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DOE OSTI2023

Assessing the Performance of a Circular Economy for Wind Energy Technologies: A Summary of Three Analytical Tools

A circular economy emphasizes the efficient use of all resources and presents opportunities for addressing series of economic and environmental objectives at local, regional, and national levels. Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. As a result, it is important to evaluate the performance and tradeoffs associated with circular economy transitions. This poster summaries three previously published analytical tools that were used to assess the performance of developing a circular economy for wind energy technologies: the Renewable Energy Materials Properties Database (REMPD), a circular economy agent-based model for wind blades (CE Wind ABM), and the Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework. The REMPD is a comprehensive database of materials used in wind and solar plants, including material quantities and physical materials availability. The CE Wind ABM allows us to understand how wind stakeholders' end-of-life behaviors influence wind blade circularity and evaluate the impact of regional variables (e.g., logistics and transportation). And, the CELAVI framework is a modular framework that can be used to evaluate the impacts associated with circular economy transitions. These three analytical tools have been applied to evaluate circular economy transitions for wind energy technologies and they could be expanded to other technologies and products.

agent-based modeling↗

DOE OSTI2023

Reducing Uncertainty in Offshore Wind Energy Yield Estimates via a Metocean Reference Site

The offshore wind industry is burgeoning in the coastal waters of the United States, specifically along the Atlantic. For wind energy to be successful, reliable observations and model simulations are needed for resource assessment and forecasting. While many of these activities have already begun, there is currently an absence of observations at hub-height in these waters, with the closest available hub-height measurements usually taken onshore. Deployment of floating lidars has occurred through various federally funded projects, but only encapsulates time periods of a couple of years at best. Private industry is also beginning to leverage floating lidars, but this data is often proprietary, and not shared with the general public. In this work, we make the case for a metocean reference site for long-term offshore wind energy. Specifically, we quantify the impact of having a metocean reference site compared to other methods of determining hub-height winds and energy production. We use an offshore floating lidar to directly measure the wind resource, and compare these measurements to predictions derived from other widely-available surface meteorological variables. These prediction methods (vertical extrapolation, machine learning, and NWP output) produce a variety of vertical wind speed profiles, of which produce different energy yield estimates for a reference offshore turbine (Figure 1). While some methods perform reasonably well against the lidar, the uncertainty in these energy yield estimates has financial implications, further illustrating the need for long-term measurements in coastal waters.

machine learning↗

DOE OSTI2013

Wave energy resources along the Hawaiian Island chain

Hawaii is subject to direct approach of swells from distant storms as well as seas generated by trade winds all year round. The island chain creates a localized weather system that modifies the wave energy resources from the far field. Here, we implement a nested computational grid across the major Hawaiian Islands at 3-min resolution in the global WaveWatch3 (WW3) model and utilize the Weather Research Forecast (WRF) model to provide high-resolution mesoscale wind forcing over the Hawaii region. Two hindcast studies representative of the year-round conditions provide a quantitative assessment of the regional wind and wave patterns as well as the wave energy resources along the Hawaiian Island chain. These events of approximately two weeks each have a range of wind speeds, ground swells, and wind waves for validation of the modeling system with satellite and buoy measurements prior to its general application.

13 HYDRO ENERGY↗

DOE OSTI2025

Validating Greater Sage-Grouse Individual-based Model (IBM) Tool (Final Report)

