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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Droughts in Wind and Solar Power: Assessing Climate Model Simulations for a Net‐Zero Energy Future

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.

Liu, Xue [Department of Oceanography Texas A&amp,M↗
DOE OSTI2025

More land is needed for solar and wind infrastructure under a high renewables scenario in the Western US by 2050

Expanding United States electricity infrastructure to meet growing demand could require extensive power plant development footprints and land use conversion, depending on the mix of generation types chosen. Understanding where future power plant sitings are likely to take place and identifying potential conflicts and land-use tradeoffs will be key to identifying feasible and affordable investments and evaluating regional planning coordination needs. Here we use an integrated modeling framework that combines capacity expansion planning, hourly grid operations, and geospatial techno-economic analysis to develop projections (2025-2050) of power plant sitings in the Western United States (US) at a 1 km 2 resolution for a business-as-usual scenario and a high renewables penetration scenario. We find that 30% more land will be needed in the high renewables scenario as compared to business-as-usual, and that 75% of that development is projected to be located within 10 km of natural areas.

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2022

Graph theory and nighttime imagery based microgrid design

Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. Finally, an application of the methodology is presented using real event, location, and asset data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

Abstract With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow With Continual Learning

Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Enhanced Inertial Support: Modeling Fast Frequency Response Controls for Energy Storage System Inverters

Reduction of power system inertia is a growing concern for grid operators given the transition under way from conventional fossil generators to inverter-based resources. One key concern is the rapid and severe dip in power system frequency following a large disturbance which could trigger remedial actions including operation of under frequency load shedding (UFLS) relays. A potential solution is to utilize battery energy storage systems (BESS) to provide fast frequency response(FFR) to mitigate the system frequency impact of low system inertia. In this work, we investigate different control parameters to achieve modeled FFR response from BESS in the simulated US Western Interconnection. First, a very low inertia model is developed from an existing positive sequence model of the Western Interconnection. Afterwards, we tune the BESS dynamic response parameters to eliminate any risks of reaching UFLS relay thresholds following the loss of large generators. Finally, we investigate the relative impact of having large BESS units in comparison of multiple smaller units in the same area. The results of the analyses indicate that 1 GW of transmission-connected BESS can help mitigate the frequency drop caused by large generators outages in the Western Interconnection, and significantly reduce the risk of under frequency load shedding.

power system dynamic performance, Low Inertia, Fas↗
DOE OSTI2023

Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids

Existing forecasting frameworks that predict time-series photovoltaic (PV) generation and consumer load for micro-grids' operation and control assume near-continuous availability of real-time predictors from the field. The incoming data are used to periodically re-train the models and update forecast snapshots over a moving horizon window. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. This paper bridges the shortcoming by leveraging a previously proposed forecasting framework that is resilient to abrupt changes in data quality caused by communication losses. Assuming no availability of real-time field system data, which is typical in extreme weather events such as hurricanes, the framework uses lightweight recursive time-series models to independently forecast solar irradiance, ambient temperature, PV power, and consumer load for three horizon windows: 24 hours, 12 hours, and 1 hour. Four types of ensemble-based regression trees-simple gradient boosted trees (GBR), GBR with an adaptive component (A-GBR), random forests (RF), and extra trees (ExTR)-are leveraged and their performances are compared against a simple historical weekly mean. Numerical results show that A-GBR performs better on average by 32% for 24-hour horizon and 39% for 12-hour horizon, whereas ExTR outdoes the other models on average by 10% for 1-hour horizon.

Sundararajan, Aditya↗
DOE OSTI2025

Optimal Control of Differentially Private EV Charging: A Scalable Learning Approach Under Uncertainty

Internet of Things (IoT)-enabled electric vehicles (IoEVs) enable intelligent charging coordination that accounts for grid congestion. However, increased data exchange raises privacy concerns, as charging patterns can reveal sensitive driver behavior to grid operators. Here, we propose a differentially private (DP) EV charging framework that enables coordinated control while protecting driver data with theoretical privacy guarantees. Nevertheless, integrating DP inevitably introduces uncertainty into the control strategy for EVs, which can lead to infeasible solutions. To tackle this challenge, we develop a feasible and scalable control algorithm based on constrained reinforcement learning (CRL) and convex hulls. While our framework is designed to handle the uncertainty introduced by DP, it is general and also applicable to other sources of uncertainty in EV charging, such as the stochastic nature of driver behavior and renewable variability. This ensures feasible and privacy-preserving coordination of EV charging at scale. Our method constructs convex hulls within the action space to guarantee feasibility under stochastic constraints and incorporates constraint reduction techniques to improve scalability. Case studies based on IEEE benchmark systems demonstrate that the proposed approach effectively balances feasibility under uncertainty, scalability, and privacy in large-scale EV charging control.

