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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 OSTI2016

The EPSA Project Finance Mapping Tool

The Energy Policy and Systems Analysis Office of DOE has requested a tool to compare the impact of various Federal policies on the financial viability of generation resources across the country. Policy options could include production tax credits, investment tax credits, solar renewable energy credits, tax abatement, accelerated depreciation, tax-free loans, and others. The tool would model the finances of projects in all fifty states, and possibly other geographic units like utility service territories and RTO/ISO territories. The tool would consider the facility s cost, financing, production, and revenues under different capital and market structures to determine things like levelized cost of energy, return on equity, and cost impacts on others (e.g., load-serving entities, society.) The tool would compare the cost and value of the facility to the local regional alternatives to determine how and where policy levers may provide sufficient incremental value to motivate investment. The results will be displayed through a purpose-built visualization that maps geographic variations and shows associated figures and tables.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Enhancing Clean Energy Innovation Ecosystem Discovery Tool

During March 2016, the DOE’s Office of Energy Policy and Systems Analysis (EPSA) sought a methodology to identify and quantify the strength of the existing clean energy innovation ecosystems (IE) in the U.S. and their characteristics. Oak Ridge National Lab (ORNL) did a pilot study (Phase 1) and demonstrated the feasibility of an application comprised of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The data-collection, ingest and analytics pipelines were combined with an advanced user interface, together known as the Ecosystem Discovery Tool, to enhance DOE’s understanding of existing geographic innovation clusters. During Phase 2 of this project, this tool has been further enhanced by integrating new data sets and through backend software architecture changes, validation and bug fixing, making it a much more robust and powerful application. The tool’s user interface is a lot more intuitive, which enables a user to visualize the IE rankings for various geographic regions(CBSA, state level) for different clean energy technologies and seek automated insights. ORNL also created a DOE-specific dashboard that allows the user to visualize and analyze federal funding from various offices including Energy Efficiency and Renewable Energy(EERE), Office of Electricity(OE), Fossil Energy(FE), and Nuclear Energy(NE). During Phase 1 of this project, EPSA defined a clean energy innovation ecosystem as the overlap of five Ecosystem Components: 1) nascent clean energy Indicators, 2) investors, 3) enabling environment, 4) networking assets and 5) large companies. EPSA and ORNL worked together to collect data for each component: 1) small and medium companies, ARPA-E awardees, SBIR awardees, patents, publications, ERCs for Nascent Clean Energy Indicators; 2) qualified investors; 3) the Clean Edge Policy Index/DSIRE for the Enabling Environment; 4) universities, national laboratories, ERCs and incubators for Networking Assets; and 5) large companies and a subset of the Russell 1000 list for Large Companies. The ORNL team created a visual tool based on Tableau that integrated ecosystem component data to score, rank and map IEs. The tool was created with the flexibility to allow the user to choose the weights of each of the five ecosystem components and the subcomponents. This flexibility allows the user to visualize different subsets and to use the underlying data for different types of analysis. During Phase 2 of this project, the backend database and computation process have undergone major changes to provide a much more refined user-interface and added new features into the scoring algorithm. This phase also involved addition of newer databases such as a list of Energy Research Centers (ERC) ,DSIRE, and funding data from EERE, FE, NE, and OE to provide more granular information and more accurate/better quantification.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

Global Nuclear Markets – Market Arrangements and Service Agreements

The U.S. Department of Energy’s Office of Energy Policy and Systems Analysis (EPSA) requested an assessment of global nuclear markets, including the structure of nuclear companies in different countries and the partnerships between reactor vendors and buyers. EPSA was interested in obtaining information on the competitive context of international sales of reactors and fuel services. The Idaho National Laboratory responded to this request with a plan for information gathering and assessment of global markets in several phases. The first phase researched global sources and developed a collection of information to assist in analyses of global market status and trends in services provided in conversion, enrichment, reactor design, construction and operation, and discharged fuel storage and reprocessing. This report summarized this first phase, including analysis conclusions about current global markets. Additional phases will address specific topics that are of particular interest to EPSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

Ecosystem discovery: Measuring clean energy innovation ecosystems through knowledge discovery and mapping techniques

Not Available

Lin, Jessica (ORCID:0000000280407251)↗

DOE OSTI2016

Energy Finance Data Warehouse Manual

The Office of Energy Policy and Systems Analysis s finance team (EPSA-50) requires a suite of automated applications that can extract specific data from a flexible data warehouse (where datasets characterizing energy-related finance, economics and markets are maintained and integrated), perform relevant operations and creatively visualize them to provide a better understanding of what policy options affect various operators/sectors of the electricity system. In addition, the underlying data warehouse should be structured in the most effective and efficient way so that it can become increasingly valuable over time. This report describes the Energy Finance Data Warehouse (EFDW) framework that has been developed to accomplish the defined requirement above. We also specifically dive into the Sankey generator use-case scenario to explain the components of the EFDW framework and their roles. An excel-based data warehouse was used in the creation of the energy finance Sankey diagram and other detailed data finance visualizations to support energy policy analysis. The framework also captures the methodology, calculations and estimations analysts used for the calculation as well as relevant sources so newer analysts can build on work done previously.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Evaluating Proposed Investments in Power System Reliability and Resilience: Preliminary Results from Interviews with Public Utility Commission Staff

