Search NASA⌕ Search

SEARCH · Search NASA

Results for “variable resources”

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.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Exploring the Grid Value of Offshore Wind Energy in Oregon

The significant offshore wind energy potential of Oregon faces several challenges, including a power grid which was not developed for the purpose of transmitting energy from the ocean. The grid impacts of the energy resource are considered through the lenses of (i) resource complementarity with Variable Renewable Energy resources; (ii) correlations with load profiles from the four balancing authorities with territory in Oregon; and (iii) spatial value to regional and coastal grids as represented through a production cost model of the Western Interconnection. The capacity implications of the interactions between offshore wind and the historical east-to-west power flows of the region are discussed. The existing system is shown to accommodate more than two gigawatts of offshore wind interconnections with minimal curtailment. Through three gigawatts of interconnection, transmission flows indicate a reduction of coastal and statewide energy imports as well as minimal statewide energy exports.

17 WIND ENERGY↗
DOE OSTI2013

Modeling of GE Appliances: Final Presentation

The Energy Information Administration (EIA) estimates that electricity use will increase by more than 30 percent by 2035, and that residential electricity usage will increase by 23 percent. During this time, peak electricity demand is expected grow at an even greater rate, requiring significant investment in system capacity. In addition, increased penetration of intermittent renewable resources will increase system variability, requiring additional resources to mitigate the variability associated with generator output. Widespread adoption of demand response (DR) enabled appliances, thermostats, and other demand-side resources can result in significant reduction to peak electrical demand and provide potential grid stabilization benefits. The key to adoption is to provide this resource at a cost commensurate with traditional grid capabilities, and to appropriately share both the costs and the benefits between the consumer and the service provider (i.e., the utility). However, the business model for investing in demand-side resources cannot be determined without an estimation of the possible technical benefits and how those benefits translate to monetary value in current electrical market structures. GE Appliances has developed a line of appliances that will have the capability of offering several levels of demand reduction actions based on information received from the utility grid, often in the form of price or grid status. However due to a number of factors, including the number of DR-enabled appliances available at any given time, the reduction of diversity factor due to the synchronizing control signal, and the percentage of consumers who may override the utility signal, it can be difficult to predict the aggregate response of a large number of residences. The effects of these behaviors have been modeled and simulated in the Pacific Northwest National Laboratory (PNNL) developed open-source software, GridLAB-D™, including evaluation of appliance controls, improvement to current algorithms, and development of aggregate control methodologies. The results of these simulations provide an estimation of the possible technical benefits (e.g., peak load reduction) attributable to smart appliances, but do not describe the monetary value of these services. This report is the final in a series of three reports funded by U.S. Department of Energy Office of Electricity Delivery and Energy Reliability (DOE-OE) in collaboration with GE Appliances’ through a Cooperative Research and Development Agreement (CRADA) to describe the potential of GE Appliances’ DR-enabled appliances to provide benefits to the utility grid.

Demand Response↗
DOE OSTI2018

Greening the Grid: Solar and Wind Grid Integration Study for the Luzon-Visayas System of the Philippines

The Republic of the Philippines is home to abundant solar, wind, and other renewable energy (RE) resources that contribute to the national government's vision to ensure sustainable, secure, sufficient, accessible, and affordable energy. Because solar and wind resources are variable and uncertain, significant generation from these resources necessitates an evolution in power system planning and operation. To support Philippine power sector planners in evaluating the impacts and opportunities associated with achieving high levels of variable RE penetration, the Department of Energy of the Philippines (DOE) and the United States Agency for International Development (USAID) have spearheaded this study along with a group of modeling representatives from across the Philippine electricity industry, which seeks to characterize the operational impacts of reaching high solar and wind targets in the Philippine power system, with a specific focus on the integrated Luzon-Visayas grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

An Analysis of Thermal Plant Flexibility Using a National Generator Performance Database

