SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This thematic map series uses Oak Ridge National Laboratory’s 2016 Existing Hydropower Assets Plant Dataset to map currently operational FERC-licensed hydropower plants that are anticipated to be up for relicensing in the near-term.
A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.
Hydropower and pumped storage hydropower (PSH) have a complex and uncertain future in the U.S. electricity system. On one hand, existing assets have the potential for an expanded role in integrating variable renewable energy while new deployment, particularly of PSH, can help meet growing needs for grid flexibility and improved reliability. On the other hand, challenging environmental and cost considerations could limit the extent of new investments in hydropower and PSH capacity and flexibility. This technical presentation demonstrates the use of a high-fidelity capacity expansion model of the U.S. electric grid (the National Renewable Energy Laboratory's Regional Energy Deployment System) to understand the impacts of alternative futures for the hydropower and PSH fleet, including scenarios of both growth and decline. Growth scenarios include new PSH deployment or improved hydropower flexibility, while scenarios of decline reduce the capacity or energy production potential of hydropower and PSH. Economic, environmental, and performance outcomes of the grid are compared across these scenarios to reveal the potential contributions of hydropower and PSH in the U.S. electric sector over the next several decades, focusing on changes to the grid technology mix, electric sector costs, electricity prices, and air emissions. This broad scenario approach demonstrates how flexible hydropower and PSH can help reduce air emissions and cost by complementing variable renewables, but the opposite can occur with reduced hydropower availability, particularly in the next decade. It is important to consider a wide range of scenarios and metrics to gain a national and regional understanding of hydropower's future in the United States.
Because of unique characteristics such as clean and cost-competitive electricity as well as fast-ramping and storage abilities, the power industry continues to evolve its operation strategies for cascading hydroelectric (CHE) systems for providing enhanced values to the grid, especially under the deeper renewable resource integration. However, existing operation practices of CHEs predate the integration of renewables, which could prohibit the effective utilization of their inherent flexibilities in delivering maximum financial benefits and providing valuable grid services to the power system and electricity market operations. Indeed, modeling and optimizing these resource-limited while flexible CHE assets with uncertainties and imperfect information across multiple spatial-temporal dimensions present significant challenges. To facilitate CHE facility operators in effectively coordinating water usage and hydropower plant operations across multiple timescales, this project aims to fill the existing gaps by developing a suite of accurate water inflow (WI) forecast models as well as enhanced CHE modeling and optimization approaches with proper consideration of their unique characteristics, which would help explore their multidimensional spatial-temporal operational flexibility potentials. The developed approaches could better align reservoir operation strategies with variability and uncertainty of future water availability. They can also promote more effective utilization of multidimensional spatial-temporal hydropower operational flexibility potentials by designing long-term evacuation plans of reservoirs and short-term operation of CHEs, along with their coordination with other types of renewables. The project leverages various resources to facilitate the research and development activities, including actual characteristics data of CHE systems and a library of current and future cases of Portland General Electric (PGE). These realistic data enable the project team to study how to maximize the value of CHEs under current and future portfolios and evaluate opportunities to improve operation practices.
Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.
Energy storage will become indispensable to complement the uncertainty of intermittent renewable resources and to firm the electricity supply as renewable power generation becomes the mainstream new-built energy source and fossil fuel power plants are phased out to meet carbon-neutral utility targets. Current energy storage methods based on pumped storage hydropower or batteries have many limitations. Thermal energy storage (TES) has unique advantages in scale and siting flexibility to provide grid-scale storage capacity. A particle-based TES system is projected to have promising cost and performance characteristics to meet the future growing energy storage needs. This paper introduces the system and components required for particle TES to become technically and economically competitive. The system integrates electric particle heaters, particle TES within insulated concrete silos, and an efficient air-Brayton combined-cycle power system to provide power for storage durations up to several days via low-cost, high-performance storage cycles. Design specifications and cost estimation of major components in a commercial-scale system are presented in this paper. A techno-economic analysis based on preliminary component designs and performance indicates that particle TES integrated with an air-Brayton combined-cycle power system has a path to achieve the targeted levelized cost of storage of 5 ¢/kWh-cycle at a round-trip efficiency of 50% when taking low-cost energy-specific components and leveraging basic assets from existing thermal power plants. The cost model provides insights for further development and economic potentials for long-duration energy storage.