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DOE OSTI2021

Absorbing the Sun: Operational Practices and Balancing Reserves in Florida's Municipal Utilities

The Florida Reliability Coordinating Council (FRCC) power system is comprised of multiple balancing authorities ranging in size from Gainesville Regional Utilities (GRU) with a 2019 summer net firm demand of 429 MW to Florida Power & Light with a 2019 summer net firm demand of 22,510 MW; and including cooperative, municipal, and investor-owned utilities. As all of these balancing authorities are and have plants to continue installing significant quantities of utility-scale solar photovoltaics, one relevant question is how much operating reserves they will need to hold as solar penetrations increase. While there are estimates of how regulating and flexibility reserve requirements change with solar penetration, the literature almost exclusively focuses on large balancing authorities with sub-hourly dispatch. In this work we analyze how reserve needs change not only with solar photovoltaic penetration, but also balancing authority size and operational practices. We find that, measured as a fraction of load, smaller balancing authorities with less frequent load and solar forecasts and less frequent dispatch need more reserves. Such utilities' reserve needs also increase more with increasing solar deployment as compared to larger or more frequently dispatched balancing authorities. These impacts are most acute for GRU. We find that moving from day-ahead to hour-ahead load and solar forecasting and system dispatch could enable GRU to incorporate 32% solar generation with median reserves at 20% instead of 60% of load; and that median reserve needs could drop further to about 10% of load if Florida's municipal utilities formed a reserve sharing group and moved to sub-hourly dispatch.

balancing authority↗

NASA NTRS2015

Space Weather Products and Tools Used in Auroral Monitoring and Forecasting at CCMC/SWRC

Key points discussed in this chapter are (1) the importance of aurora research to scientific advances and space weather applications, (2) space weather products at CCMC that are relevant to aurora monitoring and forecasting, and (3) the need for more effort from the whole community to achieve a better and long‐lead‐time forecast of auroral activity. Aurora, as manifestations of solar wind−magnetosphere−ionosphere coupling that occurs in a region of space that is relatively easy to access for sounding rockets, satellites, and other types of observational platforms, serves as a natural laboratory for studying the underlying physics of the complex system. From a space weather application perspective, auroras can cause surface charging of technological assets passing through the region, result in scintillation effects affecting communication and navigation, and cause radar cluttering that hinders military and civilian applications. Indirectly, an aurora and its currents can induce geomagnetically induced currents (GIC) on the ground, which poses major concerns for the wellbeing and operation of power grids, particularly during periods of intense geomagnetic activity. In addition, accurate auroral forecasting is desired for auroral tourism. In this chapter, we first review some of the existing auroral models and discuss past validation efforts. Such efforts are crucial in transitioning a model(s) from research to operations and for further model improvement and development that also benefits scientific endeavors. Then we will focus on products and tools that are used for auroral monitoring and forecasting at the Space Weather Research Center (SWRC). As part of the CCMC (Community Coordinated Modeling Center), SWRC has been providing space weather services since 2010.

Zheng, Yihua↗

NASA NTRS2015

Space Weather Products and Tools Used in Auroral Monitoring and Forecasting at CCMC/SWRC

Key points discussed in this chapter are (1) the importance of aurora research to scientific advances and space weather applications, (2) space weather products at CCMC that are relevant to aurora monitoring and forecasting, and (3) the need for more effort from the whole community to achieve a better and long-lead- time forecast of auroral activity. Aurora, as manifestations of solar wind-magnetosphere-ionosphere coupling that occurs in a region of space that is relatively easy to access for sounding rockets, satellites, and other types of observational platforms, serves as a natural laboratory for studying the underlying physics of the complex system. From a space weather application perspective, auroras can cause surface charging of technological assets passing through the region, result in scintillation effects affecting communication and navigation, and cause radar cluttering that hinders military and civilian applications. Indirectly, an aurora and its currents can induce geomagnetically induced currents (GIC) on the ground, which poses major concerns for the wellbeing and operation of power grids, particularly during periods of intense geomagnetic activity. In addition, accurate auroral forecasting is desired for auroral tourism. In this chapter, we first review some of the existing auroral models and discuss past validation efforts. Such efforts are crucial in transitioning a model(s) from research to operations and for further model improvement and development that also benefits scientific endeavors. Then we will focus on products and tools that are used for auroral monitoring and forecasting at the Space Weather Research Center (SWRC). As part of the CCMC (Community Coordinated Modeling Center), SWRC has been providing space weather services since 2010.

