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At least 37 records · Page 2

Implementation and Demonstration of the Digital Twin Certification System Remote Operations Framework

Microreactors are one promising advanced-reactor concept being pursued by the nuclear industry. They are distinguished by a relatively low power output of 20 MWth or less. These microreactors are intended for deployment in applications where conventional small-capacity power solutions, such as diesel generators, are either economically unfeasible or logistically challenging. Such applications include providing electric power and/or heat for remote communities, mining sites, defense installations, and humanitarian and disaster-relief missions. An important feature for the successful deployment of microreactors is their capability to be operated remotely. This capability can significantly reduce staffing costs by eliminating the need for licensed operators to be physically present at each reactor site. Instead, operators can be centralized in a single remote operations center placed in an economically advantageous location, thereby optimizing resources by consolidating expertise and enhancing operational efficiency. However, the implementation of a remote operation system for nuclear reactors raises new concerns regarding the security, reliability, and resilience of such a system. One way in which remote operations can be supported in a manner that maintains system security, reliability, and resilience is through the use of digital twins in a novel framework designed to verify and validate sensor data and commands communicated between the remote operations center and reactor. This framework, known as the Digital Twin Certification System (DTCS), has previously been proposed as an operations architecture that can bring security and resiliency levels of remote nuclear-reactor operations to a level acceptable for commercial deployment. This paper moves the proposed DTCS architecture from concept to reality by presenting the implementation and testing of the system. The rationale and implementation of the DTCS using tools such as DeepLynx and Apache Airflow, is covered in-depth. This is followed by a demonstration of the DTCS by applying the implemented system architecture to the Single Primary Heat Extraction and Removal Emulator, a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The demonstration includes both normal and abnormal operating scenarios to highlight how the DTCS can increase the security, reliability, and resilience of a remote operations system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS

LAF-Net: A Deep Residual and Cross-Attention Framework for Day-Ahead Load Forecasting: Preprint

Accurate day-ahead load forecasting is essential for reliable power system operations and market efficiency. System operators such as the Midcontinent Independent System Operator (MISO) rely on forecasts from multiple vendors, yet combining them effectively remains a persistent challenge due to vendor-specific biases. This paper presents a novel LSTM-Attention Fusion Network with Error Representation (LAF-Net) that enhances day-ahead hourly load forecasting through deep residual learning and multi-modal cross-attention. The proposed model builds a historical error memory from past vendor performance and dynamically queries it with future hour context to generate adaptive, hour-specific trust weights for each vendor. A bounded residual correction further refines forecasts by mitigating systematic and temporally localized errors. Tested on real MISO LBA data with multi-vendor forecasts, LAF-Net consistently outperforms the best vendor baseline across all 38 LBAs, achieving more than a 40% reduction in system-level mean absolute error (MAE) during peak load hours relative to the best vendor baseline.

24 POWER TRANSMISSION AND DISTRIBUTION

Evaluating the Impact of Managed EV Charging for Reliable Operation of Bulk Power Systems with High Non-Dispatchable Generation

The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.

14 - SOLAR ENERGY

Dynamic Line Rating Models and Their Potential for a Cost‐Effective Transition to Carbon‐Neutral Power Systems

Most transmission system operators (TSOs) currently use seasonally steady-state models considering limiting weather conditions that serve as reference to compute the transmission capacity of overhead power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions, and the degradation of lines in a cost-effective way. DLR can also be used in the long run in grid extension and new power capacity planning. In the short run, it should be used to help operate power systems with congested lines. The operation of the power systems is planned to have the market trading into account; thus, it computes transactions hours ahead of real-time operation, using power flow forecasts affected by large errors. In the near future, within a “smart grid” environment, in real-time operation conditions, TSOs should be able to rapidly compute the capacity rating of overhead lines using DLR models and the most reliable weather information, forecasts, and line measurements, avoiding the current steady-state approach that, in many circumstances, assumes ampacities above the thermal limits of the lines. Here, this work presents a review of the line rating methodologies in several European countries and the United States. Furthermore, it presents the results of pilot projects and studies considering the application of DLR in overhead power lines, obtaining significant reductions in the congestion of internal networks and cross-border transmission lines.

