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At least 235 records · Page 13

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python’s iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).

14 SOLAR ENERGY↗

Fundamentals of Energy Security and Resilience [Slides]

This slide deck was developed for and presented at an Energy Fundamentals Course hosted by the Bangladesh University of Engineering and Technology (BUET) in October 2022. The National Renewable Energy Laboratory (NREL) helped organize this course in partnership with the United States Agency for International Development (USAID). The students in this four-day course were postgraduates and working professionals in the energy sector or related industries in Bangladesh. While some of the content in the slide deck is tailored to Bangladesh specifically, this presentation is intended to be a general primer on energy security and resilience that can be utilized for similar purposes by other universities or organizations throughout the world. The content of this slide deck is not intended to be fully comprehensive of all energy security and resilience concepts.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Capillary priming characteristics of a high capacity dual passage heat pipe

A parametric study of the forces governing the liquid-vapor interface was performed for the purpose of determining the capillary priming characteristics of Grumman's dual passage monogroove heat pipe when subjected to low-g or zero-g conditions. The static liquid-vapor interface configuration was determined through minimization of the free surface energies and a mathematical model and computer program which describe the time to prime was developed. Modeling predictions confirmed expectations of proper priming action and established the criteria for sizing of the liquid and vapor channels.

Peterson, G. P.↗

Mechanisms of Low-Level Jet Formation in the U.S. Mid-Atlantic Offshore

Low level jets (LLJs) in the atmosphere exhibit a local windspeed maximum inthe boundary layer and are commonly observed both over land and in coastal environments. Because LLJs present strong positive shear beneath the maximum (or "jet nose") and negative shear above the nose, they pose a challenge to future offshore wind technology through their impacts on turbine performance and wakes. Summertime LLJs in the Great Plains have been attributed to frictional decoupling triggering an inertial oscillation, whilecoastal LLJs in California or the North Sea have additional driving mechanisms such as baroclinic forcing. By comparison, LLJs in the coastal US Mid-Atlantic have received less attention, with conflicting evidence about the mechanism that triggers these high shear events. Motivated by future wind energy development in the region, this work elucidates the atmospheric mechanisms of LLJ formation in the US Mid-Atlantic. Using observational data from two floating LiDAR buoys deployed bythe New York State Energy Research and Development Authority in the NY Bight, we identify four sustained LLJ events whose windspeeds and directions are well predicted by a WRF simulation (Weather Research and Forecasting Model). From analysis of the WRF data, we find that the four case studies provide evidence of concurrent inertial oscillation, reduced vertical mixing, and baroclinicity. In addition, we use simple atmospheric dynamics and a single-column-model to compare the relative contribution of each of these mechanisms to triggering the LLJ. By improving understanding and predictability of coastal mid-Atlantic LLJs, this work reduces uncertaintiesof wind energy deployment in the region, aiding the US's transition toward renewable energy.

atmospheric boundary layers↗

Numerical Validation of an Algorithm for Combined Soiling and Degradation Analysis of Photovoltaic Systems

We describe and demonstrate an open-source algorithm for simultaneously quantifying degradation and soiling of photovoltaic (PV) systems from energy-production time series data. The new analysis is based on year-on-year degradation rate analysis combined with stochastic rate and recovery soiling analysis. The algorithm is designed to fit into the workflow provided by RdTools, a Python module maintained by NREL and collaboratively developed with the community, which provides a framework and functions for degradation and loss-factor analysis of PV field data. We demonstrate the method on numerically simulated PV data sets and show that it reduces the root-mean-square error of the P50 degradation rate estimate when soiling is present.