The project focused on validating the previously developed Greater Sage-Grouse Individual-based Model (GrSG IBM; LaGory et al. 2012, 2021). The objective was to transform this predictive, spatially and temporally explicit model into a portable resource to assist siting/resource managers in proactively assessing the cumulative impacts of wind energy development on the greater sage-grouse. Utilizing a bottom-up, individual-based approach, the GrSG IBM accounts for landscape context and species behavior, aiming to reduce uncertainty in estimating development impacts and support ecologically mindful land-based wind energy development. The validation effort covered approximately 6,540 km 2 near the Seven Mile Hill Wind Project in Wyoming. The GrSG IBM tool, built on the NetLogo platform (Tisue and Wilensky 2004), was executed over a 50-year period, with the analysis focusing on years following a 10-year initialization phase. Key results demonstrated the tool’s biological soundness across five key biological metrics: non-chick age class distribution (older than 10 weeks), adult sex ratio, life expectancy, population size, and overall population growth. For instance, the tool estimated that 58.6% of the non-chick population was reproductively immature, while the reference ranges from 51.4% to 57.8% (Patterson 1952, Rogers 1964). Experts confirmed the tool’s estimate was within a reasonable range for the species. The tool estimated average life expectancy of 1.43 years, while the reference ranges from 0.9 years to 1.1 years (Ammann 1957, Hamerstrom 1949). Experts also supported the model’s life-expectancy estimate as ecologically sound for the species in the study area. In terms of population change, the model estimated an annual shift between a 0.6% decline and a 1.0% increase over 50 years. While the reference suggests 2.9% annual decline in range-wide populations (Cortes et al. 2023), that includes many at-risk populations in South Dakota and Washington, for example. Our study area—in the northeastern part of Carbon County and western-edge of Albany County, Wyoming—is one of the remaining greater sage-grouse habitats supporting some of the most stable populations. Experts confirmed that the range of the annual population change spanning from a 0.6% decline to a 1.0% increase estimated by the tool was reasonable for our study area for this reason and confirmed that aligned with population estimates from existing studies on the greater sage-grouse and wind energy development in the study area (LeBeau et al. 2017a, Smith et al. 2024). Furthermore, the project showed that temporally explicit biological metrics generated by the GrSG IBM tool can complement the USGS’ Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET; Duchardt et al. 2021) by incorporating habitat restoration strategies into seasonal habitat suitability models to visualize population responses over time.

17 WIND ENERGY↗

DOE OSTI2014

Representation of the Solar Capacity Value in the ReEDS Capacity Expansion Model: Preprint

An important emerging issue is the estimation of renewables' contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to resource variability. Reliability-based methods, particularly, effective load-carrying capacity (ELCC), are considered to be the most robust techniques for addressing this resource variability. The Regional Energy Deployment System (ReEDS) capacity expansion model and other long-term electricity capacity planning models require an approach to estimating CV for generalized PV and system configurations with low computational and data requirements. In this paper we validate treatment of solar photovoltaic (PV) capacity value by ReEDS capacity expansion model by comparing model results to literature for a range of energy penetration levels. Results from the ReEDS model are found to compare well with both comparisons--despite not being resolved at an hourly scale.

14 SOLAR ENERGY↗

DOE OSTI2022

Hydro-battery Hybrids – A Case for Holistic Assessment of Hybrid Energy Systems

With increasing penetration of renewable energy resources like solar and wind, the flexibility offered by the hydropower generation facilities would be instrumental in providing grid reliability. However, hydropower’s capabilities are often constrained to meet asset management targets and environmental flow requirements. To meet these necessary requisites while simultaneously being able to utilize hydropower’s full potential, hydro plus battery hybrids offer a logical solution. However it is often challenging to make the case for economic feasibility of hydro-battery hybrids at the required scale of battery storage. To that end, this paper proposes a multi-objective optimization framework to maximize advantages of hydro-battery hybrid systems from three different avenues: new market opportunities, environmental benefits and machine wear and fatigue. Using an illustrative case study of a peaking plant, we simulate hydropower operations for with and without 120 MWh of battery storage for four different simulated flow patterns ranging from peaking operations to run-of-the-river operations and two intermediate flow patterns in between. The results demonstrate that hybridizing the hydropower plant can potentially generate additional revenue while simultaneously enable more environmental friendly operations and reduce the overall machine wear and tear.

Chalishazar, Vishvas H.↗

DOE OSTI2025

Optimal Operation and Impact Assessment of Distributed Wind for Improving Efficiency and Resilience of Rural Electricity Systems

This project aims to empower rural utilities by developing advanced optimization models and algorithms for effectively integrating distributed wind energy alongside battery storage and other distributed energy resources (DERs). The primary objectives are to reduce peak demand, ensure reliable emergency power supply, and regulate voltage and frequency. To address operational challenges, the project introduces innovative mitigation strategies and ultrafast assessment frameworks to evaluate the impacts of distributed wind and DERs on rural grids, offering actionable solutions to potential issues. Economic viability is assessed through cost-benefit analysis using real rural utility data, ensuring the practical application of the project outcomes.