29 ENERGY PLANNING, POLICY, AND ECONOMY

Preventive Power Outage Estimation Based on a Novel Scenario Clustering Strategy

The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D

Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-The-Meter Solar

The ever-growing integration of distributed energy resources (DERs), especially behind-the-meter (BTM) solar generations, poses imperative operational challenges to system operators such as regional transmission organizations (RTOs). It is important for RTOs to effectively and accurately extract actual load profiles at the transmission level for a single node with significant BTM solar injection. This paper first illustrates the necessity of disaggregating the daily actual load profile of a single node. Furthermore, by segmenting nodes with selected timeseries features, nodes with significant BTM solar generation are identified. Lastly, a bi-level framework is proposed, comprising reference node disaggregation and DeepFM nodal disaggregation, aimed at disaggregating the nodal load profiles from which system operators require more information. By adopting a hybrid Deep Factorization Machine (DeepFM) model, the model achieve accurate results by extracting both linear and nonlinear relations between nodes in the same region and the zonal load and nodal load profile. To overcome the lack of ground truth, this paper segments the load profile into daytime, nighttime, and zero-crossing points and utilizes the latter two for evaluation purposes. The proposed disaggregation procedure is validated using real world, minute-level, normalized, and anonymized nodal data in the PJM service territory.

42 ENGINEERING

Generator Interconnection Costs to the Transmission System in non-ISO Balancing Authorities [Slides]

Electric transmission system operators—including Independent System Operators (ISOs), Regional Transmission Organizations (RTOs), and utilities—require proposed power plants to undergo a series of interconnection studies before connecting to the grid. These studies assess what transmission upgrades or new infrastructure may be necessary and assign the associated costs to the project. Lawrence Berkeley National Laboratory has compiled, aggregated, and cleaned interconnection cost data, originally for ISOs/RTOs, and now for five non-ISO Balancing Authorities: PacifiCorp, Bonneville Power Authority, Duke Energy Progress, Duke Energy Carolinas and Duke Energy Florida. Insufficient transparency in interconnection cost data may contribute to rapidly expanding interconnection queues, with active queue capacities tripling between 2020 and 2024 in the studied BAs. Most projects withdraw after receiving high interconnection cost estimates. Interconnection costs have increased since the early 2000s, with average costs for "complete" projects reaching $194/kW between 2018 and 2024. Active queue projects and withdrawn projects incur substantially higher costs, primarily due to rising network upgrade costs. Recent interconnection costs in non-ISO balancing authorities are higher than in ISO regions, potentially due to a greater willingness to pay among developers. Utility-scale solar, wind, and storage projects have interconnection costs that exceed those for natural gas. However, when focusing on projects that do not withdraw from the queue, the interconnection costs for these technologies are more similar to natural gas projects. Other key findings include: (1) Larger generation projects benefit from lower proportional interconnection costs, (2) capacity transmission service (NRIS) often requires additional network investments, and (3) projects with high network upgrade costs are often clustered geographically. The dataset includes results from 2,104 interconnection studies conducted between 2000 and 2024, covering projects that are operational, withdrawn, or still progressing through the study process. The Excel file contains (a) the complete project-level interconnection cost dataset, and (b) seven additional tabs summarizing cost metrics across dimensions such as time, market structure, cost category (point of interconnection vs. broader network upgrades), fuel type, service type (ERIS vs. NRIS), generator size, and geography.

24 POWER TRANSMISSION AND DISTRIBUTION

IT vs. OT: Trends and Incidents

This presentation discusses the differences between information systems (IT) and operational systems (OT) and why that difference is important in the context of cybersecurity for the energy sector.

