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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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DOE OSTI2020

A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Community Solar Policy Screening Workbook for Multifamily Affordable Housing Building Portfolios

This workbook provides a framework to help Multifamily Affordable Housing (MFAH) housing providers evaluate state-level policy landscapes as they relate to MFAH community solar development. By exploring which programs and policies aid in the technical and economic feasibility of MFAH solar projects, building owners can gain an understanding of where in their portfolios to begin developing solar. This workbook also walks through the steps necessary for MFAH building owners to assess their portfolios using this framework. It was developed by the National Renewable Energy Laboratory for the National Community Solar Partnership (NCSP) Multifamily Affordable Housing Collaborative. Learn more about the NCSP and the Collaborative at https://www.energy.gov/communitysolar/community-solar.

14 SOLAR ENERGY↗
DOE OSTI2021

Optimal energy storage portfolio for high and ultrahigh carbon-free and renewable power systems

Achieving 100% carbon-free or renewable power systems can be facilitated by the deployment of energy storage technologies at all timescales, including short-duration, long-duration, and seasonal scales; however, most current literature focuses on cost assessments of energy storage for a given timescale or type of technology. In this work, we use an optimization framework with high spatial and temporal resolution to simultaneously assess the variable renewable power deployment and the optimal storage portfolio for seven independent system operators in the United States. Results indicate that achieving high (75–90%) and ultrahigh (>90%) energy mixes requires combining several flexibility options, including renewable curtailment, short-duration, long-duration, and seasonal storage. For instance, carbon-free and renewable energy mix targets of up to 80% are achieved with economic curtailment and a combination of short- and long-duration energy storage for the performance and cost assumptions used. After that, there is a point between 80% and 95% where seasonal storage becomes cost-competitive, depending on the specific power system. Moreover, our results indicate that storage-to-storage operation—one storage device used to charge another storage device—and the decoupling of charging and discharging storage power capacity are cost-effective options for the integration of high and ultrahigh shares of carbon-free or renewable power sources. Additionally, the results from this study show that an 85% carbon-free or renewable energy mix can be achieved at a cost of avoided CO 2 emissions of US$66.0 per tonne or less, regardless of the power system.

25 ENERGY STORAGE↗
DOE OSTI2022

Probabilistic R&D Portfolio Management Applied to Energy Storage

Here this paper proposes a methodology to analyze the potential of several alternative technologies in advancing a research and development portfolio. The methodology considers technology maturation, use case requirements, performance goals, cost projections, and existing research and development (R&D) efforts to predict the impact of various R&D investment tracks across several energy storage technologies. Technology maturation and cost projections are derived from a probabilistic Monte Carlo simulation, yielding results that incorporate uncertainty in modeling the impact of future R&D. This framework allows the researcher to perform a robust sensitivity analysis to uncover critical pathways and areas where investment will have a high impact.

25 ENERGY STORAGE↗
DOE OSTI2023

Simulating energy performance of buildings: a study using eQUEST and Energy Star ® portfolio manager

Commercial buildings consume significant energy in the United States and exhibit high potential for energy use reduction through retrofits. Benchmarking and energy simulation are well established tools in the industry to identify potential improvements and measure performance. Analysis to identify most sensitive retrofit parameters to energy performance can optimize investment and available energy savings. Presented study demonstrates methodology using a static model to determine sensitivity of building design and retrofit parameters with respect to energy performance. Calibrated simulation energy models (eQUEST) of two distribution centers (A, B) are presented. A fractional factorial analysis is conducted on retrofit parameters of efficiency measures targeting the highest energy consumers, and the results are benchmarked using Energy Star® Portfolio Manager. A custom Microsoft Excel® based simulation model is created to simulate occupancy levels, lighting, plug loads, and other equipment used in various spaces throughout the day. For Building A, efficient lighting was the most influential parameter for energy savings, carbon savings and benchmarking score; whereas, for Building B, HVAC efficiency was most influential for energy and demand controlled ventilation and economizers was most influential for benchmarking score. While retrofit projects can save energy and carbon emissions, variation in source-site ratios and state grid emissions, benchmarking scores may not always reflect equivalent improvement. State grid emissions factors, natural gas composition are difficult to model and hence not considered in this study. In conclusion, the synergistic analysis presented, emphasizes the importance of benchmarking and efficiency retrofits in promoting sustainable building practices to reduce energy consumption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

2022 Combined Heat and Power/District Energy System Portfolio Meeting (Summary Report)

The U.S. Department of Energy’s (DOE’s) Advanced Manufacturing Office held the 2022 Combined Heat and Power & District Energy System Portfolio Meeting on June 7–9, 2022. The meeting brought together approximately 50 researchers from industry, national laboratories, and research institutes. The purpose of the meeting was for DOE to understand the performance of the projects being sponsored through the CHP program and the status of the technology development, which informs future program planning. This report summarizes the presentations that took place at this event.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Framework for Greenhouse Gas Emissions Reduction Planning: Building Portfolios