Policymakers and regulatory agencies are expressing renewed interest in the reliability and resilience of the U.S. electric power system in large part due to growing recognition of the challenges posed by climate change, extreme weather events, and other emerging threats. Unfortunately, there has been little or no consolidated information in the public domain describing how public utility/service commission (PUC) staff evaluate the economics of proposed investments in the resilience of the power system. Having more consolidated information would give policymakers a better understanding of how different state regulatory entities across the U.S. make economic decisions pertaining to reliability/resiliency. To help address this, Lawrence Berkeley National Laboratory (LBNL) was tasked by the U.S. Department of Energy Office of Energy Policy and Systems Analysis (EPSA) to conduct an initial set of interviews with PUC staff to learn more about how proposed utility investments in reliability/resilience are being evaluated from an economics perspective. LBNL conducted structured interviews in late May-early June 2016 with staff from the following PUCs: Washington D.C. (DCPSC), Florida (FPSC), and California (CPUC).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Electricity End Uses, Energy Efficiency, and Distributed Energy Resources Baseline

This report was developed by a team of analysts at Lawrence Berkeley National Laboratory, with Argonne National Laboratory contributing the transportation section, and is a DOE EPSA product and part of a series of “baseline” reports intended to inform the second installment of the Quadrennial Energy Review (QER 1.2). QER 1.2 provides a comprehensive review of the nation’s electricity system and cover the current state and key trends related to the electricity system, including generation, transmission, distribution, grid operations and planning, and end use. The baseline reports provide an overview of elements of the electricity system. This report focuses on end uses, electricity consumption, electric energy efficiency, distributed energy resources (DERs) (such as demand response, distributed generation, and distributed storage), and evaluation, measurement, and verification (EM&V) methods for energy efficiency and DERs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Evaluation of Factors that Influence Residential Solar Panel Installations

Though rooftop photovoltaic (PV) systems are the fastest growing source of distributed generation, detailed information about where they are located and who their owners are is often known only to installers and utility companies. This lack of detailed information is a barrier to policy and financial assessment of solar energy generation and use. To bridge the described data gap, Oak Ridge National Laboratory (ORNL) was sponsored by the Department of Energy (DOE) Office of Energy Policy and Systems Analysis (EPSA) to create an automated approach for detecting and characterizing buildings with installed solar panels using high-resolution overhead imagery. Additionally, ORNL was tasked with using machine learning techniques to classify parcels on which solar panels were automatically detected in the Washington, DC, and Boston areas as commercial or residential, and then providing a list of recommended variables and modeling techniques that could be combined with these results to identify attributes that motivate the installation of residential solar panels. This technical report describes the methodology, results, and recommendations in greater detail, including lessons learned and future work.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Multifamily Building Stock Modeling: Cooperative Research and Development Final Report, CRADA Number CRD-17-00701

U.S. multifamily buildings house 35 million households, consuming 4 quads of source energy and spending $48 billion on utility bills every year. Almost all of these households live in urban areas, where cities are taking the lead on setting aggressive energy goals. Cities currently do not have the data and tools necessary to identify and target opportunities to save energy in their building stock. Building on the open-source, OpenStudio-based ResStock platform, this project will extend the publicly-available ResStock modeling capabilities to the multifamily sector, enabling Radiant Labs, and others, to partner with cities to market and strategically deploy cost-effective, energy efficiency (EE) upgrades directly to high-priority households. Radiant Labs has been successful in using ResStock's single-family capabilities to provide value to the City of Boulder, Colorado. However, lack of multifamily capabilities in ResStock is a roadblock for Radiant Labs working with New York City, San Francisco, Washington, D.C., and other cities that have expressed significant interest in working with them. In addition, other cities, companies, and utilities can use these open-source capabilities to grow their EE portfolios, thereby multiplying the impact of this project. This work also enables national-scale analysis of EE potential in multifamily buildings, which is of strong interest to a variety of stakeholders, including the U.S. Department of Energy (DOE) Office of Energy Policy and Systems Analysis (EPSA), DOE Weatherization Assistance Program (WAP), U.S. Department of Housing and Urban Development (HUD), and the Bonneville Power Administration (BPA).