29 - ENERGY PLANNING, POLICY AND ECONOMY

Review of Technical Requirements for Inverter-Based Resources in Chile

This document compares the technical requirements in the grid code of Chile (NTSyCS) against the EirGrid (Ireland transmission system operator) and National Grid Electricity System Operator (NESO) grid codes and the Institute of Electrical and Electronics Engineers (IEEE) 2800-2022 standard for conventional inverter technology at the transmission and subtransmission levels in the process of being adopted by system operators in the U.S. The document is intended to be a guide and reference for future updates of the NTSyCS, considering the local system requirements and present improvements in inverter-based resource (IBR) technology. In light of the findings of the aforementioned comparative review, this document proposes and describes the requirements for conventional IBRs that could be incorporated and updated into the Chilean grid code. The document is structured around the main grid challenges posed by the transition to an IBR-dominated grid and conventional IBRs capabilities to help address them. This document does not provide guidance on how this specification should be implemented (e.g., through a mandate or market solution).

24 POWER TRANSMISSION AND DISTRIBUTION

The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning

Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines. This study provides the first assessment of ML performance against actual operational forecasting systems operated by the California Nevada River Forecast Center (CNRFC), which combines the Community Hydrologic Prediction System (CHPS) with forecasters-in-the-loop. Results demonstrate that forecasters-in-the-loop systems consistently outperform ML models in both general forecasts and flood alerting across lead times up to 96 hr, even when ML models use observed forcings, while CNRFC operational process relies on biased weather forecasts. Our analysis reveals that forecaster expertise maintains forecast reliability despite inaccurate precipitation inputs, with human-guided systems showing superior performance degradation characteristics at extended lead times. These findings highlight the irreplaceable value of human expertise in operational forecasting and caution against overstating current ML capabilities in real-world applications.

Tran, Vinh Ngoc [Univ. of Michigan, Ann Arbor, MI

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. Finally, these findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.

24 POWER TRANSMISSION AND DISTRIBUTION

Planning and Operations in Electricity Markets Under System Tansformation: Key Findings

This report summarizes a set of key findings that have been developed through a set of interconnected research activities performed by five institutions between January 2020 and December 2023. The project team, comprising Argonne National Laboratory, the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, the Electric Power Research Institute, and Johns Hopkins University, collective engaged with the North American Independent System Operators and Regional Transmission Operators (ISO/RTOs) to identify the key challenges they are facing and opportunities for the project team to provide technical assistance in several prioritized challenge areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY

A Review on Protection Challenges on Transmission Lines Connected to Inverter Based Resources

The increased penetration of inverter-based resources (IBRs) connected to transmission grids is challenging how traditional electric power systems operate. Such systems were originally designed based on the dominant presence of synchronous generators. Some main challenges include the increased concerns about system stability due to the reduced system inertia caused by IBRs and the reduced protection reliability caused by the non-deterministic fault current response of IBRs. The protection relays’ algorithms have been designed and validated based on well-known fault current characteristics of synchronous generations, while the short-circuit responses of IBRs heavily depend on their vendor-specific control schemes, which are mainly set to protect inverter semiconductor switches and DC components. The magnitude of short current that IBRs contribute is typically between 1.1–1.5 times the rated current that may behave unstably and incoherently with the voltages. The control scheme of the IBRs also controls the active (P) and reactive (Q) support during system voltage sag and swell conditions. These characteristics challenge not only protective relay schemes but also existing short-circuit software tools to correctly model IBRs for fault studies and protection coordination analyses. Utilities, university researchers, research institutes, and relay manufacturers have performed studies to investigate different aspects of high IBR penetration. Almost all studies support 1) creating international and national standards to regulate the IBRs, 2) revising engineering tools to address shortcomings, and 3) adjusting the protective relay settings to resolve protection problems. This paper summarizes the studies and research conducted in recent years to quantify the impact of IBR penetration on transmission line protection and to identify potential solutions to improve the dependability and reliability of grid protection systems.

14 SOLAR ENERGY

A review on protection challenges of Transmission Lines connected to Inverter Based Resources

The increased penetration of inverter-based resources (IBRs) connected to transmission grids is challenging how traditional electric power systems operate. Such systems were originally designed based on the dominant presence of synchronous generators. Some main challenges include the increased concerns about system stability due to the reduced system inertia caused by IBRs and the reduced protection reliability caused by the non-deterministic fault current response of IBRs. The protection relays’ algorithms have been designed and validated based on well-known fault current characteristics of synchronous generations, while the short-circuit responses of IBRs heavily depend on their vendor-specific control schemes, which are mainly set to protect inverter semiconductor switches and DC components. The magnitude of short current that IBRs contribute is typically between 1.1–1.5 times the rated current that may behave unstably and incoherently with the voltages. The control scheme of the IBRs also controls the active (P) and reactive (Q) support during system voltage sag and swell conditions. These characteristics challenge not only protective relay schemes but also existing short-circuit software tools to correctly model IBRs for fault studies and protection coordination analyses.

