Le Xie

High-fidelity synthetic power-system time series are essential for data-driven and AI approaches, enabling robust benchmarking and scenario analysis under rare and evolving operating conditions. However, existing generative methods in this field are typically tailored to a single data type and rely on a fixed, pre-defined conditioning schema, limiting their ability to generalize across time-series domains. This paper presents GridLDM, a unified generative model for power-system time series that is both dataset-domain adaptive and scenario-condition adaptive using a single latent-diffusion backbone. GridLDM performs diffusion in a compact latent space and leverages cross-attention to incorporate natural-language prompts that specify scenario attributes spanning geography, seasonality, operating regimes, and event descriptions. Experiments across multiple domains show that GridLDM achieves high distributional fidelity and controllable generation across diverse datasets and scenarios, remains robust under novel prompt configurations, and can be efficiently fine-tuned when additional adaptation is required.

We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solvers. Existing datasets and libraries face three main challenges: (1) lack of realistic stochastic load and topology perturbations, limiting scenario diversity; (2) PF datasets are restricted to OPF-feasible points, hindering generalization of ML solvers to cases that violate operating limits (e.g., branch overloads or voltage violations); and (3) OPF datasets use fixed generator cost functions, limiting generalization across varying costs. gridfm-datakit addresses these challenges by: (1) combining global load scaling from real-world profiles with localized noise and supporting arbitrary N-k topology perturbations to create diverse yet realistic datasets; (2) generating PF samples beyond operating limits; and (3) producing OPF data with varying generator costs. It also scales efficiently to large grids (up to 10,000 buses). Comparisons with OPFData, OPF-Learn, PGLearn, and PF$Δ$ are provided. Available on GitHub at https://github.com/gridfm/gridfm-datakit under Apache 2.0 and via `pip install gridfm-datakit`.

System identification in control theory aims to approximate dynamical systems from trajectory data. While neural networks have demonstrated strong predictive accuracy, they often fail to preserve critical physical properties such as stability and typically assume stationary dynamics, limiting their applicability under distribution shifts. Existing approaches generally address either stability or adaptability in isolation, lacking a unified framework that ensures both. We propose LILAD (Learning In-Context Lyapunov-stable Adaptive Dynamics), a novel framework for system identification that jointly guarantees adaptability and stability. LILAD simultaneously learns a dynamics model and a Lyapunov function through in-context learning (ICL), explicitly accounting for parametric uncertainty. Trained across a diverse set of tasks, LILAD produces a stability-aware, adaptive dynamics model alongside an adaptive Lyapunov certificate. At test time, both components adapt to a new system instance using a short trajectory prompt, which enables fast generalization. To rigorously ensure stability, LILAD also computes a state-dependent attenuator that enforces a sufficient decrease condition on the Lyapunov function for any state in the new system instance. This mechanism extends stability guarantees even under out-of-distribution and out-of-task scenarios. We evaluate LILAD on benchmark autonomous systems and demonstrate that it outperforms adaptive, robust, and non-adaptive baselines in predictive accuracy.

Data centers host a variety of essential services such as cloud computing and artificial intelligence. Electric grid operators, however, have limited knowledge of the reliability risks of data center interconnection due to their unique operational characteristics. An emerging concern is the sub-synchronous resonance (SSR) which refer to unexpected voltage/current oscillations at typical frequencies below 60/50 Hz. It remains unknown whether and how the interactions between data centers and the grid may trigger resonances, equipment damages, and even cascading failures. In this paper, we focus on grid-connected data centers that draw electricity from the grid through power factor correction (PFC) converters. We conduct two-tone frequency sweep to investigate the data centers' impedance characteristics, i.e. magnitude and phase angle variations over frequencies, and showcase their deep dependence on compute workloads. The impedance modeling provides a direct approach to evaluating SSR risks and enable a cooperative mechanism to alarm and avoid resonance-prone situations. Building upon the impedance, a data-driven preventive controller is then established to raise early warnings of risky operation and suggest flexible workload management according to the given grid conditions. Through case study, we demonstrate how to use impedance to understand the unexpected interactions. Data-driven impedance is validated to show decent performance in capturing the unique impedance dips and tracking the impedance variations across a range of workload scenarios. The early warning and preventive control approaches are further effective to improve the safety margins with minimal workload rescheduling. The key findings of this work will provide valuable insights for grid operators and data center managers, and support preparation for future scenarios involving large-scale data center integration.