Grid integration studies are key to understanding our ability to integrate variable generation resources into the power system and evaluating the associated costs and benefits. In these studies, it is important to understand the flexibility of the thermal power fleet, including how thermal plants operate at part load. Without a comprehensive understanding of thermal plant operation, we may over- or underestimate our ability to integrate variable generation resources and thus draw incomplete or inaccurate conclusions regarding their potential economic and environmental effects. The only public data source for understanding many elements of the operational characteristics of the thermal fleet is the U.S. Environmental Protection Agency Clean Air Markets database of historical power plant operation. However, though these data sets have been widely utilized, their use has proven to be difficult, and methods to clean and filter the data are not transparent. Here, we describe the database and a method to clean and filter it. We then use the cleaned database to demonstrate several characteristics of historical plant operation, including frequent part load operation. Finally, we provide a cleaned data set with heat rate curves and describe how to use it in general modeling activities and analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Sources of Operational Flexibility, Greening the Grid

Changes in electricity demand and generation must be constantly balanced to maintain power system stability and reliability. Operational flexibility refers to the ability of a power system to respond to these changes. Power systems are designed and operated to efficiently manage variability and uncertainty in electricity demand and resource availability. Variable renewable energy (VRE) increases this inherent variability and uncertainty, and thus increases the need for flexibility. Systems with significant variability and uncertainty require flexible generators that can rapidly change output, operate efficiently at lower outputs, and operate for short durations. This flexibility in turn can reduce the need to curtail (decrease the output of) solar and wind generation; improve investor confidence in VRE and revenue streams; decrease the risk of negative market pricing (which results when conventional generators cannot turn down to low outputs during times of high VRE output); and reduce environmental impacts by increasing system efficiency and maximizing the utilization of VRE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Modeling the Grid Integration of Variable Renewable Energy (VRE) Resources

The presentation was given at the November 21-23, 2018 'CEM Days - Integration of Renewables in the Electric Sector: Paths and Challenges to Energy Planning' in Rio de Janeiro, Brazil. It provides a summary of NREL's current modeling approaches, datasets, and considerations for integrating renewables into the power system. It also covers current model method improvement efforts, including our vision for a truly integrated modeling approach.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Operational Probabilistic Tools for Solar Uncertainty (OPTSUN) (Final Project Report for DOE Solar Forecasting II Project)

Increasing levels of solar PV can challenge system operations and may require novel methods to operate the power system reliably and efficiently. Power system operating plans generally use deterministic forecasts, in which the variable energy resources are represented by the expected value for each interval of the decision horizon. Probabilistic forecasts are relatively new but have the potential to address the shortfalls of deterministic forecasts. However, understanding how best to use such forecasts is still a key gap in industry and was the focus of this project. The project had three workstreams. In a forecasting workstream, improvements were made to baseline probabilistic forecasts using a number of new approaches such as machine learning methods and improved input data. In a design workstream, advanced simulation tools used these forecasts to investigate newly proposed reserve determination methods. Lastly, in a demonstration workstream a scheduling management platform (SMP) was developed to leverage probabilistic forecasts in a modular and customizable manner. In order to study the benefits that could be accrued, the project team collaborated with three utility partners (Duke Energy, Southern Company and Hawaiian Electric) to deliver improved probabilistic forecasts for each region and to model each region in case studies using advanced production cost modeling tools. Different methods to determine operating reserve requirements from probabilistic forecasts were developed, simulated, and tested across each region. The benefits of using these newly proposed methods varied by utility, but, in general, using probabilistic forecasts as well as historical data to set the reserve requirements seems to improve reliability related results, with less risk of reserve or supply shortfalls. The cost implications were not always straightforward; in some cases the new methods could show a reduction in expected operating costs, but often the increase in reserves associated with better risk mitigation using probabilistic forecasts could result in an increase in operating costs in the simulations. The SMP tool was developed to process probabilistic forecasts from their initial receipt through to scheduling decisions. This open-source tool consists of several modules for scenario development, reserve requirements calculation, and visualization. The SMP tool was demonstrated to a wide range of operators and stakeholders at all three utilities and further improved based on their feedback. The tool will be available on www.epri.com/optsun. The proposed probabilistic information-based reserve determination approaches have the potential to be implemented by different regions to ensure an economic and reliable power system operation on power systems integrating increasing levels of variable renewable resources. The innovative yet practical methods developed in this project demonstrated tangible benefits from using probabilistic forecasts beyond just study-based assessments to include three unique balancing areas. The demonstrated benefits across the multiple utility environments, are expected to provide system operators in all regions the confidence required and a platform to adopt the new forecasting and operating methods.