14 SOLAR ENERGY

A Review on Protection Challenges of Transmission Lines Connected to Inverter-Based Resources

The increased penetration of inverter-based resources (IBRs) connected to transmission grids is challenging how traditional electric power systems operate. Such systems were originally designed based on the dominant presence of synchronous generators. Some main challenges include the increased concerns about system stability due to the reduced system inertia caused by IBRs and the reduced protection reliability caused by the non-deterministic fault current response of IBRs. The protection relays’ algorithms have been designed and validated based on well-known fault current characteristics of synchronous generations, while the short-circuit responses of IBRs heavily depend on their vendor-specific control schemes, which are mainly set to protect inverter semiconductor switches and DC components. The magnitude of short current that IBRs contribute is typically between 1.1–1.5 times the rated current that may behave unstably and incoherently with the voltages. The control scheme of the IBRs also controls the active (P) and reactive (Q) support during system voltage sag and swell conditions. These characteristics challenge not only protective relay schemes but also existing short-circuit software tools to correctly model IBRs for fault studies and protection coordination analyses

14 SOLAR ENERGY

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML),

Application of Banking Scoring and Rating for Coherent Risk Measures in Electricity Systems ABSCORES

This project developed a framework for asset and system risk management that can be incorporated into current electricity system operations to improve economic efficiency and establish an Electric Assets Risk Bureau. We leveraged scoring and ratings from banking and financial institutions alongside current optimization methods in dispatching power systems to help system operators and electricity markets schedule resources. This approach is based on the observation that there are major discrepancies between the power scheduled by a system operator and the actual power generated/consumed. These discrepancies—exacerbated by unplanned contingencies (e.g., natural disasters)—are caused by multiple factors, including the different financial, environmental and risk preferences of power producers, consumers, and aggregators. We developed a framework that counteracts two failures in electricity system operations: imperfect information and missing markets for products. The technical approach included five tasks. Tasks 1 and 2 supported the development of risk scores at the asset level with historical data collected for this project. Tasks 3, 4, and 5 incorporated scoring into decision-making at the system level. The proposed effort achieved PERFORM's Program Objectives because the proposed outputs and algorithms do not exist in the electricity industry and are an innovative approach to managing risk. Since the acknowledged need to better assess and act upon risk profiles for grid assets has not been met by the industry, this project will also impact ARPA-E's Mission Areas, including improving energy efficiency and giving the U.S. a technological lead in advanced energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY

Intercomparison of Three Continuous Monitoring Systems on Operating Oil and Gas Sites

We compare continuous monitoring systems (CMS) from three different vendors on six operating oil and gas sites in the Appalachian Basin using several months of data. We highlight similarities and differences between the three CMS solutions when deployed in the field and compare their output to concurrent top-down aerial measurements and to site-level bottom-up inventories. Furthermore, we compare vendor-provided emission rate estimates to estimates from an open-source quantification algorithm applied to the raw CMS concentration data. This experimental setup allows us to separate the effect of the sensor platform (i.e., sensor type and arrangement) from the quantification algorithm. We find that 1) localization and quantification estimates rarely agree between the three CMS solutions on short time scales (i.e., 30 min), but temporally aggregated emission rate distributions are similar between solutions, 2) differences in emission rate distributions are generally driven by the quantification algorithm, rather than the sensor platform, 3) agreement between CMS and aerial rate estimates varies by CMS solution but is close to parity when CMS estimates are averaged across solutions, and 4) similar sites with similar bottom-up inventories do not necessarily have similar emission characteristics. These results have important implications for developing measurement-informed inventories and for incorporating CMS-inferred emission characteristics into emission mitigation efforts.

54 ENVIRONMENTAL SCIENCES