This paper introduces a data-driven framework and comprehensive repository for profit-robust planning of Energy Storage Systems (ESS) in electricity markets. Utilizing nearly a decade of granular nodal price data, which we have made accessible for public use, we develop a rigorous methodology — a scenario-based optimization approach combined with clustering techniques — to uncover regions where ESS investments can maximize returns, even in the face of price volatility. By analyzing both spatial and temporal real-time price patterns, we show that profit-robust clusters often correspond to areas with abundant renewable energy resources and high demand, such as key urban and industrial zones. While the case study is based in Texas, the data repository and computational tools developed are publicly accessible, enabling broader application and further research into energy storage planning across diverse regions.

The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key characteristics and patterns of electricity demand across the stages of model preparation, training, fine-tuning, and inference, (iii) analyzes the critical challenges that AI data center loads pose to power systems across three interrelated timescales, including long-term planning and interconnection, short-term operation and electricity markets, and real-time dynamics and stability, and (iv) discusses potential solutions from the perspectives of the grid, AI data centers, and AI end-users to address these challenges. By synthesizing current knowledge and outlining future directions, this review aims to guide research and development in support of the joint advancement of AI data centers and power systems toward reliable, efficient, and sustainable operation.

The operational resilience of electric power grids is facing growing challenges caused by aging infrastructure, increasing system complexity, and a rising frequency of extreme weather events. Traditional control paradigms, built around deterministic models and human-in-the-loop decision making, will become insufficient to manage the escalating demands on power grids. In response, recent advances in artificial intelligence (AI)—particularly the emergence of general-purpose AI agents capable of tool use, reasoning, and task orchestration—offer a new direction for enhancing grid flexibility and resiliency. This article introduces the concept of the Power Agent: an AI-enabled, context-aware assistant that leverages foundation models, standardized tool interfaces, and structured workflows to support grid operation and planning decisions. We discuss the conceptual architecture, implementation pathways, and system-level benefits of deploying Power Agents in power grid operations, with an emphasis on augmenting operator capabilities, improving situational awareness, and reducing operational bottlenecks.

<sec> <title>UNSTRUCTURED</title> ABSTRACT Background: Convalescent coronavirus disease 2019 (COVID-19) refers to a series of clinical syndromes in patients with COVID-19 infection that follow the relevant discharge indications but do not fulfill the criteria for a clinical cure, and these patients are discharged from the hospital with residual multifunctional deficits, including coughing, fatigue, and insomnia. Due to the prolonged convalescent COVID-19 infection, patients continue to experience symptoms or develop new symptoms after three months of infection, and some symptoms persist for over two months without any apparent triggers, which has a significant impact on the health status and quality of life of the population. Patients with convalescent COVID-19 lack a definitive pharmacological treatment. Traditional Chinese medicine (TCM) exhibits a distinct, synergistic effect on the treatment of convalescent COVID-19. However, there exists a limited number of clinical trials on TCM with lower evidence levels in convalescent COVID-19; therefore, randomized trials are urgently required. Methods: A multicenter, randomized, double-blind, placebo-controlled, phase II clinical trial was performed to evaluate the efficacy and safety of Shenlingkangfu (SLKF) granules in treating patients with convalescent COVID-19 and lung-spleen qi deficiency syndrome. Eligible participants were aged 18–75 years, had a confirmed or physician-suspected severe acute respiratory syndrome coronavirus 2 infection at least six months prior, and satisfied clinical criteria. Individuals with a history of severe pulmonary dysfunction or major liver and kidney illness or those on medications were excluded. Multicenter subjects satisfying all criteria were assigned (1:1) randomly into an intervention group and a control group. After a 2-day adjustment period, A total of 154 participants were randomly divided into an intervention group and a control group. The intervention group was given the SLKF granules orally once a bag, 16.9 g, twice daily, whereas the control group received the SLKF granule simulation at the same dosage. The trial was conducted over 14 days, with assessments performed at baseline and 14 days. Results: The primary outcomes were the therapeutic efficacy rate and total clinical symptom score. The secondary outcomes included the fatigue self-assessment scale, pain visual analog scale, Pittsburgh sleep quality index, mini-mental state examination, hospital anxiety and depression scale, TCM syndrome score, C-reactive protein, erythrocyte sedimentation rate, and interleukin-6. Three routine examinations, liver and kidney function tests, and electrocardiography were used as safety indicators. Conclusions:This study aimed to verify whether SLKF granules can significantly improve clinical symptoms, including fatigue, loss of appetite, cough, phlegm, and insomnia, in patients with convalescent COVID-19. For a comprehensive investigation, additional clinical trials with larger sample sizes and longer intervention periods are required.Clinical Trial Registration Center NCT1900024524, Registered on 26 January, 2024. </sec>