17 WIND ENERGY↗

DOE OSTI2013

Assessment of Offshore Wind Farm Effects on Sea Surface, Subsurface and Airborne Electronic Systems (Final Report)

Offshore wind energy is a valuable resource that can provide a significant boost to the US renewable energy portfolio. A current constraint to the development of offshore wind farms is the potential for interference to be caused by large wind farms on existing electronic and acoustical equipment such as radar and sonar systems for surveillance, navigation and communications. The US Department of Energy funded this study as an objective assessment of possible interference to various types of equipment operating in the marine environment where offshore wind farms could be installed. The objective of this project was to conduct a baseline evaluation of electromagnetic and acoustical challenges to sea surface, subsurface and airborne electronic systems presented by offshore wind farms. To accomplish this goal, the following tasks were carried out: (1) survey electronic systems that can potentially be impacted by large offshore wind farms, and identify impact assessment studies and research and development activities both within and outside the US, (2) engage key stakeholders to identify their possible concerns and operating requirements, (3) conduct first-principle modeling on the interactions of electromagnetic signals with, and the radiation of underwater acoustic signals from, offshore wind farms to evaluate the effect of such interactions on electronic systems, and (4) provide impact assessments, recommend mitigation methods, prioritize future research directions, and disseminate project findings. This report provides a detailed description of the methodologies used to carry out the study, key findings of the study, and a list of recommendations derived based the findings.

17 WIND ENERGY↗

DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Unified Modeling Architecture for Load Management in Extreme Heat: The New York City Case

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on this key subject by assessing the feasibility of using coastal-urban renewable energy resources, namely, offshore wind and rooftop photovoltaic systems, to meet electricity demand of New York City during the intense recent heat wave period of June 2025. A unified modeling framework, based on the urbanized weather research and forecasting model, is used to simulate climate, renewable resources, and energy demand variables. Findings show significant energy load mismatch of approximately 1150 GWh over the month, between the demand and the combined renewable generation outcome. Three storage integration scenarios are analyzed to mitigate the deficits, reducing said deficits by a minimum of approximately 9% over the duration of the month. This study provides a transferable modeling framework tool for evaluating renewable integration in dense urban environments that can be used by grid operators to support grid resilience during extreme heat events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Energy Options Analysis Project (Final Report)

This Bear River Band of Rohnerville Rancheria (BRB) Energy Options Analysis Project provides a rigorous and comprehensive near-term renewable energy implementation plan that aligns with the BRB’s long term strategic vision of “zero net annual utility energy consumption.” Final recommendations were arrived at by following four key project phases:A gas and electricity load assessment was conducted for all existing buildings using historic consumption data, and projected loads of new or anticipated buildings using building designs. A renewable energy resource assessment was conducted that estimated the gross generation potential of solar and wind, constrained to areas that could potentially be developed. Other renewable generation technologies were not considered feasible to meet the loads of the BRB. Demand-side efficiency and fuel switching opportunities were identified that can reduce electrical and gas consumption. These opportunities were not integrated into the load assessment in order to provide a conservative implementation plan, but are recommended to be pursued in order to cost-optimize projects during a feasibility assessment. A strategic vision advisory committee was organized and consulted when iterating on the viability of possible projects. These project phases resulted in finalizing the following three solar PV projects for the near term, which also lay the foundation for a future community-scale or multiple-facility microgrid for added resiliency. Additional solar PV on the hillside south of the Tish-Non Community Center. Solar plus battery storage microgrid at the Pump & Play fuel station. Solar PV at the Casino.

14 SOLAR ENERGY↗

DOE OSTI2023

Energy Transitions Initiative Partnership Project: City and Borough of Sitka, Alaska - Modeling and Controls Assistance and Renewable Energy Resource Assessment [Slides]

This presentation provides a summary of the ETIPP project objectives and findings for Sitka, Alaska, including sizing of wind penetration, dynamic models, and analysis of efficiency of load control, stability and grid control impacts of wind capacity expansions and locations, and wind-hydro control coordination.