The goal of this framework for greenhouse gas (GHG) emissions reduction planning (ERP) is to provide guidance to organizations seeking to reduce GHG emissions for their building portfolios and vehicle fleets. The process described in this framework helps organizations develop an actionable plan that prioritizes emissions reduction measures, identifies solutions, and lays out a phased pathway to achieve deep emissions reductions. The plan aims to result in the achievement of Scope 1 and 2 GHG emissions reduction targets, in alignment with goals set within the U.S. Department of Energy’s (DOE’s) Better Climate Challenge, as well as longer term emissions reduction goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Practical Approaches to Embed Energy and Environmental Justice into DOE Technology Portfolios (August 2021–August 2024)

This document serves as a resource to science and technology offices within DOE seeking to incorporate energy and environmental justice (EEJ) into their programs and portfolios. It provides background information on EEJ principles, how those principles apply from early-stage technology development through deployment and commercialization. Through providing foundational information about justice and equity, and how they apply to technology development and program facilitation, this document provides information to take tangible and interim steps towards these goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Achieving an 80% Renewable Portfolio in Alaska's Railbelt: Cost Analysis

This study examines the system-level costs and benefits of increased renewable energy deployment in the Railbelt grid, in the context of a proposed 80% renewable portfolio standard (RPS). This work studies the period from 2024 to 2040 and uses a model that simulates the planning, evolution, and operation of the power system to identify the mix of resources that maintains system reliability at the lowest electricity system cost over the period of analysis. The model tracks several reliability metrics, including the ability to serve demand during all hours of the year, even when normal power system failures occur. The model includes several measures (and associated costs) to address the variable output of renewable resources, including additional operating reserves, fuel storage, cycling of fossil plants, and additional equipment needed to maintain system stability. The Reference (least-cost) scenario results in substantial deployment of renewable energy and cost savings, reaching about 76% of Railbelt generation derived from renewables in 2040. Annual savings average about $105 M/year from 2030 to 2040. About 50% of this generation is from wind by 2040. Enforcing an 80% RPS results in about a 2% cumulative reduction in net savings. Demand is met in all scenarios, relying heavily on use of existing hydropower and fossil-fueled generators during periods of low renewable output. Meeting the increase in variability will require substantial changes in how the system is operated, with inverter-based resources providing nearly 100% of electricity during some periods.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

GeoRePORT Case Study Examples: Reporting Using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT)

The Geothermal Research Portfolio Optimization & Reporting Technique (GeoRePORT) was developed with funding from the United States Department of Energy's Geothermal Technologies Office (GTO) to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and non-scientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and, (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g. temperature, permeability, permitting), a site's project grade and readiness are evaluated on twelve independent attributes pertaining to geological, technical, or socio-economic feasibility. In this paper, we present case studies illustrating how GeoRePORT can be used to compare geological, technical, and socio-economic attributes between geothermal systems. The consistent and objective assessment protocols used in GeoRePORT allow for comparison of project attributes across unique locations and geological settings. GeoRePORT case studies outline the geological, socio-economic and technical features of four individual geothermal sites: Coso, Chena, Dixie Valley, and White Sands Missile Range. The case studies presented herein illustrate the usefulness of GeoRePORT in evaluating project risk/return, identifying gaps in reported data, evaluating R&D impact, and gathering insights on successes/failures as applicable to future projects.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2025

Redefining the product portfolio of oilcane bagasse biorefinery: Recovering natural colorants, vegetative lipids and sugars

Bioenergy crops have been known for their ability to produce biofuels and bioproducts. In this study, the product portfolio of recently developed transgenic sugarcane (oilcane) bagasse has been redefined for recovering natural pigments (anthocyanins), sugars, and vegetative lipids. The total anthocyanin content in oilcane bagasse has been estimated as 92.9 ± 18.9 µg/g of dried bagasse with cyanidin-3-glucoside (13.5 ± 18.9 µg per g of dried bagasse) as the most prominent anthocyanin present. More than 85 % (w/w) of the total anthocyanins were recovered from oilcane bagasse at a pretreatment temperature of 150 °C for 15 min. These conditions for the hydrothermal pretreatment also led to a 2-fold increase in the glucose yield upon the enzymatic saccharification of the pretreated bagasse. Further, a 1.5-fold enrichment of the vegetative lipids was demonstrated in the pretreated residue. Re-defining green biorefineries with multiple high-value products in a zero-waste approach is the need of the hour for attaining sustainability.