As Electrification and Renewable Energy adoption accelerate, the electric grid faces growing challenges in delivering clean and reliable power to meet surging demand from sectors such as transportation, industry, and artificial intelligence (AI)-driven computing. However, expanding grid infrastructure remains constrained by regulatory, environmental, and economic barriers. This article explores how AI, advanced conductor materials, energy-aware computing, and energy storage can effectively enhance transmission capacity without new construction. AI enables real-time grid optimization and stability management, while advanced materials like composite-core conductors reduce losses and increase throughput. Additionally, energy-flexible computing dynamically aligns computational workloads with grid conditions, alleviating peak demand. The future integration of energy storage as a transmission asset offers new pathways for congestion management. Together, these innovations form a scalable blueprint for an efficient and sustainable power system that supports sustainable electrification goals.

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

The operational resilience of electric power grids is facing growing challenges due to aging infrastructure, increasing system complexity, and a rising frequency of extreme weather events. Traditional control paradigms, built around deterministic models and human-in-the-loop decision-making, are becoming insufficient to manage the escalating demands on power grids. In response, recent advances in artificial intelligence-particularly the emergence of general-purpose AI agents capable of tool use, reasoning, and task orchestration-offer a new direction for enhancing grid flexibility and resiliency. This article introduces the concept of the Power Agent: an AI-enabled, context-aware assistant that leverages foundation models, standardized tool interfaces, and structured workflows to support grid operation and planning decisions. We discuss the conceptual architecture, implementation pathways, and system-level benefits of deploying Power Agents in power grid operations, with an emphasis on augmenting operator capabilities, improving situational awareness, and reducing operational bottlenecks.

The reliable power supply and economic analysis of ship charging and swapping station are crucial for promoting the electrification of the shipping industry and achieving the dual carbon goals. This paper focuses on the development of an economic analysis method for ship charging and swapping stations within smart grid application scenarios. Firstly, the cost model is established by considering the construction, operation, maintenance, and equipment replacement of ship charging and swapping stations. Secondly, an operational model is defined, outlining the constraints for charging and discharging processes as well as backup power capabilities. Thirdly, an economic analysis framework is developed to minimize total investment and operational costs, incorporating factors such as thermal power unit operation, wind power curtailment, and deep peak shaving of thermal units. Finally, the proposed models are validated through a case study using modified IEEE 9-bus and IEEE 30-bus systems, and the results demonstrate significant improvements in economic efficiency and system performance when incorporating ship charging and swapping station.

Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Conventional RL relies on trial-and-error interactions with the environment and reward feedback, which often leads to exploring unsafe operating regions and executing unsafe actions, especially when deployed in real-world power systems. To address these challenges, safe RL has been proposed to optimize operational objectives while ensuring safety constraints are met, keeping actions and states within safe regions throughout both training and deployment. Rather than relying solely on manually designed penalty terms for unsafe actions, as is common in conventional RL, safe RL methods reviewed here primarily leverage advanced and proactive mechanisms. These include techniques such as Lagrangian relaxation, safety layers, and theoretical guarantees like Lyapunov functions to rigorously enforce safety boundaries. This article provides a comprehensive review of safe RL methods and their applications across various power system operations and control domains, including security control, real-time operation, operational planning, and emerging areas. It summarizes existing safe RL techniques, evaluates their performance, analyzes suitable deployment scenarios, and examines algorithm benchmarks and application environments. This article also highlights real-world implementation cases and identifies critical challenges such as scalability in large-scale systems and robustness under uncertainty, providing potential solutions and outlining future directions to advance the reliable integration and deployment of safe RL in modern power systems.