Engineering - Power transmission and distribution↗
DOE OSTI2025

Regional Inertia Estimation Using Actual Event Measurements: Florida Case

As inverter-based resources’ integration in power grids increases, their uneven distribution across the electrical network leads to the formation of local regions that are weakly coupled to the larger interconnection. This signifies the necessity of regional frequency dynamics investigation and analyzing various inertia metrics. This paper presents a practical methodology for estimating regional rate-of-change of frequency (RoCoF) using actual event recordings, which is then used to evaluate regional inertia. Florida (FL) is selected as the region of interest due to its distinct regional frequency dynamics and the rising levels of solar generation. We identify and analyze confirmed events from 2017 to 2024 that occurred in FL. The results indicate that FL contributes about 14% to the total inertia of the US Eastern Interconnection, which approximately matches its share of generation capacity. Results also highlight seasonal fluctuations in energy generation, which play a significant role in influencing inertia and thus the RoCoF levels. This emphasizes the importance of estimating regional inertia to enhance grid operations for a future that focuses on distributed generation.

Dulal, Saurav [University of Tennessee, Knoxville ↗
DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗
DOE OSTI2025

Electromagnetic Transient Simulation of Large-Scale Inverter-Based Resources With High-Granularity

The power grid is undergoing a significant transformation with the rapid increase in inverter-based resources (IBRs), including large-scale photovoltaic (PV) plants. Ensuring reliable and resilient grid operation in this new paradigm necessitates high-granularity electromagnetic transient (EMT) modeling that accurately captures the behavior of individual inverters and their interactions within IBR plants. Central to this approach is the detailed representation of both the IBR plant’s collector system and the dynamics of individual inverters. To achieve this, a high-granularity EMT model of a large-scale PV plant has been developed using advanced simulation algorithms, including matrix splitting and the Schur complement. These proposed techniques significantly enhance simulation speed, numerical stability, and accuracy while improving the modularity and efficiency of the collector system’s representation. The effectiveness of the proposed methods is validated through simulations of a representative large-scale PV plant consisting of 125 individual PV inverters, 25 IBR unit transformers, and a 52-bus collector system.

Choi, Jongchan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2021

An Application for Validation of Power Distribution System Models in an ADMS Environment

An accurate model of a power distribution system is the foundation for model-based applications that ensure efficient and reliable grid operation in an advanced distribution management system (ADMS) environment. However, these models are error-prone and comprehensive model validation is challenging due to lack of standards-based systems, data originating from disparate databases and other sources, and the constantly evolving nature of modern power distribution systems. In this paper, a novel framework for comprehensive model validation is described. The proposed application, the Model Validator, ensures that a model is both consistent and feasible by validating the derivative static and operational network model. A modular architecture for the application has been implemented and integrated with an open-source standards-based platform for ADMS application development, GridAPPS-D, allowing new validation capability to be added with minimal time and effort. The Model Validator application is demonstrated on the IEEE 13-bus, 123-bus, and 8500-node test cases over three validation scenarios.

Poudel, Shiva↗
DOE OSTI2021

Data-Driven Reliability Assessment for Marine Renewable Energy Enabled Island Power Systems

Marine renewable energy (MRE) resources are highly predictable and persistent sources of energy, when compared to other renewable sources like wind and solar. These lend them favorably for potential grid applications, particularly for coastal/island power systems where their generation potential is high. Island power systems, on the other hand, are either supported by onsite generation or by transported energy from the mainland grid. Therefore, robustness of grid operations depend heavily on the diversity of onsite generation resources and the reliability of the power transportation medium. Issues relating to either of these two factors may lead to impediments in smooth and reliable operation of the power system. Analyzing and quantifying operational risks for such island power systems with diverse non-conventional generation portfolios through conventional techniques can also prove to be cumbersome, often requiring multiple different inputs. Therefore, in this paper, we firstly present a novel, purely data-driven formulation which quantifies the operational reliability of such island power systems through minimal input data. Specifically, our proposed methodology only relies on historical knowledge of typical hourly load and generation profiles to quantify associated operational risks. Subsequently, we use our proposed formulation to evaluate the effectiveness of MRE resources (over other renewable resources like wind and solar) in providing resilience benefits to island power systems. The proposed formulation is demonstrated with a case study for an island power system in Nantucket, MA.