17 WIND ENERGY↗

DOE OSTI2021

Wave energy resources assessment for the multi-modal sea state of Hawaii

This paper describes development and validation of a 32-year wave hindcast for Hawaii as part of the wave energy resources assessment for the Exclusive Economic Zones of the US and affiliated territories. The nested model system comprises structured WAVEWATCH III models for the entire globe and the Central North Pacific as well as an unstructured SWAN model around the Hawaiian Islands with wind forcing from the Global Forecast System Reanalysis and its regional downscaling. The development effort follows the standards introduced by the International Electrotechnical Commission (IEC). The hindcast dataset was firstly validated with buoy measurements in terms of the six IEC wave energy resources parameters including the omnidirectional wave power ????, significant wave height ????????0, energy period ????????, spectral width ????0, direction of maximum directionally resolved wave power ?????????? ????????, and directionality coefficient ????. The comparison shows good overall agreement but with greater uncertainties for the spectral parameters ????0, ???????????????????? , and ???? formulated for narrow band spectra. The multimodal sea state in Hawaii always includes a mix of short-period wind seas, long-period swells from the North and South Pacific as well as waves with moderate periods from multiple sources. We partition the hindcast spectra based on the respective period ranges and directions and validate the partitioned spectral energy and parameters with measured values for implementation in wave energy resources assessment. The partitioned wave parameters from the hindcast allow separate seasonal descriptions of wind seas, north and south swells, and moderate-period waves for in-depth understanding of the wave climate and resources in Hawaii.

Li, Ning↗

DOE OSTI2015

Budget Period 2 Summary Report Part 1: Hywind Maine Project

This project was performed under the Work for Others—Funds in Agreement FIA-14-1793 between Statoil and the Alliance for Sustainable Energy, manager and operator of the National Renewable Energy Laboratory (NREL). To support the development of a 6-MW spar-mounted offshore wind turbine, Statoil funded NREL to perform tasks in the following three categories: 1. Design and analysis 2. Wake modeling 3. Concept resource assessment. This summarizes the document reports on the design and analysis work package, which built a FAST [Jonkman & Buhl, 2005] computer model of the Hywind 6-MW floating wind turbine system and uses this tool to evaluate the performance of a set of design load cases (DLCs). The FAST model was also used in Work Package 2: Wake Modeling .

17 WIND ENERGY↗

DOE OSTI2014

Ramping and Uncertainty Prediction Tool - Analysis and Visualization of Wind Generation Impact on Electrical Grid

RUT software is designed for use by the Balancing Authorities to predict and display additional requirements caused by the variability and uncertainty in load and generation. The prediction is made for the next operating hours as well as for the next day. The tool predicts possible deficiencies in generation capability and ramping capability. This deficiency of balancing resources can cause serious risks to power system stability and also impact real-time market energy prices. The tool dynamically and adaptively correlates changing system conditions with the additional balancing needs triggered by the interplay between forecasted and actual load and output of variable resources. The assessment is performed using a specially developed probabilistic algorithm incorporating multiple sources of uncertainty including wind, solar and load forecast errors. The tool evaluates required generation for a worst case scenario, with a user-specified confidence level.

Etingov, Pavel↗

DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

A life cycle assessment of e-hydrogen production using proton-exchange membrane water electrolysis coupled with desalination in Saudi Arabia

Hydrogen, considered a crucial element in the transition towards a sustainable energy future, offers the potential to mitigate greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. Here, this study explores the viability of hydrogen production using proton exchange membrane water electrolysis (PEMWE) as a key driver of decarbonization within the Vision 2030 framework in the Kingdom of Saudi Arabia. A first-of-a-kind life cycle assessment (LCA) of electrolytic hydrogen (e-hydrogen) production using PEMWE in the Kingdom is performed. As the hydrogen will be produced in a freshwater scarce region, the inclusion of water desalination processes adds an important dimension to the assessment, reflecting the local context and resource availability. Two main renewable energy scenarios are assessed: solar energy through photovoltaics (PV) and wind energy through onshore turbines. The global warming potential (GWP) results indicate a GHG emissions reduction of up to 95 % compared to the state-of-the-art steam methane reforming process if the electrolysis process is powered exclusively by renewable electricity. The scenarios powered by solar and wind energy result in 3.66 and 0.76 kg CO 2 eq/kg H 2 , respectively. The metal depletion is assessed to consider the requirement of rare materials, with a 7.19 × 10 −2 kg Cu eq/kg H 2 for the solar scenario and 2.82 × 10 −2 kg Cu eq/kg H 2 for the wind scenario. A contribution analysis reveals that the majority of emissions in both scenarios originate from the electricity used for electrolysis, with the electrolyser itself contributing minimally. The absolute impact of the water desalination process is the same in both scenarios; however, it appears more prominent in the wind-powered case due to the significantly lower overall emissions in that scenario. The findings underscore the importance of renewable energy integration and process optimization in minimizing environmental impacts and advancing the sustainability of e-hydrogen production.

08 HYDROGEN↗