Virtual Power Plants (VPPs) represent a critical advancement in modern energy management, aggregating distributed energy resources (DERs) to enhance grid reliability and operational efficiency. However, the increasing digitization and interconnectivity of VPPs expose them to significant cybersecurity threats. This article explores the concept of cyber resilience in VPPs, emphasizing the need for robust security frameworks that ensure continued functionality even in the face of cyberattacks. We propose cyber-resilient VPP architecture that incorporates multi-layered anomaly detection, real-time threat mitigation, and adaptive control mechanisms. The study outlines a hierarchical control structure within VPPs, where primary controllers manage local DER stability, secondary controllers optimize cluster-level energy coordination, and tertiary controllers handle system-wide forecasting and market participation. Each layer presents unique vulnerabilities to cyber threats, requiring specialized defensive strategies. Advanced techniques such as software-defined networking (SDN) and blockchain-based secure transactions are explored to fortify VPP communications and prevent unauthorized access. Additionally, human-in-the-loop considerations highlight the importance of operator training, cognitive analysis, and adaptive response strategies in mitigating cyber risks. A key innovation discussed is the implementation of dynamic watermarking techniques for real-time detection of cyber intrusions at the sensor layer, ensuring data integrity and operational stability. A hardware-in-the-loop (HIL) testbed is employed to simulate these cyberattacks, demonstrating the effectiveness of anomaly detection frameworks in safeguarding VPP infrastructure. The findings underscore the necessity of integrating cybersecurity with operational strategies to build resilient VPPs capable of sustaining energy delivery under adversarial conditions. By adopting a multi-scale, multi-layer approach to cybersecurity, VPPs can enhance their robustness against emerging cyber threats while maintaining grid reliability and efficiency.

Many regions such as Texas face escalating grid stress due to rapid population growth, industrialization, and extreme weather events. Virtual Power Plants (VPPs), aggregating distributed energy resources (DERs) such as smart thermostats, behind-the-meter photovoltaics (BTM-PV), and flexible loads, offer a scalable solution for enhancing grid flexibility and resilience. This study quantifies the peak demand reduction potential of demand-side resources in the Texas region using Monte Carlo simulations. Results indicate that widespread smart thermostat adoption could potentially reduce peak demand by up to 3.98 GW, while 10% BTM-PV penetration could potentially offset over 2 GW during peak hours. Key challenges include steep net-load ramp rates and distribution system constraints due to reverse power flow. Additionally, demand response (DR) effectiveness is highly sensitive to consumer participation, shaped by enrollment models, incentives, and communication strategies. These findings emphasize both technical and socio-economic prerequisites for effective VPP deployment and offer a possible framework for other regions targeting enhanced grid reliability and decarbonization.

The climate crisis necessitates decarbonization solutions that transform energy systems across all scales. While attention today focuses on utility-scale power systems, mini-or metro-scale grids, and at end-use device efficiency, the individual user scale remains underexplored. Just as with energy efficiency innovations tailored to micro-environments, body-scale energy savings offer new opportunities alongside technological and behavioral challenges. Here we propose a technique and a suite of potential innovations focused on the “body grid” in which devices, circuits, information network, human body and the environment interact within a universal framework to achieve energy savings, new functionality, and improved comfort. We present and test a prototype body grid supporting inter-device synergy and cooperation with external energy systems indoors and outdoors. This system yields substantial energy and economic savings, enhances personal control and comfort, and enables potential energy market participation. Simulation results demonstrate global energy savings of up to 50% for space cooling and heating. The climate crisis demands low-carbon solutions at the individual scale. Jiahe Xu, Xuan Zhang, Daniel M. Kammen and colleagues propose a body grid framework and mechanisms to enhance energy efficiency and personal comfort, with simulations suggesting up to 50% global savings in space cooling and heating.

A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.

In recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these firms in Texas, focusing on how their consumption is influenced by cryptocurrency conversion rates, electricity prices, local weather, and other factors. We transform the skewed electricity consumption data of these firms, perform correlation analysis, and apply a seasonal autoregressive moving average model for analysis. Our findings reveal that, surprisingly, short-term mining electricity consumption is not directly correlated with cryptocurrency conversion rates. Instead, the primary influencers are the temperature and electricity prices. These firms also respond to avoid transmission and distribution network (T&D) charges - commonly referred to as four Coincident peak (4CP) charges - during the summer months. As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid. The developed model could also lead to better pricing mechanisms to effectively use the flexibility of these resources towards improving power grid reliability.

