necessitating the need for electromagnetic transient (EMT) analysis. However, EMT simulations are notoriously slow for large-scale grids due to both equation formulations and computational limitations. To overcome this challenge, EMT-TS hybrid simulation is often used, since it offers a balanced trade-off between accuracy and speed, making it feasible to perform EMT analysis on large systems. One open question about EMT-TS hybrid simulation is the accuracy of the EMT-TS boundary or interface. This paper introduces an error index to quantify EMT-TS hybrid interface errors, identifies conditions where the hybrid simulation approach may become inaccurate, and suggests EMT region expansions to improve the simulation accuracy. Additionally, a three-sequence hybrid interface model is proposed to mitigate inaccuracies caused by unbalanced conditions.

[Most recently, there have been significant efforts by power system operators in studying generation and transmission uncertainties in operation planning. With the increasing penetration of weather-dependent renewable generation and frequent geo-climate events, power system operators have begun to predict weather changes and preventively mitigate their impacts on power generation and transmission. Uncertainty evaluation and subsequent preventive mitigation actions have become important tasks in operating regions with significant renewable generation. Amid these efforts, artificial intelligence and machine learning are playing instrumental roles in the development of new tools. Based on a handful of public reports, this article discusses the emerging new problem of operational uncertainties for power system operators and outlines a systematic approach that involves risk evaluation and mitigation in both generation scheduling and transmission operation. It is envisioned that risk-based operation will be the new paradigm for power system security assessment.

The electric power grid faces unprecedented challenges as it evolves to meet rising energy demands, integrate renewable sources, and adapt to new technologies. This paper explores how cloud computing and artificial intelligence (AI) are emerging as transformative solutions for grid modernization. Cloud platforms provide the scalable infrastructure and computing power needed to process vast amounts of grid data, while AI enables advanced analytics and decision-making capabilities. Together, these technologies are revolutionizing grid operations across multiple domains. Key applications include intelligent solutions for data center integration, accelerating generator interconnection processes, facilitating distributed energy resource management at scale, enabling data-driven predictive maintenance, enhancing grid resilience and self-healing, optimizing energy generation and distribution, and implementing advanced cybersecurity measures. The article discusses several real-world implementations demonstrating significant operational improvements and cost savings for utilities. It also examines emerging trends like generative AI and multiagent systems that promise to further enhance grid capabilities. While highlighting the immense potential of cloud and AI technologies, the authors emphasize that successful adoption requires a holistic approach encompassing organizational change, workforce development, and regulatory evolution. Looking ahead, the convergence of cloud and AI is poised to create a more intelligent, efficient, and reliable power grid to meet future energy needs. However, realizing this vision will require ongoing collaboration among utilities, technology providers, regulators, and researchers.

Understanding and quantifying the impact of severe weather events on the electric transmission and distribution system is crucial for ensuring its resiliency in the context of the increasing frequency and intensity of extreme weather events caused by climate change. While weather impact models for the distribution system have been widely developed during the past decade, transmission system impact models lagged behind because of the scarcity of data. This study demonstrates a weather impact model for predicting the probability of failure of transmission lines. It builds upon a recently developed model and focuses on reducing model bias, through multi-model integration, feature engineering, and the development of a storm index that leverages upon distribution system data to aid the prediction of transmission risk. We explored three methods for integrating machine learning with mechanistic models. They consist of: (a) creating a linear combination of the outputs of the two modeling approaches, (b) including fragility curves as additional inputs to machine learning models and (c) developing a new machine learning model that uses the outputs of the weather-based machine learning model, fragility curve estimates and wind data to make new predictions. Moreover, due to the limited number of historical outages in transmission networks, a storm index was developed leveraging a dataset of distribution outages to learn about storm behavior to improve model skills. In the current version of the model, we substantially reduced the overestimation in the sum of predicted values of transmission line probability of failure that was present in the previously published model by 10 times. This has led to a reduction of model bias from 3352% to 14.46-15.43%. The model with the integrated approach and storm index demonstrates substantial improvements in the estimation the probability of failure of transmission line and their ranking by risk level. The improved model is able to capture 60% of the outages within the top 22.5% of the ranked power lines, compared to a value of 34.9% for the previous model. With an estimate of the probability of failure of transmission lines ahead of storms, power system planning and maintenance engineers will have critical information to make informed decisions to create better mitigation plans and minimize power disruptions. Long term, this model can assist with resilience investments as it highlights areas of the system more susceptible to damage.

Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL's performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL's effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability.

Traditional grid analytics heavily rely on accurate power system models, especially dynamic ones for generators, controllers, and loads. However, obtaining comprehensive models is impractical in real operations due to inaccessible parameters and consumer privacy. This necessitates dynamic equivalencing for unknown subsystems, which employs physics-informed machine learning and neural ordinary differential equations (ODE-NET) to preserve dynamic behaviors post-disturbances. The contributions include: (1) A neural dynamic equivalence (NeuDyE) formulation enabling continuous-time, data-driven dynamic equivalence, eliminating the need for acquiring inaccessible system details; (2) Introduction of Physics-Informed NeuDyE learning (PI-NeuDyE) to actively control NeuDyE’s closed-loop accuracy; (3) Driving Port NeuDyE (DP-NeuDyE), a practical application of NeuDyE, reducing the number of inputs required for training. Extensive case studies on the 140-bus NPCC system validate the generalizability and accuracy of both PI-NeuDyE and DP-NeuDyE. These analyses cover various scenarios, including limitations in data accessibility. Test results demonstrate the scalability and practicality of NeuDyE, showcasing its potential application in ISO and utility control centers for online transient stability analysis and planning purposes.

This letter devises Neural Dynamic Equivalence (NeuDyE), which explores physics-aware machine learning and neural-ordinary-differential-equations (ODE-Net) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The contributions are threefold: 1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; 2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; 3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models. Extensive case studies in the NPCC system validate the efficacy of NeuDyE, and, in particular, its capability under various contingencies.

Motivated by the Federal Energy Regulatory Commission’s (FERC) recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs. This paper summarizes the business drivers, challenges, guidance, and best practices for cloud adoption in power systems from the Task Force’s perspective, after extensive review and deliberation by its members, including grid operators, utility companies, software vendors, and cloud providers. The paper begins by enumerating various business drivers for cloud adoption in the power industry. It follows with the discussion of the challenges and risks of migrating power grid utility workloads to the cloud. Next, for each corresponding challenge or risk, the paper provides appropriate guidance. Notably, the guidance is directed toward power industry professionals who are considering cloud solutions and are yet hesitant about the practical execution. Finally, to tie all the sections together, the paper documents various real-world use cases of cloud technology in the power system domain, which both the power industry practitioners and software vendors can look toward to design and select their own future cloud solutions. We hope that the information in this paper will serve as helpful guidance for the development of NERC guidelines and standards relevant to cloud adoption in the industry.