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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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Project Sunlight's aim is to develop a sustainable and scalable approach to overcome the hurdles of deploying solar energy for low-income populations living in multifamily affordable housing (MFAH).
This fact sheet is a revision to the PowerSaver Loan Benefits fact sheet from April 2014. It describes how the U.S. Department of Housing and Urban Development (HUD) PowerSaver Loan Program offers borrowers low-cost FHA-insured loans to make energy-saving improvements to their homes.
Master Limited Partnerships (MLPs) and Real Estate Investment Trusts (REITs)—both well-established investment structures—should be opened up to renewable energy investment. MLPs and, more recently, REITs have a proven track record for promoting oil, gas, and other traditional energy sources. When extended to renewable energy projects these tools will help promote growth, move renewables closer to subsidy independence, and vastly broaden the base of investors in America’s energy economy. The extension of MLPs and REITs to renewables enjoys significant support from the investment and clean energy communities. In addition, MLPs for renewables also enjoy bipartisan political backing in Congress.
Solar and solar and battery storage deployment is under-utilized by businesses in low-income and disadvantaged communities, in large part due to system costs and limited or complicated financial options. This document summarizes existing financial tools, discusses important financial barriers, and identifies emerging programs and opportunities to address these barriers.
We consider stochastic control problems with jump-diffusion processes and formulate an algorithm which produces, starting from a given admissible control {pi}, a new control with a better value. If no improvement is possible, then {pi} is optimal. Such an algorithm is well-known for discrete-time Markov Decision Problems under the name Howard's policy improvement algorithm. The idea can be traced back to Bellman. Here we show with the help of martingale techniques that such an algorithm can also be formulated for stochastic control problems with jump-diffusion processes. As an application we derive some interesting results in financial portfolio optimization.
Reliable data is needed to understand financial relationships in the power sector. However, relevant data acquisition and visualization can be a challenge due to the fragmented nature of the power sector. The Energy Policy and Systems Analysis office of the U.S. Department of Energy led a team of data scientists at Oak Ridge National Lab to collect revenue information from a variety of sources.
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Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. Here, we present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modelling, optimization and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle. Quantum computers are expected to surpass classical computers and transform industries. This Review focuses on quantum computing for financial applications and provides a summary for physicists on potential advantages and limitations of quantum techniques, as well as challenges that physicists could help tackle.
The U.S. Department of Energy and the U.S. Department of Transportation have published a guide to highlight examples of federal support and technical assistance for plug-in electric vehicles (PEVs) and charging stations. The guide provides a description of each opportunity and a point of contact to assist those interested in advancing PEV technology. The Department of Energy’s Alternative Fuels Data Center provides a comprehensive database of federal and state programs that support plug-in electric vehicles and infrastructure.
This module will help viewers understand the PV financial incentives and policies that apply to on-site PV. It is the fourth training in a series intended to help municipal staff procure solar PV for their land and buildings. A recording of the training is also available.
This issue brief is for local governments that are well-positioned to participate in a commercial property assessed clean energy (C-PACE) program but are looking to inform a decision about whether to join or create a program. This resource addresses two specific barriers these local governments may face regarding C-PACE programs: (1) uncertainty about the likelihood of tax foreclosure on properties in default of C-PACE payments and the risks local governments bear, and (2) uncertainty about the staff labor commitment associated with administering the program, including the execution of the special tax assessment process.
This study investigates the issues and challenges surrounding energy storage project and portfolio valuation and provide insights in to improving visibility into the process for developers, capital providers, and customers so they can make more informed choices. Energy storage project valuation methodology is typical of power sector projects through evaluating various revenue and cost assumptions in a project economic model. The difference is that energy storage projects have many more design and operational variables to incorporate, and the governing market rules that control these variables are still evolving. This makes project valuation for energy storage more difficult. As the number of operating projects grow, portfolios of these projects are being developed, garnering the interest of larger investors. Valuation challenges of these portfolios can be even more challenging as market role and geographical diversity can actually exacerbate the variability, not mitigate it. By proposing additional visibility of key factors and drivers for industry participants, the US DOE can reduce investment risk, expanding both the number and types of investors, plus helping emerging energy storage technology into sustained commercialization.
Individual heat substations (IHSs) are important for improving the district heating (DH) infrastructure in Ukraine. The purpose of this memo is to discuss the position of key stakeholders on IHS deployment and developed amendments to the Law of Ukraine “On Heat Supply.” This memo also briefly discusses ways to bring funding for mass-scale IHS installation by DH companies.
This report documents the final technical accomplishments and outcomes of Rocky Mountain Institute’s project under the U.S. Department of Energy (DOE) Award DE-EE0009064. The project aimed to develop, validate, and scale whole building retrofit solutions for multifamily buildings, including two configurations of Integrated Mechanical System Pods (IMSP-C and IMSP-U), in alignment with DOE Advanced Building Construction (ABC) initiative's decarbonization and energy efficiency goals. While the project made significant progress in Budget Period 1 (Phase 1) and throughout Budget Period 2 (Phase 2), activities were discontinued as of March 26, 2025, following a Stop Work Order issued by DOE. As such, this report reflects all completed work through that date. The project did not enter Budget Periods 3 and 4 (Phase 2), and demonstration site implementation, field M&V, and final commercialization execution were not conducted.
Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.
Research project on federal and industrial patent acquisition and economic implications - NASA PROGRAM