This paper proposes a physics-informed graph neural network (GNN) framework for scalable and efficient AC power flow-based N-2 contingency screening in large-scale power systems. Formulated as a graph classification problem, the approach is specifically designed to identify critical N-2 contingencies that are likely to result in infeasible post-contingency AC power flow solutions. The integration of physics-based domain knowledge into the neural network architecture enhances the model’s capability to capture the underlying physical behaviors governing power flow, thereby improving classification accuracy. Comprehensive numerical experiments on the real-world Texas transmission network demonstrate that the proposed method achieves a 37-fold improvement in computational efficiency over conventional simulation-based N-2 contingency analysis techniques, underscoring its potential for operational deployment in real-time or near real-time security assessment.

This paper introduces a framework and computational algorithm that utilizes energy storage systems in pairs to improve transmission capacity in electric power systems. Recognizing prolonged development timelines and urgent needs for inter-regional transmission corridors, this paper proposes a near-term supplementary solution that schedules pairs of energy storage systems to increase the throughput of congested transmission lines effectively. We establish a theoretical lower bound on the minimum capacity required for electric power delivery, defined as a function of cumulative power over time. In sharp contrast with conventional transmission planning based on peak power delivery, this new framework allows transmission capacity to be designed around average power delivery needs. This shift would significantly enhance asset utilization in a future grid with large renewable power fluctuations. Numerical experiments demonstrate the proposed method across various grids. In the RTS-GMLC system, the minimum line capacity required was reduced by 36.8% compared to peak-based planning and further decreased by 43.5% when contingency scenarios were considered. In the Texas synthetic grid, the approach achieved a 46.2% reduction in line capacity while maintaining system reliability. These results highlight storage’s potential as a transmission asset, providing practical guidance for planning and policy while enabling insights into future market designs.

With the growing popularity of EVs in Texas, ERCOT faces the challenges of accommodating the increasing demand for electricity from these vehicles while maintaining system security. This paper aims to investigate the impact of electric vehicle (EV) charging on the stability and cybersecurity of the ERCOT grid. Utilizing ERCOT’s industry level PSSE model, this paper examines the thresholds of simultaneous EV charging that the system can sustain safely. Besides, it highlights cybersecurity vulnerabilities introduced by EV charging infrastructure.

Abstract Understanding and predicting “droughts” in wind and solar power availability can help the electric grid operator planning and operation toward deep renewable penetration. We assess climate models' ability to simulate these droughts at different horizontal resolutions, ∼100 and ∼25 km, over Western North America and Texas. We find that these power droughts are associated with the high/low pressure systems. The simulated wind and solar power variabilities and their corresponding droughts during historical periods are more sensitive to the model bias than to the model resolution. Future climate simulations reveal varied future change of these droughts across different regions. Although model resolution does not affect the simulation of historical droughts, it does impact the simulated future changes. This suggests that regional response to future warming can vary considerably in high‐ and low‐resolution models. These insights have important implications for adapting power system planning and operations to the changing climate.

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant; on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A* which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion.

The electricity sector is undergoing substantial transformations due to the rising electrification of demand, enhanced integration of renewable energy resources, and the emergence of new technologies. These changes are rendering the electric grid more volatile and unpredictable, making it difficult to maintain reliable operations. In order to address these issues, advanced time series prediction models are needed for closing the gap between the forecasted and actual grid outcomes. In this paper, we introduce a multivariate time series prediction model that combines traditional state space models with deep learning methods to simultaneously capture and predict the underlying dynamics of multiple time series. Additionally, we design a time series processing module that incorporates high-resolution external forecasts into sequence-to-sequence prediction models, achieving this with negligible increases in size and no loss of accuracy. We also release an extended dataset spanning five years of load, electricity price, ancillary service price, and renewable generation. To complement this dataset, we provide an open-access toolbox that includes our proposed model, the dataset itself, and several state-of-the-art prediction models, thereby creating a unified framework for benchmarking advanced machine learning approaches. Our findings indicate that the proposed model outperforms existing models across various prediction tasks, improving state-of-the-art prediction error by an average of 7% and decreasing model parameters by 43%.

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.