Chalishazar, Vishvas H.↗
DOE OSTI2024

Enhancing Solar Power Forecasting with Regularized Constrained Quantile Regression Averaging and Bootstrapping Techniques

Probabilistic solar power forecasting (SPF) plays an essential role in optimizing power-grid operations by quantifying the forecast uncertainty. To improve the accuracy and robustness of probabilistic SPF, this paper introduces the regularized constrained quantile regression averaging (rCQRA) method to combine outputs from multiple PSPF models. In addition, a bootstrapping method was used to quantify model uncertainty, providing insights into the reliability and significance of each ensemble component. To evaluate its efficacy, the proposed rCQRA method is used to integrate four PSPF methods. The resulting SPF models are trained and validated using a real-world six-year dataset from a rooftop solar plant in the USA. The performance of the proposed rCQRA method is evaluated and compared with two benchmark methods under three categories of weather conditions. It is shown that the rCQRA method has superior performance in its forecast reliability, sharpness, and accuracy.

Ensemble learning, probabilistic solar power forec↗
DOE OSTI2024

Optimizing Grid-interactive Efficient Building Designs with Stacked Value Streams

Grid-interactive efficient buildings (GEBs) are those characterized by the combination of energy efficiency and demand flexibility with smart technologies and communications to not only deliver greater affordability and comfort to buildings, but also help utilities manage grid operations and lower system costs. This paper presents an innovative techno-economic assessment framework to effectively examine different GEB design options, explore various use cases, define technically achievable benefits, and thereby assist in informed decision-making. In particular, building load flexibility, thermal storage, and battery energy storage are considered. Advanced optimal dispatch problem is formulated to maximize the stacked value streams from multiple, competing use cases, subject to the physical capabilities and operational flexibility associated with different designs and configurations. Comprehensive case studies were performed for a real-world building to evaluate the cost-effectiveness of different GEB designs and offer in-depth insights. It was found that the proposed assessment method could effectively capture the costs and benefits linked to each GEB design option. Furthermore, the study revealed that outage mitigation and demand response are the two most significant sources of benefits for GEBs.

Ma, Xu↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a two-agent soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak demand reduction. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operations to enhance control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment and reduce peak load demand from feeder's head.

deep reinforcement learning↗
DOE OSTI2023

Black Start with Inverter-Based Resources: Hardware Testing

Black start, or grid restoration after a wide-spread power outage, is a critical service on the power system that has historically been provided by transmission-connected synchronous generators. As the power system transitions to rely on more distributed and inverter-based resources it will be critical that these resources can also provide black start services. In this work we investigated battery energy storage and solar photovoltaics technical capabilities and limitations to provide black start services through hardware testing in an experimental microgrid testbed. This hardware demonstration of inverter-based resources providing black start functions can help inform grid operators on how to include these types of resources in their black start plans.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

OpenSAMPL: An Open Source Library for Timing and Synchronization Measurements and Analytics

Today's power grid operators are implementing timing and synchronization solutions that provide resilience to Global Navigation Satellite System (GNSS) vulnerabilities. These vendor-specific solutions often come with additional software applications that are designed to monitor that vendor's synchronization performance data. However, resilient timing architectures often resulting in multi-vendor solutions, including approaches that blend terrestrial clocks with space-based subscription services. In such an environment, collecting, analyzing, and visualizing data from a variety of sources within a single platform was heretofore not possible. To address this need, the US Department of Energy's Center for Alternative Synchronization and Timing (CAST) developed OpenSAMPL, the Open Synchronized Analytics and Monitoring Platform, an open-source Python framework for processing, loading, and observing clock measurement data from distributed devices. OpenSAMPL enables the ingestion of diverse clock-probe sources into a scalable time-series database and applies robust analytics. OpenSAMPL currently supports two vendor data pipelines, and will be extended to more in the near future, enabling seamless monitoring of a variety of timing and synchronization devices in a common environment.

Grant, Josh [ORNL] (ORCID:0000000163475060)↗