14 SOLAR ENERGY↗
DOE OSTI2020

Pumped Storage Hydropower (PSH) FAST Commissioning Prize Technical Analysis

The US energy landscape has undergone major changes over the past 10 years and will continue to see significant changes in future decades as the power grid increases its reliance on variable renewable energy resources. Because of the inherent variability of these resources, renewable energy growth may require additional energy storage capacity to provide flexible load-following capabilities and other grid services that can quickly adjust to changes in energy demand and generation. Pumped storage hydropower (PSH)—one such energy storage technology—uses pumps to convey water from a lower reservoir to an upper reservoir for energy storage and releases water back to the lower reservoir via a powerhouse for hydropower generation. PSH facility pump and generation cycling often follows economic and energy demand conditions. Across the United States, 43 PSH facilities are in operation and 55 projects are in various permitting or licensing stages. Altogether, the 43 operational projects provide the wide majority (95%) of utility-scale electricity storage in the United States. These facilities also provide significant power and nonpower grid benefits, including large-scale electrical system reserve capacity, grid reliability support, and electricity supply-demand balancing through quick-response capabilities and operational flexibility. PSH systems can accomplish these at a scale (e.g., size) and cost that makes these systems highly attractive from a technical standpoint. Although these research concepts are still in their infancy, they demonstrate promising potential as future PSH energy storage technologies. Although PSH has many advantages, development in the United States has effectively stalled since the 1990s, partially because of the magnitude of project costs and financing interest during development and construction, the length of time from project investment until project revenue begins, permitting challenges, construction risks, competition from other storage technologies (e.g., batteries, hydrogen storage), and electricity market evolution and uncertainty. In short, the time, cost, and risk associated with modern PSH development have resulted in limited growth in the United States recently, despite the growing energy storage demand stemming from increased wind and solar power deployment. Technology innovation is needed to help reduce PSH commissioning time, cost, and risk, particularly during the post-licensing phase of project development. To address challenges facing the PSH industry and to improve PSH commissioning timelines, the US Department of Energy (DOE) Water Power Technologies Office (WPTO) initiated the PSH Furthering Advancements to Shorten Time to (FAST) Commissioning Prize project.

13 HYDRO ENERGY↗
DOE OSTI2012

Probability-Weighted LMP and RCP for Day-Ahead Energy Markets using Stochastic Security-Constrained Unit Commitment: Preprint

Variable renewable generation resources are increasing their penetration on electric power grids. These resources have weather-driven fuel sources that vary on different time scales and are difficult to predict in advance. These characteristics create challenges for system operators managing the load balance on different timescales. Research is looking into new operational techniques andstrategies that show great promise on facilitating greater integration of variable resources. Stochastic Security-Constrained Unit Commitment models are one strategy that has been discussed in literature and shows great benefit. However, it is rarely used outside the research community due to its computational limits and difficulties integrating with electricity markets. This paper discusses howit can be integrated into day-ahead energy markets and especially on what pricing schemes should be used to ensure an efficient and fair market.

17 WIND ENERGY↗
NASA NTRS2014

The Development of a Long-Term, Continually Updated Global Solar Resource at 10 km Resolution: Preliminary Results From Test Processing and Continuing Plans

Background: Considering the likelihood of global climatic weather pattern changes and the global competition for energy resources, there is an increasing need to provide improved and continuously updated global Earth surface solar resource information. Toward this end, a project was funded under the NASA Applied Science program involving the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), National Renewable Energy Laboratory (NREL), the State University of New York/Albany (SUNY) and the NOAA National Climatic Data Center (NCDC) to provide NREL with a global long-term advanced global solar mapping production system for improved depiction of historical solar resources and variability and to provide a mechanism for continual updates of solar resource information. This new production system is made possible by the efforts of NOAA and NASA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 3-hourly basis beginning from July 1983. The old version of the ISCCP data provided this information for all the world TMs available geosynchronous satellite systems and NOAA TMs AVHRR data sets at a 30 km effective resolution. This new version aims to provide a new and improved satellite calibration at an effective 10 km resolution. Thus, working with SUNY, NASA will develop and test an improved production system that will enable NREL to continually update the Earth TM solar resource. Objective and Methods: In this presentation, we provide a general overview of this project together with samples of the new solar irradiance mapped data products and comparisons to surface measurements at various locations across the world. An assessment of the solar resource values relative to calibration uncertainty and assumptions are presented. Errors resulting assumptions in snow cover and background aerosol amount are described. These uncertainties and the statistics of the agreement between the measurements and new satellite estimates are also reviewed and compared to other solar data sets. Findings and Conclusions: Preliminary results show that insolation values show an overall small bias (less than 1%) with a RMS of 25% relative to surface measurements. Exceptions at certain locations were found and will be discussed relative to the uncertainties identified above. Lastly, we will identify the next steps in the development and improvement of this production system including some accuracy goals in preparation for ultimate delivery to NREL.