Zheng, Yihua↗

NASA NTRS2022

Space Weather: From Solar Origins to Risks and Hazards Evolving in Time

Space Weather is the portion of space physics that has a direct effect onhumankind. Space Weather is an old branch of space physics that originatesback to 1808 with the publication of a paper by the great naturalist Alexandervon Humboldt (Von Humboldt, Ann. Phys. 1808, 29, 425–429),first defining a“Magnetische Ungewitter”or magnetic storm from auroral observations fromhis home in Berlin, Germany. Space Weather is currently experiencing explosivegrowth, because its effects on human technologies have become more andmore diverse. Space Weather is due to the variability of solar processes thatcause interplanetary, magnetospheric, ionospheric, atmospheric and groundlevel effects. Space Weather can at times have strong impacts on technologicalsystems and human health. The threats and risks are not hypothetical, and in theevent of extreme Space Weather events the consequences could be quitesevere for humankind. The purpose of the review is to give a brief overall view ofthe full chain of physical processes responsible for Space Weather risks andhazards, tracing them from solar origins to effects and impacts in interplanetaryspace, in the Earth’s magnetosphere and ionosphere and at the ground. Inaddition, the paper shows that the risks associated with Space Weather have notbeen constant over time; they have evolved as our society becomes more andmore technologically advanced. The paper begins with a brief introduction tothe Carrington event, arguably the greatest geomagnetic storm in recordedhistory. Next, the descriptions of the strongest known Space Weather processesare reviewed, tracing them from their solar origins. The concepts ofgeomagnetic storms and substorms are briefly introduced. The main effects/impacts of Space Weather are also considered, including geomagneticallyinduced currents (GICs) which are thought to cause power outages. Theeffects of radiation on avionics and human health, ionospheric effects andimpacts, and thermosphere effects and satellite drag will also be discussed.Finally, we will discuss the current challenges of Space Weather forecasting andexamine some of the worst-case scenarios.

Natalia Buzulukova↗

DOE OSTI2023

Clear-Sky Probability for the 2023 Annular Solar Eclipse and the 2024 Total Solar Eclipse Using the NREL National Solar Radiation Database

The National Renewable Energy Laboratory (NREL) and collaborators have created a clear-sky probability analysis to help guide viewers to the 2023 annular solar eclipse and the 2024 total solar eclipse. Using cloud and solar data from the NREL National Solar Radiation Database (NSRDB), the analysis provides cloudless-sky probabilities specific to the date, time, and location of each eclipse with a 4-km resolution. Though not intended to be an eclipse weather forecast, the detailed maps can help guide eclipse enthusiasts to likely optimal viewing locations. Additionally, high-resolution data are presented for the centerline of the path of each eclipse, representing the likelihood for cloudless skies and atmospheric clarity. The NSRDB provides industry, academia, and other stakeholders with high-resolution solar irradiance data to support feasibility analyses for photovoltaic and concentrating solar power generation projects.

14 SOLAR ENERGY↗

NASA NTRS1991

Key issues in space nuclear power

The future appears rich in missions that will extend the frontiers of knowledge, human presence in space, and opportunities for profitable commerce. Key to the success of these ventures is the availability of plentiful, cost effective electric power and assured, low cost access to space. While forecasts of space power needs are problematic, an assessment of future needs based on terrestrial experience has been made. These needs fall into three broad categories: survival, self sufficiency, and industrialization. The cost of delivering payloads to orbital locations from LEO to Mars has been determined and future launch cost reductions projected. From these factors, then, projections of the performance necessary for future solar and nuclear space power options has been made. These goals are largely dependent upon orbital location and energy storage needs. Finally the cost of present space power systems has been determined and projections made for future systems.

Brandhorst, Henry W.↗

NASA NTRS1991

Key issues in space nuclear power challenges for the future

Brandhorst, Henry W., Jr.↗

NASA NTRS2016

NASA's Radioisotope Power Systems Planning and Potential Future Systems Overview

The goal of NASA's Radioisotope Power Systems (RPS) Program is to make RPS ready and available to support the exploration of the solar system in environments where the use of conventional solar or chemical power generation is impractical or impossible to meet the needs of the missions. To meet this goal, the RPS Program, working closely with the Department of Energy, performs mission and system studies (such as the recently released Nuclear Power Assessment Study), assesses the readiness of promising technologies to infuse in future generators, assesses the sustainment of key RPS capabilities and knowledge, forecasts and tracks the Program's budgetary needs, and disseminates current information about RPS to the community of potential users. This process has been refined and used to determine the current content of the RPS Program's portfolio. This portfolio currently includes an effort to mature advanced thermoelectric technology for possible integration into an enhanced Multi-Mission Radioisotope Generator (eMMRTG), sustainment and production of the currently deployed MMRTG, and technology investments that could lead to a future Stirling Radioisotope Generator (SRG). This paper describes the program planning processes that have been used, the currently available MMRTG, and one of the potential future systems, the eMMRTG.

