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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Microgrid Analysis Tools Summary

The over-arching goal of the Alaska Microgrid Partnership is to reduce the use of total imported fuel into communities to secure all energy services by at least 50% in Alaska's remote microgrids without increasing system life cycle costs while also improving overall system reliability, security, and resilience. One goal of the Alaska Microgrid Partnership is to investigate whether a combination of energy efficiency and high-contribution (from renewable energy) power systems can reduce total imported energy usage by 50% while reducing life cycle costs and improving reliability and resiliency. This presentation provides an overview of the following four renewable energy optimization tools. Information is from respective tool websites, tool developers, and author experience. Distributed Energy Resources Customer Adoption Model (DER-CAM) Microgrid Design Toolkit (MDT) Renewable Energy Optimization (REopt) Tool Hybrid Optimization Model for Electric Renewables (HOMER).

17 WIND ENERGY↗
DOE OSTI2025

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Renewable Energy Laboratory's (NREL) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Laboratory of the Rockies's (NLR) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

REopt Screenings Catalyze Development of Hundreds of Megawatts of Renewable Energy for Federal Agencies

The Federal Energy Management Program (FEMP) offers renewable energy project assistance to federal agencies, which often begins with a desktop screening to develop a prioritized portfolio of project opportunities. FEMP uses the National Renewable Energy Laboratory's REopt energy planning platform to screen potential renewable energy opportunities at a single site or across a range of sites. REopt helps organizations prioritize the most economi­cally and technically viable projects for further study and identifies the size and mix of technologies that meet the orga­nization's goals at minimum cost, along with the optimal operating strategies.

FEMP↗
DOE OSTI2018

NREL Tools Help Identify and Evaluate Renewable Energy Projects

NREL offers no-cost, publicly available web tools that can be used throughout the development of behind-the-meter renewable energy projects. These tools help gauge initial project potential, optimize system sizing, and refine project economics. This flyer compares four tools that can be used at various stages of project development: Economic Site Analysis (ESA) Tool, PVWatts Calculator, REopt (TM) Lite, and System Advisor Model (SAM).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Summary: Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

The Clean Energy Investment Accelerator conducted a case study analysis of battery energy storage system (BESS) feasibility for an industrial park in Vietnam using the National Renewable Energy Laboratory's (NREL's) REopt platform (a distributed energy modeling and optimization tool) to evaluate how BESS may reduce electricity costs, increase utilization of onsite renewable energy generation, and improve resilience to utility grid outages. This presentation summarizes the analysis and key takeaways.

batteries↗
DOE OSTI2022

Loads assessment of a fixed-bottom offshore wind farm with wake steering

Wake steering via deliberate yaw offset is an emerging wind farm control technique that has the potential to mitigate wake losses and further increase wind farm energy yield. The loads impact of this technique has been studied, but there is limited insight into wind-farm-wide impacts of wake steering. Understanding such impacts is crucial to determining the feasibility of using wake steering in commercial wind farms. To that end, this work investigates the impacts of wake steering on the loads of all turbine components across all turbines in a wind farm operating under a broad set of inflow conditions, including inflow velocity, shear exponent, turbulence class, and inflow angle. This was done by performing FAST.Farm simulations of a 12-turbine wind farm array, excerpted from a larger hypothetical wind farm. The International Energy Agency Wind 15-MW reference wind turbine was modeled atop a monopile substructure, an open-source model that closely approximates the properties of similar commercial options. Wake steering was included via yaw offsets that were computed using an offline optimization with the National Renewable Energy Laboratory tool FLORIS. For each inflow case, the 12-turbine array was simulated with and without wake steering. Results were compared in terms of time-averaged means, standard deviations, ultimate loads, and damage-equivalent loads. The findings show that because wake steering is generally applied at rated wind speeds and below, it is unlikely to drive ultimate loads. For fatigue loads, wake steering does increase the overall fatigue accumulation for some load channels, such as blade-root and shaft bending. This is to be expected when overall power yield increases but may cause the damage accumulation to be more uniform throughout the array. The significance of the added fatigue loading is dependent on how frequent wake steering is utilized in the overall set of inflow conditions across the wind rose.

