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DOE OSTI2025

Financial Analysis of the Smallmouth Bass Flows implemented at the Glen Canyon Dam during Water Year 2024

The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specifies criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases. This report presents a financial analysis of the Smallmouth bass (Micropterus dolomieu) (SMB) flows implemented at GCD during Water Year (WY) 2024. These bypass flows were introduced by the U.S. Bureau of Reclamation (USBR) as an emergency response to the growing threat posed by invasive SMB in the Colorado River ecosystem downstream of the dam. SMB are a non-native predatory species that pose a significant threat to native fish populations, including the endangered humpback chub (Gila cypha). The thermal regime below GCD, typically cold due to hypolimnetic releases from Lake Powell, has historically served as a thermal barrier limiting SMB establishment. However, persistently low reservoir levels in recent years have reduced stratification in Lake Powell, allowing warmer water to be released downstream. This has enabled SMB to spawn successfully below the dam, prompting urgent ecological concerns. To mitigate the risk of SMB proliferation, the USBR implemented a series of bypass flows in WY 2024. Drawn from a lower elevation than the penstocks, the bypass structures released cooler water downstream. These short-duration bypass flows aimed to keep temperatures cool enough to prevent SMB from spawning, thereby reducing the ecological threat posed by this invasive species. Although motivated by ecological objectives, these bypass flows came with financial tradeoffs. Releasing water through the bypass structures instead of the turbines at GCD reduced hydropower generation, resulting in a significantly lower financial position for Western Area Power Administration (WAPA), which is responsible for marketing the electricity produced by the GCD Powerplant. This report analyzes the financial impact of the SMB flows implemented from July to November 2024. These experimental releases led to an estimated financial cost of approximately $18.9 million, primarily driven by the substantial volume of water diverted through the bypass structures. This study applies an integrated set of tools to estimate WAPA financial impacts by simulating GCD under two types of cases; namely, (1) a “With Experiment” case that mimics the water operations that actually occurred, including the SMB bypass flows, and (2) a “Without Experiment” case that simulates operations under the assumption that the SMB flows did not occur. Both cases comply with LTEMP hourly and daily operating criteria, and the monthly water release volumes are assumed to be identical under both cases. The Colorado River Storage Project Python-based model (CRiSPPy) model was the main modeling tool used to simulate the dispatch of the GCD hydropower plant and associated water releases from Lake Powell. In the modeling process, the research team used extensive data sets and historical information on SLCA/IP power plant characteristics, hydrologic conditions, and WAPA’s power purchases and sales prices.

13 HYDRO ENERGY↗

DOE OSTI2025

Carbon Capture and Storage for Small-to-Medium Biorefineries: Promising Carbon Removal Solution with Economic Challenges

Carbon capture and storage (CCS) integrated with biomass-based fuel production can provide cost-effective biomass carbon removal and storage (BiCRS) and produce high-value bioproducts, such as sustainable aviation fuels. To accelerate BiCRS deployment, it is crucial to quantify the costs of CCS integration, particularly for small- and medium-scale biorefineries that are representative of early-stage deployment. Existing studies tend to focus on plant sizes that are orders-of-magnitude larger than early-stage installations, possibly underestimating CCS costs for small-to-medium biorefineries. We show that the capture, transport, and storage costs to maximize CO 2 removal from a 526 dry tonne per day (tpd) biomass gasification plant (largest existing size) could be 13–47% higher than costs for a typical modeled plant size (2000 dry tpd). The higher cost estimates are driven by less favorable economies of scale and realistic assumptions about the availability of affordable CO 2 transport infrastructure with both drivers broadly applicable to other BiCRS technologies. Compliance and voluntary carbon markets could incentivize biorefinery CCS, but both carry a high degree of uncertainty. In conclusion, these findings highlight that sufficient and reliable financial mechanisms would be essential to unlocking the full CO 2 removal potential of biorefineries and facilitating BiCRS scale-up.