Nuclear↗

NASA NTRS2016

NASA RPS Program Overview: A Focus on RPS Users

The goal of NASA's Radioisotope Power Systems (RPS) Program is to make RPS ready and available to support the exploration of the solar system in environments where the use of conventional solar or chemical power generation is impractical or impossible to meet the needs of the missions. To meet this goal, the RPS Program, working closely with the Department of Energy, performs mission and system studies (such as the recently released Nuclear Power Assessment Study), assesses the readiness of promising technologies to infuse in future generators, assesses the sustainment of key RPS capabilities and knowledge, forecasts and tracks the Programs budgetary needs, and disseminates current information about RPS to the community of potential users. This process has been refined and used to determine the current content of the RPS Programs portfolio. This portfolio currently includes an effort to mature advanced thermoelectric technology for possible integration into an enhanced Multi-Mission Radioisotope Generator (eMMRTG), sustainment and production of the currently deployed MMRTG, and technology investments that could lead to a future Stirling Radioisotope Generator (SRG). This paper describes the program planning processes that have been used, the currently available MMRTG, and one of the potential future systems, the eMMRTG.

Radioisotope↗

NASA NTRS2016

NASA's Radioisotope Power Systems Planning and Potential Future Systems Overview

The goal of NASA's Radioisotope Power Systems (RPS) Program is to make RPS ready and available to support the exploration of the solar system in environments where the use of conventional solar or chemical power generation is impractical or impossible to meet the needs of the missions. To meet this goal, the RPS Program, working closely with the Department of Energy, performs mission and system studies (such as the recently released Nuclear Power Assessment Study), assesses the readiness of promising technologies to infuse in future generators, assesses the sustainment of key RPS capabilities and knowledge, forecasts and tracks the Programs budgetary needs, and disseminates current information about RPS to the community of potential users. This process has been refined and used to determine the current content of the RPS Programs portfolio. This portfolio currently includes an effort to mature advanced thermoelectric technology for possible integration into an enhanced Multi-Mission Radioisotope Generator (eMMRTG), sustainment and production of the currently deployed MMRTG, and technology investments that could lead to a future Stirling Radioisotope Generator (SRG). This paper describes the program planning processes that have been used, the currently available MMRTG, and one of the potential future systems, the eMMRTG.

Radioisotope↗

NASA NTRS2016

Usage of Multi-Mission Radioisotope Thermoelectric Generators (MMRTGs) for Future Potential Missions

The goal of NASAs Radioisotope Power Systems (RPS) Program is to make RPS ready and available to support the exploration of the solar system in environments where the use of conventional solar or chemical power generation is impractical or impossible to meet the needs of the missions. To meet this goal, the RPS Program, working closely with the Department of Energy, performs mission and system studies (such as the recently released Nuclear Power Assessment Study), evaluates the readiness of promising technologies to infuse in future generators, assesses the sustainment of key RPS capabilities and knowledge, forecasts and tracks the Programs budgetary needs, and disseminates current information about RPS to the community of potential users. This presentation focuses on the needs of the mission community and provides users a better understanding of how to integrate the MMRTG (Multi-Mission Radioisotope Thermoelectric Generator).

Radioisotope↗

NASA NTRS1989

Challenges for future space power systems

The future appears rich in missions that will extend the frontiers of knowledge, human presence in space, and opportunities for profitable commerce. The key to success of these ventures is the availability of plentiful, cost effective electric power and assured, low cost access to space. While forecasts of space power needs are problematic, an assessment of future needs based on terrestrial experience was made. These needs fall into three broad categories-survival, self sufficiency and industrialization. The cost of delivering payloads to orbital locations from low earth orbit (LEO) to Mars was determined and future launch cost reductions projected. From these factors, then, projections of the performance necessary for future solar and nuclear space power options were made. These goals are largely dependent upon orbital location and energy storage needs.

Brandhorst, Henry W., Jr.↗

DOE OSTI2025

Evaluating Microchannel Heat Exchanger Lifetime for Concentrating Solar Power Applications Research Performance Progress Report (RPPR-1)

Microchannel heat exchanger technology is being pursued for next generation CSP concepts for primary power cycle heat addition and power cycle heat recuperation due to the high heat transfer coefficients and pressure containment advantages of small sCO 2 channels. The economics of future CSP plants as dictated by the SETO 2020 or 2030 targets depend on a heat exchanger with a 30-year lifetime (resisting creep, fatigue, corrosion, erosion) and operational characteristics such as fast ramping and the ability to withstand thermal shock. However, the lifetime and operational limits of microchannel heat exchangers operating at high temperatures, particularly those constructed from high-nickel alloys, are not well known. This uncertainty has resulted in heat exchanger vendors not being able to accurately forecast heat exchanger lifetime as required by customers, specify operational limits as required by process engineers to prevent premature heat exchanger failure, or overdesign heat exchanger which leads to higher cost than necessary.