17 WIND ENERGY↗
DOE OSTI2022

Integration of Clean Energy into Oil Field Operations

Integrating clean energy into oil and gas operations could reduce emissions and maximize higher-value use of produced hydrocarbons. In this published study, analysts from the Joint Institute for Strategic Energy Analysis (JISEA) and the National Renewable Energy Laboratory (NREL) evaluated clean power technologies for an oil field in the Delaware Basin using NREL’s Renewable Energy Integration and Optimization, or REopt, tool. The analysts evaluated different configurations of distributed energy resources based on the technologies available and the load they could satisfy, available land, and hypothetical carbon pricing. The analysis is part of a collaborative program with industry to understand site-specific energy consumption and prices in the oil and gas supply chain and determine under what conditions clean energy options are economically attractive. This work was sponsored by a consortium including ConocoPhillips, Baker Hughes, Extraction Oil & Gas, Kinder Morgan, and the Interstate Natural Gas Association of America Foundation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A Detailed Wind Turbine Blade Cost Model

This technical report describes a detailed blade cost model for wind turbine blades in the range of 30 to 100 meters in length. The model estimates the bill of materials, the number of labor hours and the cycle time, and the costs related to direct labor, overhead, buildings, tooling, equipment, maintenance, and capital. The model applies to multimegawatt wind turbine blades manufactured via vacuum-assisted resin transfer molding, which is the most commonly adopted manufacturing method for modern wind turbine blades. The model is implemented both in a large Excel file and in Python. The latter is freely available in the repository of the Wind-Plant Integrated System Design and Engineering Model (WISDEM[R]) github.com/WISDEM. WISDEM is a multidisciplinary analysis and optimization design framework developed at the National Renewable Energy Laboratory. This blade cost model represents a valuable tool to run design optimization studies for wind turbines. In this report, the model is first presented with its approach and assumptions and then computes the costs of three blades, namely the 33-meter-long Wind Partnership for Advanced Component Technologies (WindPACT) study blade, the 63-meter-long International Energy Agency (IEA) Wind Task 37 land-based reference wind turbine blade, and the 100-meter-long SNL-100-03 blade developed at Sandia National Laboratories.

17 WIND ENERGY↗
DOE OSTI2022

National Community Solar Partnership Multifamily Affordable Housing Portfolio Screening Approach

Community solar offers multiple benefits to providers and residents of multifamily affordable housing (MFAH), including access to affordable renewable energy for low- and moderate-income households, tax credits and other financial benefits for property owners, and increased community resilience when paired with energy storage. Even so, MFAH providers interested in realizing these benefits may need support to identify where to start or how to expand and accelerate solar deployment across their property portfolios once they have completed their first few installations. This fact sheet describes a portfolio screening approach for MFAH providers to evaluate their multi-state building portfolios for community solar project viability. The approach was developed by the National Renewable Energy Laboratory as part of its support for the National Community Solar Partnership (NCSP) MFAH Collaborative. By utilizing NREL's companion Policy Screening Workbook in conjunction with the web-based tools PVWatts® Calculator and REopt™ (Renewable Energy Integration and Optimization), MFAH providers can take a data-informed approach to identifying and prioritizing buildings in their portfolios that are most amenable to community solar development.

community solar↗
DOE OSTI2017

Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation

Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast of the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. In the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage.

25 ENERGY STORAGE↗
DOE OSTI2023

Lessons Learned From Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

Through the Clean Energy Investment Accelerator (CEIA), engineers from the United States (U.S.) National Renewable Energy Laboratory (NREL) conducted a case study analysis evaluating the techno-economic feasibility of battery energy storage systems (BESS) at an industrial park in Vietnam. The analysis uses NREL's REopt platform, a distributed energy modeling and optimization tool, to identify the cost-optimal system sizing for BESS, in conjunction with onsite renewables such as solar photovoltaics (PV), to reduce electricity costs, increase onsite renewable energy generation utilization, and improve resilience to grid outages.

batteries↗
DOE OSTI2016

Scenario analysis for techno-economic model development of U.S. offshore wind support structures

