Hybrid Model-based Approach for Remote Diagnostics and Prognostics of Wind Turbines
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17 WIND ENERGY↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint
Frequency control from Photovoltaic (PV) plants has great potential to address the frequency response challenge of the power system with high renewable penetration. However, using model-based approaches to determine the optimal PV headroom reserve requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic one-day operation profile of the WECC system and demonstrates over 40% PV headroom saving compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.
41 EE - Solar Energy Technologies Office (EE-4S)↗
The Technical and Economic Potential of Hydrogen within the United States
Hydrogen markets have an opportunity to grow as its value as an energy intermediate increases. Hydrogen’s estimated serviceable consumption potential in the United States is 106 MMT/yr which is a ten-fold increase over the current market size. That opportunity is driven primarily by increased demands for metals refining, biofuel and hydrocarbon production, supplementing the natural gas system, seasonal storage for the electricity grid, and fuel cell electric vehicles. The U.S. has many renewable, nuclear, and fossil resources that each could meet that demand itself. The economic potential, which considers both costs to produce hydrogen and willingness of applications to pay for it, is 22 to 41 MMT/yr. The range is impacted by factors that impact the cost to produce including the price of natural gas and the cost of electrolyzers. It is also impacted by competition to provide the same services as hydrogen including electrification.
electrolysis↗
What's New in the Battery Model for the System Advisor Model
This webinar was presented on September 1, 2021 and provides an overview of new battery features to be including in the next SAM release.
battery↗
DOE/INL—JAEA Collaboration: HTTR Secondary System Modeling
Summary of INL-JAEA collaborative work on high temperature gas-cooled reactors utilized for hydrogen production.
08 HYDROGEN↗
Data and Multistage Optimization for the New Grid
Three essential capabilities for using exascale computing resources on next generation power grid applications are: generating large renewable energy datasets, modeling damage and operations during and after extreme events, and employing multi-stage optimization for infrastructure planning. Powerscenarios, developed as part of the ExaSGD project, addresses these capabilities by enabling users to build synthetic wind farms for large test systems using numerical weather prediction-based data sources. We share examples of using Powerscenarios to build out wind farms on the ACTIVSg2000 test system, to enable simulated operations and emergency asset allocation during a hurricane strike, and to compute multi-stage infrastructure buildouts.
economic dispatch↗
A Thermally-Driven Transcritical CO2 Heat Pump for Upgrading LWR-Grade Heat to High Temperatures
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22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Evolution of Scenario Generation Capabilities in the ExaSGD Project
High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.
economic dispatch↗
GFM Inverter Interoperability Through Hardware Testing
This presentation highlights the individual unit testing results of the UNIFI 1MW multi-vendor GFM inverter testing.
black start↗
Bidding Curve Design for Hybrid Power Plants with Uncertain Solar Forecast: Preprint
This paper presents a novel bidding curve design algorithm tailored for hybrid power plants (HPPs) to participate in the wholesale electricity market. Utilizing forecasts for photovoltaic (PV) generation and available battery power, our algorithm strategically computes the bidding curve to maximize HPP profit while adeptly managing the inherent uncertainty associated with PV power generation. In addition, the introduction of the penalty cost in HPP bidding curves provides the system operator a tool to effectively manage the system-level uncertainty that caused by HPPs. Numerical analysis through Monte Carlo simulations confirms that our bidding curve methodology outperforms the benchmark across various scenarios.
bidding curve↗