14 SOLAR ENERGY↗

DOE OSTI2022

A reforecasting-based dynamic reserve estimation for variable renewable generation and demand uncertainty

The installed capacity of renewables-based energy sources has been increasing in traditional power systems. In order to accommodate the increased variability and uncertainty associated with the deeper penetration of renewable sources like solar and wind, adjusted amounts of dynamic reserve are needed. Although probabilistic dynamic reserve estimation methods have been previously developed, most of them consider the uncertainty to be represented by parametric density functions that tend to perform poorly under extreme events and, moreover, neglect uncertainty introduced by the forecasting model itself. Toward addressing these limitations, this work presents, for the first time, a dynamic reserve estimation method for flexibility that incorporates nonparametric density estimation and a machine learning based reforecasting to provide a day-ahead prediction of the mean and spread of uncertainty around the base forecast. The prediction is, in turn, used to estimate the up and down reserve relative to the base forecast. Here, the present method takes various endogenous and exogenous features, including the calendar variables, as input to estimate the day-ahead reserve. Using a combination of reforecasting and dynamic reserve estimation techniques, the method is shown to adjust better to the dynamic nature of reserve requirements providing only what is needed to accommodate the expected deviations. Considering California Independent System Operator (CAISO) solar, wind and load data over an 18 month period, up to 67% reduction in the amount of reserve capacity needed for a one day reserve and reserve penalty for solar uncertainty is demonstrated. Additionally, the risk of reserve insufficiency in meeting the net demand is reduced by 20% with the proposed method.

14 SOLAR ENERGY↗

DOE OSTI2024

Evaluating Microchannel Heat Exchanger Lifetime for Concentrating Solar Power Applications FY24Q4 (RPPR-1)

Microchannel heat exchanger technology is being pursued for next generation CSP concepts for primary power cycle heat addition and power cycle heat recuperation due to the high heat transfer coefficients and pressure containment advantages of small sCO 2 channels. The economics of future CSP plants as dictated by the SETO 2020 or 2030 targets depend on a heat exchanger with a 30-year lifetime (resisting creep, fatigue, corrosion, erosion) and operational characteristics such as fast ramping and the ability to withstand thermal shock. However, the lifetime and operational limits of microchannel heat exchangers operating at high-temperatures, particularly those constructed from high-nickel alloys, are not well known. This uncertainty has resulted in heat exchanger vendors not being able to accurately forecast heat exchanger lifetime as required by customers, specify operational limits as required by process engineers to prevent premature heat exchanger failure, or overdesign heat exchanger which leads to higher cost than necessary. Our goal is to evaluate heat exchanger lifetime and operational limits for the manufacturing and prototype design for next-generation CSP heat exchanger technology through a combination of collecting experimental data and modeling studies.

14 SOLAR ENERGY↗

DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Efficient prediction of concentrating solar power plant productivity using data clustering

Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.

14 SOLAR ENERGY↗

DOE OSTI2022

Quantitative Ceilometer-Radar Studies of Clouds Field Campaign Report

The Long Island Solar Farm (LISF) is a 32-megawatt solar photovoltaic power plant built through a collaboration including BP Solar, the Long Island Power Authority (LIPA), and the U.S. Department of Energy (DOE). The LISF, located on the Brookhaven National Laboratory site, began delivering power to the LIPA grid in November 2011, and is currently one of the largest solar photovoltaic power plants in the eastern United States. It is generating enough renewable energy to power approximately 4,500 homes and is helping New York State meet its clean energy and carbon reduction goals. Brookhaven National Laboratory (BN)L has the LISF instrumented and is capturing solar insolation and power data that can be used for research purposes. Additional information on LISF is available here: https://www.bnl.gov/lisf/. A network of nine high-definition (HD cameras) and 32 pyranometers are deployed within the LISF for the purpose of monitoring the location and characteristics of clouds and available global horizontal irradiance at the surface across the region. Information collected by this network every 30 s is currently used in BNL’s solar NowCasting algorithm, which forecasts near-term solar energy availability accounting for the behavior of clouds. The most important parameters for forecasting the near-term solar energy availability are accurate estimations of their horizontal extend, horizontal motion, and cloud base height. Currently, images from nearly located cameras are used to estimate the cloud base height. In order to evaluate the potential of this method for estimating the cloud base height, we decided to deploy a Vaisala ceilometer at the LISF.

14 SOLAR ENERGY↗