Challenging bathymetry and soil conditions of future US offshore wind power plants might promote the use of multimember, fixed-bottom structures (or 'jackets') in place of monopiles. Support structures affect costs associated with the balance of system and operation and maintenance. Understanding the link between these costs and the main environmental design drivers is crucial in the quest for a lower levelized cost of energy, and it is the main rationale for this work. Actual cost and engineering data are still scarce; hence, we evaluated a simplified engineering approach to tie key site and turbine parameters (e.g. water depth, wave height, tower-head mass, hub height and generator rating) to the overall support weight. A jacket-and-tower sizing tool, part of the National Renewable Energy Laboratory's system engineering software suite, was utilized to achieve mass-optimized support structures for 81 different configurations. This tool set provides preliminary sizing of all jacket components. Results showed reasonable agreement with the available industry data, and that the jacket mass is mainly driven by water depth, but hub height and tower-head mass become more influential at greater turbine ratings. A larger sensitivity of the structural mass to wave height and target eigenfrequency was observed for the deepest water conditions (>40 m). Thus, techno-economic analyses using this model should be based on accurate estimates of actual metocean conditions and turbine parameters especially for deep waters. Finally, the relationships derived from this study will inform National Renewable Energy Laboratory's offshore balance of system cost model, and they will be used to evaluate the impact of changes in technology on offshore wind lower levelized cost of energy.

17 WIND ENERGY↗
DOE OSTI2019

Assessment of Existing Capabilities and Future Needs for Designing Networked Microgrids

This is a review of existing microgrid design tool capabilities, such as the Microgrid Design Tool (MDT), LANL PNNL NRECA Optimal Resilience Model (LPNORM), Distributed Energy Resource-Customer Adoption Model (DER-CAM), Renewable Energy Optimization (REopt), and the Hybrid Optimization Model for Multiple Energy Resources (HOMER). Additionally, other simulation and analysis tools which may provide fundamental support will be examined. These will include GridLAB-DTM, OpenDSS, and the hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). Their applicability to networked microgrid operations will be evaluated, and strengths and gaps of existing tools will be identified. This review will help to determine which elements of the proposed optimal design and operations (OD&D) tool should be formulated from first principles, and which elements should be integrated from past DOE investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Optimization and Comparison of Modern Offshore Wind Turbine Generators Using GeneratorSE 2.0

As the offshore wind industry keeps growing at a rapid pace, developers are bracing themselves for a huge demand in critical rare earth metals which will threaten an already vulnerable supply chain. The wind energy industry is addressing this problem by investing in modern generator technologies that employ magnets with reduced rare earth content and high-field magnets enabled by rare-earth-free superconductors. In this paper we introduce the National Renewable Energy Laboratory's newly advanced GeneratorSE 2.0, which is a design and optimization tool that was developed to investigate the feasibility of such modern generators. Two direct-drive generator topologies with different magnet materials and mounting arrangements are investigated: an outer-rotor, V-shaped interior permanent magnet generator, and an inner-rotor normally conducting armature, paired with a low-temperature superconducting field with race-track coils. These technologies were evaluated for a range of power ratings between 15 and 25 MW, which represent the next generation of offshore wind turbines for both fixed-bottom and floating applications. The analyses indicate a new trend favoring the low-temperature superconducting technology for the direct-drive system.

direct-drive generators↗
DOE OSTI2020

REopt Lite Overview & Training Exercise

This training exercise provides users with an introduction to and hands-on, interactive exploration of REopt Lite's capabilities. REopt Lite is a free, publicly available techno-economic optimization web tool for distributed energy systems, developed at the National Renewable Energy Laboratory (NREL). REopt Lite helps organizations evaluate the economic viability of grid-connected solar photovoltaics (PV), wind turbines, and battery storage; identify system sizes and battery dispatch strategies to minimize energy costs; and estimate how long a system can sustain critical load during a grid outage. The model is formulated as a mixed-integer linear program based in an underlying application programming interface (API) that is also free and publicly available. This training activity is structured as a group exercise. Participants split into eight groups and each group is assigned a different hypothetical site to model and assess the opportunity for solar PV + battery storage. Groups work together to develop results for their site and then re-convene to compare and discuss results, inputs/drivers of the analysis, and other factors impacting the decision-making process for behind-the-meter solar PV and battery storage.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

REopt Lite Web Tool Evaluates Photovoltaics and Battery Storage

Building on the success of the REopt renewable energy integration and optimization platform, NREL has developed a free, publicly available web version of REopt called REopt Lite. REopt Lite evaluates the economics of grid-connected photovoltaics (PV) and battery storage at a site. It allows building owners to identify the system sizes and battery dispatch strategy that minimize their life cycle cost of energy. This web tool also estimates the amount of time a PV and storage system can sustain the site's critical load during a grid outage.

24 POWER TRANSMISSION AND DISTRIBUTION↗