Stackhouse, P.↗
DOE OSTI2016

Wholesale electricity market design with increasing levels of renewable generation: Revenue sufficiency and long-term reliability

This paper discusses challenges that relate to assessing and properly incentivizing the resources necessary to ensure a reliable electricity system with growing penetrations of variable generation (VG). The output of VG (primarily wind and solar generation) varies over time and cannot be predicted precisely. Therefore, the energy from VG is not always guaranteed to be available at times when it is most needed. This means that its contribution towards resource adequacy can be significantly less than the contribution from traditional resources. Variable renewable resources also have near-zero variable costs, and with production-based subsidies they may even have negative offer costs. Because variable costs drive the spot price of energy, this can lead to reduced prices, sales, and therefore revenue for all resources within the energy market. The characteristics of VG can also result in increased price volatility as well as the need for more flexibility in the resource fleet in order to maintain system reliability. Furthermore, we explore both traditional and evolving electricity market designs in the United States that aim to ensure resource adequacy and sufficient revenues to recover costs when those resources are needed for long-term reliability. We also investigate how reliability needs may be evolving and discuss how VG may affect future electricity market designs.

17 WIND ENERGY↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
DOE OSTI2020

Probabilistic Zonal Reserve Requirements for Improved Energy Deliverability with Wind Power

In power systems with high penetration of renewable energy resources, uncertainty and variability of these stochastic resources introduce additional challenges for the operation of power systems. To improve the power system's reliability in the face of uncertainty, reserves are required as additional generation capacity to rebalance the power system following random disturbances. However, reserve deliverability is not guaranteed, because it may encounter potential transmission line congestion. Zonal reserve requirements can address this issue, but operators lack efficient ways to allocate reserves to zones while accounting for wind power forecast uncertainty. Herein we propose a methodology for probabilistic zonal reserve requirements to address wind power forecast uncertainties. This method estimates the probability distribution of line flows based on the system generation margin and injection shift factor. This estimate is then used to construct pre-defined and post-zonal reserve requirements. Case studies demonstrate that the proposed method efficiently schedules energy and reserves to balance energy and manage deliverability with wind power forecast uncertainty.We also discuss operational implications of the proposed method.

17 WIND ENERGY↗
DOE OSTI2020

Implications of a regional resource adequacy program on utility integrated resource planning: Study for the Western United States

LBNL collaborated with the Western Interstate Energy Board and the University of Texas, Austin to investigate the implications of a regional resource adequacy (RA) program on utility integrated resource planning. This report is focused on an active policy discussion to develop a novel voluntary program to share capacity resource and improve RA in the Western Interconnection. This report identifies two RA components of IRP that will be highly impacted by a regional RA program: resource capacity accreditation and RA targets. There are at least four resources that will require specific attention for their capacity credit calculation: (1) variable renewable resources, (2) demand-side resources, (3) hydropower, and (4) contracts. It will then be necessary to decide on a RA target reliability metric (e.g., a planning reserve margin) that is at least the minimum requirement in IRPs to ensure consistency in RA requirement calculations. The report finds that load forecasting and transmission expansion analyses will be moderately impacted, but that most of IRP components will not be significantly impacted. The report includes (i) a review of traditional resource adequacy practices in IRP; (ii) a case study of an existing regional RA program that interacts with IRP (Southwest Power Pool) and (iii) an overview of the NWPP regional resource adequacy proposal that is the object of this work. This paper does not (1) advocate for or against a regional RA program for the NWPP, (2) make detailed design recommendations for this program, or (3) assess its benefits and costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1% and as large as 0.5% for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

17 WIND ENERGY↗