09 BIOMASS FUELS↗
DOE OSTI2025

Data for Redefining the Product Portfolio of Oilcane Bagasse Biorefinery: Recovering Natural Colorants, Vegetative Lipids and Sugars

:Bioenergy crops have been known for their ability to produce biofuels and bioproducts. In this study, the product portfolio of recently developed transgenic sugarcane (oilcane) bagasse has been redefined for recovering natural pigments (anthocyanins), sugars, and vegetative lipids. The total anthocyanin content in oilcane bagasse has been estimated as 92.9 ± 18.9 µg/g of dried bagasse with cyanidin-3-glucoside (13.5 ± 18.9 µg per g of dried bagasse) as the most prominent anthocyanin present. More than 85 % (w/w) of the total anthocyanins were recovered from oilcane bagasse at a pretreatment temperature of 150 °C for 15 min. These conditions for the hydrothermal pretreatment also led to a 2-fold increase in the glucose yield upon the enzymatic saccharification of the pretreated bagasse. Further, a 1.5-fold enrichment of the vegetative lipids was demonstrated in the pretreated residue. Re-defining green biorefineries with multiple high-value products in a zero-waste approach is the need of the hour for attaining sustainability.

Biomass Analytics↗
DOE OSTI2025

A Portfolio Approach to Massively Parallel Bayesian Optimization

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Behind-the-Meter Solar Accounting in Renewable Portfolio Standards

If a behind-the-meter solar photovoltaic (BTM PV) system is adopted, how does that influence the total amount of renewable electricity in its state in the long run (i.e., after the existence of the generator is reflected in the relevant utility's generation mix)? Would we expect the total amount of renewable generation to increase on a 1:1 basis with the BTM PV's generation? Or could it be something more, or something less? We show in this paper that the answer can depend on two key elements of how BTM PV is accounted for in a state’s renewable portfolio standard (RPS): (1) whether renewable energy certificates (RECs) from BTM PV can be used for RPS compliance, and (2) whether load served by generation from BTM PV counts as load covered by the RPS. These two elements combine into four possible accounting options, and we characterize the implications of each under the simplifying assumptions that the RPS is binding and the BTM PV RECs are used for compliance when allowed. For example, if load served by BTM PV generation counts toward the RPS load and BTM PV RECs cannot be used for compliance, the presence of BTM does not change the amount of RECs that the utility is required to retire, and yet additional RECs will be retired by the BTM PV owner - therefore, the total amount of renewable generation would increase on a 1:1 basis with the BTM PV generation. In contrast, under a common RPS design in which BTM PV RECs can be used for compliance and the load served by BTM PV generation is not covered by the RPS, the presence of BTM PV and transfer of RECs for compliance can actually decrease the total amount of renewable generation in the state, relative to a situation in which there is no BTM PV.

14 SOLAR ENERGY↗
DOE OSTI2021

U.S. Renewables Portfolio Standards 2021 Status Update: Early Release

Berkeley Lab’s annual status report on U.S. renewables portfolio standards (RPS) provides an overview of key trends associated with U.S. state RPS policies. The report, published in slide-deck form, describes recent legislative revisions, key policy design features, compliance with interim targets, past and projected impacts on renewables development, and compliance costs. The 2021 Early Release, published in lieu of a 2020 edition, presents historical data through year-end 2019, with some limited results for the year 2020. Key trends from this edition of the report include the following: Evolution of state RPS programs: States continue to refine and revise their RPS policies. Among other significant changes since the start of 2019, eight states enacted higher RPS targets or created new clean-energy/zero-carbon targets (AZ, DC, MD, NM, NV, VA, WA), in most cases setting targets equal to at least 50% of retail sales. Historical impacts on renewables development: Roughly half of all growth in U.S. renewable electricity (RE) generation and capacity since 2000 is associated with state RPS requirements, though that percentage has declined in recent years, representing 23% of all U.S. RE capacity additions in 2019. However, within particular regions-namely, the Northeast and Mid-Atlantic-RPS policies continue to serve a central role in motivating RE growth. Future RPS demand and incremental needs: RPS demand growth through 2030 will require roughly 90 GW of new RE capacity and will require total U.S. non-hydro RE generation to reach 17% of electricity sales (compared to 12% in 2019). Relative to EIA projections, this amounts to roughly one-third of projected RE growth over the next decade. RPS target achievement to-date: States have generally met their interim RPS targets in recent years, with only a few exceptions reflecting unique, state-specific issues. REC pricing trends: Prices for NEPOOL Class I RECs rose steeply over 2019, reaching $40/MWh and remaining at roughly that level over 2020. PJM Tier I REC prices continued to rise at a modest pace over the course of 2020, reaching $10/MWh by year-end. Prices for solar RECs remained relatively stable over 2020, and continue to exhibit wide variation across states, with the highest prices ($200-450/MWh) in NJ, MA, and DC.RPS compliance costs and cost caps: RPS compliance costs in 2019 averaged roughly 2.6% of retail electricity bills in RPS states, compared to 2.3% in 2018, with costs in most states ranging from 0.5% to 4.5% of retail electricity bills.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Renewable Portfolio Standard Assessment for Alaska's Railbelt

This memorandum for the Governor of Alaska evaluates the technical feasibility of reaching 80% renewable electricity by 2040 in the Railbelt corridor of Alaska, which represents 75% of Alaska's electric load. This analysis was developed to inform renewable energy portfolio standard (RPS) legislation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