Foundational Large Language Models (LLMs) such as GPT-3.5-turbo allow users to refine the model based on newer information, known as "fine-tuning". This paper leverages this ability to analyze AC-DC converter behaviors, focusing on the ripple current in DC-link capacitors. Capacitors degrade faster under high ripple currents, complicating life monitoring and necessitating preemptive replacements. Using minimal invasive noisy hardware measurements from a full bridge rectifier and 90W Power Factor Correction (PFC) boost converter, an LLM-based models to predict ripple content in DC-link currents was developed which demonstrated the LLMs’ ability for near-accurate predictions. This study also highlights data requirements for precise nonlinear power electronic circuit parameter predictions to predict component degradation without any additional sensors. Furthermore, the proposed framework could be extended to any non-linear function mapping problem as well as estimating the capacitor Equivalent Series Resistance (ESR).

In this paper, we introduce a systematic design of an end-to-end cyber-secure monitoring and safe operation framework for solar photovoltaic (PV)-rich microgrids, by focusing on the use of on-line attack detector for defending against potential cyber-physical attacks and sensor measurement data processing for operational safety. The proposed approach is based on: 1) identifying component models using their input and output measurements; 2) dynamic watermarking (DW) methodology for on-line cyber-attack detection and its timing; and 3) an adaptive event-driven safe shutdown procedure after attack detection. It is shown how these three components interactive steps jointly to ensure safe operation under both normal operational scenarios and compromised operational scenarios with some malicious sensors caused by cyber-attacks on solar PV microgrids. Through this on-line integration, the proposed framework dynamically adapts to varying solar irradiance, load demands, operating conditions, and potential cyber-physical attacks. This forms a cyber-physical system which can pro-actively detect and mitigate potential cyber-physical attacks on solar PV microgrids. We describe its proof-of-concept implementation using real-world industrial solar farm data provided by the CenterPoint Energy. This study contributes to enhancing the cyber-security and safe operation of solar PVrich power systems, offering a scalable solution to other types of emerging renewable-rich energy distribution systems.

This paper assesses the impact of incorporating climate data into long-term power system planning, using simulated wind speed data from Texas as a case study. Two experiments are conducted. First, we evaluate the quality of wind speed time series data obtained from climate model simulations and how it varies with spatial resolution. Our analysis suggests that both high- and low-resolution simulated climate data generally align with the probability distribution of historical data, but high-resolution climate data is able to capture extreme events more accurately. Second, we employ simulated climate data for time-series prediction of daily, weekly, and monthly wind power production. The findings caution against the hasty adoption of climate data for time-dependent prediction, as observations indicate minimal impact on shorter prediction intervals like daily and weekly averaged power generation. The results suggest that the integration of climate data may not provide substantial improvements in forecast accuracy for shorter intervals, under-scoring the need for careful consideration and further research when incorporating climate data into forecasting models. Code availability github.com/fatemehdoudi/Climate4Grid

This paper studies some compression methods to accelerate the scenario-based chance-constrained security-constrained economic dispatch (SCED) problem. In particular, we show that by exclusively employing the vertices after convex hull compression, an equivalent solution can be obtained compared to utilizing the entire scenario set. For other compression methods that might relax the original solution, such as box compression, this paper presents the compression risk validation scheme to assess the risk arising from the sample space. By quantifying the risk associated with compression, decision-makers are empowered to select either solution risk or compression risk as the risk metric, depending on the complexity of specific problems. Numerical examples based on the 118-bus system and synthetic Texas grids compare these two risk metrics. The results also demonstrate the efficiency of compression methods in both problem formulation and solving processes.

Addressing the urgency of climate change necessitates a coordinated and inclusive effort from all relevant stakeholders. Critical to this effort is the modelling, analysis, control and integration of technological innovations within the electric energy system, which plays a major role in scaling up climate change solutions. This Perspective presents a set of research challenges and opportunities in the area of electric power systems that would be crucial in accelerating gigaton-level decarbonization. Furthermore, it highlights institutional challenges associated with developing market mechanisms and regulatory architectures, ensuring that incentives are aligned for stakeholders to effectively implement the technological solutions on a large scale. The decarbonization of energy systems needs to be integrated with electric grid infrastructure, yet combined climate–grid studies are lacking. This Perspective discusses electric grid research that should be prioritized, and how researchers from different communities could better collaborate.

In recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these firms in Texas, focusing on how their consumption is influenced by cryptocurrency conversion rates, electricity prices, local weather, and other factors. We transform the skewed electricity consumption data of these firms, perform correlation analysis, and apply a seasonal autoregressive moving average model for analysis. Our findings reveal that, surprisingly, short-term mining electricity consumption is not directly correlated with cryptocurrency conversion rates. Instead, the primary influencers are the temperature and electricity prices. These firms also respond to avoid transmission and distribution network (T&amp;D) charges - commonly referred to as four Coincident peak (4CP) charges - during the summer months. As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid. The developed model could also lead to better pricing mechanisms to effectively use the flexibility of these resources towards improving power grid reliability.

This paper studies some compression methods to accelerate the scenario-based chance-constrained security-constrained economic dispatch (SCED) problem. In particular, we show that by exclusively employing the vertices after convex hull compression, an equivalent solution can be obtained compared to utilizing the entire scenario set. For other compression methods that might relax the original solution, such as box compression, this paper presents the compression risk validation scheme to assess the risk arising from the sample space. By quantifying the risk associated with compression, decision-makers are empowered to select either solution risk or compression risk as the risk metric, depending on the complexity of specific problems. Numerical examples based on the 118-bus system and synthetic Texas grids compare these two risk metrics. The results also demonstrate the efficiency of compression methods in both problem formulation and solving processes.

The electricity sector is facing the dual challenge of supporting increasing level of demand electrification while substantially reducing its carbon footprint. Among electricity demands, the energy consumption of cryptocurrency mining data centers has witnessed significant growth worldwide. If well-coordinated, these data centers could be tailor-designed to aggressively absorb the increasing uncertainties of energy supply and, in turn, provide valuable grid- level services in the electricity market. In this paper, we study the impact of integrating new cryptocurrency mining loads into Texas power grid and the potential profit of utilizing demand flexibility from cryptocurrency mining facilities in the electricity market. We investigate different demand response programs available for data centers and quantify the annual profit of cryptocurrency mining units participating in these programs. We perform our simulations using a synthetic 2000 bus ERCOT grid model, along with added cryptocurrency mining loads on top of the real-world demand profiles in Texas. Our preliminary results show that depending on the size and location of these new loads, we observe different impacts on the ERCOT electricity market, where they could increase the electricity prices and incur more fluctuations in a highly non-uniform manner.

In this paper, we developed an Electromagnetic Transient (EMT) model tailored for large cryptocurrency mining loads to understand the cross-interaction of these loads with the electric grid. The load model has been built using Electromagnetic Transients Program (EMTP) software. We have cross-validated the tripping characteristics of the EMT model of this load with commercial application-specific integrated circuit miners, typically used by large-scale mining facilities, by comparing the low-voltage ride-through (LVRT) capabilities. Subsequently, LVRT capabilities of the large-scale miners have been tested against various fault scenarios both within the miner’s remote facility as well as at one of the distant buses of the interconnected grid. The significance of this model lies in its scalability to accommodate larger blocks of mining loads and its seamless integration into a larger electric grid.

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.

This study addresses the transmission value of energy storage in electric grids. The inherent connection between storage and transmission infrastructure is captured from a "cumulative energy" perspective, which enables the reformulating of the conventional optimization problem by employing line power flow as the decision variable. The study also establishes the theoretical limitations of both storage and transmission lines that can be replaced by each other, providing explicit closed-form expressions for the minimum capacity needed. As a key departure from conventional practice in which transmission lines are designed according to the peak power delivery needs, with sufficient storage capacity, the transmission line capacity can be designed based on the average power delivery needs. The models of this paper only rely on a few basic assumptions, paving the way for understanding future storage as a transmission asset market design. Numerical experiments based on 2-bus, modified RTS 24-bus, RTS-GMLC, and Texas synthetic power systems illustrate the results.

We study the scenario approach for solving chance-constrained optimization in time-coupled dynamic environments. Scenario generation methods approximate the true feasible region from scenarios generated independently and identically from the actual distribution. In this paper, we consider this problem in a dynamic environment, where the scenarios are assumed to be drawn in a sequential fashion from an unknown and time-varying distribution. Such dynamic environments are driven by changing environmental conditions that could be found in many real-world applications such as energy systems. We couple the time-varying distributions using the Wasserstein metric between the sequence of scenario-generating distributions and the actual chance-constrained distribution. Our main results are bounds on the number of samples essential for ensuring the ex-post risk in chance-constrained optimization problems when the underlying feasible set is convex or non-convex. Finally, our results are illustrated on multiple numerical experiments for both types of feasible sets.