14 SOLAR ENERGY↗

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Modeling Power Plant Siting Opportunities and Constraints in the Eastern Interconnection

The electrical transmission grid is a critical part of the U.S. national infrastructure, and its modernization is a U.S. Department of Energy (DOE) priority. We describe the Energy Zones Mapping Tool (EZMT), a unique, powerful, and public web-based system with multi-criteria decision analysis (MCDA) models for more than 20 power plant technologies and many other capabilities. Through a case study on natural gas combined cycle (NGCC) power plants in the Eastern Interconnection (EI), we provide an example of incorporating an EZMT MCDA model into a larger planning context, with projections of interconnection-level capacity expansion, thermoelectric power plant retirements, and water availability. Our results provide insights on candidate NGCC site distributions and the criteria influencing them. The case study provides both an efficient methodology for performing similar analyses and hundreds of candidate NGCC power plant sites that can be studied in more detail as potential project sites.

20 FOSSIL-FUELED POWER PLANTS↗

Using Open Data to Characterize Building Stock Trends for Energy and Equity Evaluation

This project provides a methodology for retrieving data from both existing public data and municipal open data sets to characterize changes in the building stock, socioeconomic, demographic, and climate indicators at a neighborhood scale. This methodology forms the basis for a geospatial dashboard to analyze and share the data. The developed methodology may be used to collect data for further energy equity analyses. Additional data sets, such as from other municipalities, may be incorporated in the future to expand the extents of the possible analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Distribution Integration Solution Cost Options (DISCO) [Slides]

DISCO is a Python-based NREL software tool for conducting scalable, repeatable distribution analyses. Although DISCO was originally developed to support photovoltaic impact analyses, it can also be used to understand the impact of other distributed energy resources and load changes on distribution systems. This presentation took place on April 9, 2024, and included NREL power grid researchers Sherin Ann Abraham and Shibani Ghosh.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

REopt Model Overview and Example Use Cases [Slides]

REopt(R) is a mixed-integer optimization model that minimizes the lifecycle cost of serving energy loads at a site. This work provides and introduction to the model along with its key workflow, techno-economic inputs, key outputs, and key caveats for readers to understand REopt the when, why, how of using this model. This resource also includes helpful links related to REopt model and the data sources it uses during the optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

ResStock Technical Reference Documentation (V.3.3.0)

ResStock™ is the best-in-class building stock energy model for simulating and publishing energy use, utility bills, and greenhouse gas emissions from the residential sector of the U.S. ResStock answers two primary questions: (1) How is energy used in the U.S. residential building stock? and (2) What is the timeseries aggregate impact of energy technologies? Specifically, ResStock quantifies energy use across geographical locations, demographic groups, building types, fuels, end uses, and time of day. Additionally, it details the impact of efficiency or electrification measures: total changes in the amount of energy used by measure; where or in what use cases efficiency or electrification upgrade measures save energy; when or at what times of day savings occur; and which building stock or demographic segments have the biggest savings potential. This model, and the datasets it produces, are foundational to identifying pathways to affordable and equitable decarbonization of the U.S. residential buildings sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ComStock Measure Scenario Documentation: Reduced Thermostat Setbacks for Heat Pumps

This report provides documentation of a measure for the End Use Savings Shapes project, which consists of analysis of different energy efficiency and renewable energy measures on the commercial building stock, using NREL's ComStock tool. The measure consists of a reduction in thermostat setbacks for air-source heat pump RTUs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Addressing Rising Energy Demand Through Innovation

The U.S. is facing a significant increase in energy demand, driven by AI advancements, the rapid expansion of data centers, manufacturing and industrial growth, and the electrification of transportation and buildings. Buildings alone account for approximately 75% of U.S. electricity consumption and 40% of total energy use. To address these challenges, NLR leverages its state-of-the-art research facilities, advanced energy modeling, hardware-in-the-loop emulation, and real-world demonstrations to provide data-driven insights that de-risk emerging energy solutions, increase efficiency and demand flexibility, optimize grid controls, and identify vulnerabilities to enhance energy security. This presentation will highlight our research ecosystem and its role in supporting a more reliable, affordable, and adaptive energy infrastructure in the face of accelerating demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