Biological transport↗

DOE OSTI2023

A review of privacy in energy applications

As the distribution system continues to experience an increase in distributed energy resource (DER) and electric vehicle (EV) penetration, so does the need for new solutions that can help grid operators manage and leverage their capabilities. This will undoubtedly lead to new operational schemes and business opportunities that will transform the traditional consumer into a prosumer who will be more actively engaged in grid operations. Although the field is still under active development, many of the potential use cases presented in literature or industry are built upon edge computing, two-way communications, and other innovative computational constructs to attain their goals. However, at their core, many use cases assume a great level of data access to aid with the decision-making process, an assumption that may need to be revised to ensure fair and equitable operational processes are maintained. This may be particularly true as edge resources are predicted to participate in retail-side, many-to-many, or peer-to-peer markets and thus may lead to financial impacts if data access considerations are ignored. The need to revise data access mechanisms can be further justified by the introduction of new participants into the operational process, who do not have the same level of trust, nor the incentives to focus on energy delivery as their primary objective. At the same time, more consumers are becoming aware of their own data, and the potential impacts of its abuse. To help solution developers better understand these risks, this report has been developed to offer an initial introduction to the topic of privacy. This is achieved by 1) Highlighting the need for privacy-aware solutions; 2) Encouraging system designers to be inquisitive about the status quo; 3) Documenting the existing threat space; 4) Presenting and evaluating tools that may be helpful towards enabling better privacy postures; and 5) Making recommendations to encourage the adoption of better practices. From a technical perspective, the report focuses on evaluating two potential techniques by applying them to the Transactive Energy Space. Based on the obtained results, it can be established that differential privacy (DP) methods may have limited applicability when highly correlated, time-series data records need to be protected. However, DP may be a powerful tool when it is used to aggregate and analyze mid-size and large-size data sets in a more traditional statistical environment. The second tool under evaluation is threshold cryptography, which can guarantee complete secrecy (and thus privacy) but requires the establishment of key management procedures and dedicated communication channels for key coordination. Therefore, due to its increased computational overhead, the use of threshold cryptography must be weighted using a cost/benefit analysis on a per-application basis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1% and as large as 0.5% for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

17 WIND ENERGY↗

DOE OSTI2024

Technology progress and clean vehicle policies on fleet turnover and equity: insights from household vehicle fleet micro-simulations with $\text{ATLAS}$

This paper documents the design and application of ATLAS (Automobile and Technology Lifecycle-Based ASsignment), a comprehensive household vehicle transaction and technology adoption micro-simulator in the San Francisco Bay Area. ATLAS evolves the fleet mix of individual households by simulating the vehicle transaction and choice decisions in response to co-evolving demographics, land use, and vehicle technology simulations. While most existing literature has focused on the aggregate clean vehicle uptake, this paper differentiates distributional effects and decomposes the underlying mechanisms across heterogeneous sub-populations of households. Using scenarios and sensitivity simulations that vary vehicle technology and policy assumptions, we find that Zero Emission Vehicles (ZEVs) penetrate into higher income groups at a faster rate than into lower income groups, which is intuitive and aligns with expectations. Interestingly, the relative income disparity in ZEV ownership shrinks over time across all scenarios, with a ZEV mandate coupled with declining battery cost leading to the greatest reduction in disparity of ZEV ownership by 2050. Federal, state, and local financial incentives influence the redistribution of ZEV uptake across income groups and contribute to narrowing income disparity. Vehicle transaction frequency and new versus used market dynamics are found to be important factors contributing to the income disparity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Quantifying the Financial Impacts of Electric Vehicles on Utility Ratepayers and Shareholders [Slides]

Widespread electric vehicle (EV) adoption is critical for meeting economy-wide decarbonization goals and, as a result, states are considering enabling policies and rate designs to accelerate EV deployment. EVs can provide possible financial upside to electric utilities and ratepayers in several ways. For example, from the utility perspective, EVs could drive increased electricity sales and new earnings opportunities through increased capital investments. From the ratepayer perspective, increased electric loads from EVs could reduce average all-in retail rates. The degree to which there are net benefits or costs to shareholders and/or ratepayers depends on how EVs are integrated and managed through enabling grid investments and charging strategies. Using Berkeley Lab’s Financial Impacts of Distributed Energy Resources (FINDER) model that mimics the electric utility investment planning and ratemaking processes, we estimate the utility earnings and customer rate impacts of EVs using a bookend approach of “managed” (i.e., best case) and “mismanaged” (i.e., worst case) charging strategies for a generic summer-peaking, investor-owned, and vertically integrated utility. The analysis also examines the sensitivity of results to different assumptions of EV deployment characteristics, EV impacts on retail electricity sales, incremental distribution system costs, EV charging location, and utility EV enablement costs (i.e., utility costs to invest in EV charging, controls, and communication to deliver and administer EV programs). The results are intended to inform EV policies and deployment strategies that maximize utility system benefits and minimize ratepayer costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Potential bill impacts of dynamic electricity pricing on California utility customers

The rapid growth of renewable generation is creating challenges for the California grid in the form of the “duck curve,” with increasingly steep ramping required for conventional generation resources in the morning and evening, and growing curtailment of solar resources in midday periods. Time-varying electricity tariffs have received considerable attention as a tool to address these challenges, with a renewed recent focus on the potential for dynamic tariffs that vary to reflect conditions on the grid in near-real time. Consideration of dynamic tariffs may raise concerns about the financial impact on utility customers, especially for those who have limited flexibility to modify their electricity consumption in response. Specific areas of concern include electricity bills, bill volatility, and equity implications related to cost shifting among customer groups. In this paper we leverage smart meter data for more than 400,000 California utility customers, spanning residential, commercial, industrial, and agricultural customers, to assess potential customer bill impacts arising from a multi-component dynamic tariff . Specifically, we compute impacts on customer bills and bill volatility under the assumption of fully inelastic demand, i.e., where customers do not change their consumption patterns in response to the tariff. We also assess various approaches designing subscription load shapes that customers can pre-purchase as a hedge that may provide a measure of protection against large negative impacts, while still incentivizing the modification of loads on the margin. We compare and contrast the relative impacts on different customer classes and discuss benefits and pitfalls of different dynamic tariff structures and subscription load shapes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Regression of exchangeable relational arrays

Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial transactions between individuals, contact frequencies between school children in classrooms and dynamic protein-protein interactions are all examples of relational arrays. Elements of a relational array are often modelled as a linear function of observable covariates. Uncertainty estimates for regression coefficient estimators, and ideally the coefficient estimators themselves, must account for dependence between elements of the array, e.g., relations involving the same actor. Existing estimators of standard errors that recognize such relational dependence rely on estimating extremely complex, heterogeneous structure across actors. This paper develops a new class of parsimonious coefficient and standard error estimators for regressions of relational arrays. Here we leverage an exchangeability assumption to derive standard error estimators that pool information across actors, and are substantially more accurate than existing estimators in a variety of settings. This exchangeability assumption is pervasive in network and array models in the statistics literature, but not previously considered when adjusting for dependence in a regression setting with relational data. We demonstrate improvements in inference theoretically, via a simulation study, and by analysis of a dataset involving international trade.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The Role of Innovation in the Circularity of EV Lithium-Ion Batteries

This case study analysis highlights the role of innovations in EV battery design (cells, modules, and packs), reverse supply chain, and recycling processes in yielding value for the economics of recycling (and have the largest impact on the circularity of LIBs). Part of the assessment of value will be a semi-quantitative evaluation of the value of resilience in the rapidly evolving LIB market – i.e., there are a variety of risks that recyclers would face in making a financial commitment to a recycling facility including; the possibility that batteries would not be collected in sufficient quantities, the market for key constituents (e.g., cobalt) might decrease because of changes in battery chemistry, battery manufacturers may not be willing to pay as much for recycled material, etc. The analysis would be semi-quantitative in that two simple models would be used to assess 1. material flows using a previously developed excel-based reverse supply chain flows model, and 2. A LIB recycling process material and energy balance cost model that will be used to qualitatively assess the cost impacts of process innovations and changes in feed streams. Both sets of models are highly speculative in that they rely on a multitude of assumptions and input values (e.g., EV adoption rates) that vary widely in the literature. Additionally, the initial process flow diagrams and equipment lists for the recycling cost model are based on the Argonne EverBatt model, which is still under development. However, in combination with a critical review of the literature, economic modeling can yield valuable insights into the role of innovation in the circularity of LIBs and high-technology (“energy relevant”) products in general.

28 EE - Advanced Manufacturing Office (EE-5A)↗

DOE OSTI2022

Modeling interconnections of safety and financial performance of nuclear power plants, part 3: Spatiotemporal probabilistic physics-of-failure analysis and its connection to safety and financial performance

Here, this paper is a byproduct of a line of research by the authors to analyze interrelationships of safety and financial performance of nuclear power plants (NPPs). The result of this line of research is summarized in three parts: Part 1 covers a categorical review of relevant literature and the theoretical bases that support the methodological developments in Part 2. Part 2 introduces an Integrated Enterprise Risk Management (I-ERM) methodological framework to quantify the interconnections of safety and financial performance with a focus on operation and maintenance (O&M) of NPPs. Part 2 has also demonstrated the applicability and values of the I-ERM methodology through an NPP case study. This paper is Part 3, where detailed development and implementation of one of the I-ERM modules, i.e., probabilistic physics-of-failure (PPoF) analysis, and its connection with safety and financial performance is reported. In this article, the physical failure modeling for hardware components is advanced by incorporating finite element analysis (FEA) into PPoF analysis and coupling the FEA-based PPoF with the maintenance performance through a renewal process model. This article covers two scientific contributions: (i) first-of-its-kind incorporation of FEA into the PPoF model of thermal fatigue for NPP components; and (ii) advancing the interface between the PPoF analysis and the renewal process model in order to deal with spatiotemporal FEA outputs and to efficiently estimate the physical transition rates even when the PPoF outputs are dominated by success data. Through the incorporation of FEA, the resolution of the PPoF analysis is enhanced as spatiotemporal conditions such as stress and temperature can be considered explicitly instead of relying on simplified assumptions or analytical models with reduced spatiotemporal dimensions. To demonstrate an application of the FEA-based PPoF analysis and its coupling with maintenance through the renewal process model, a case study is conducted using excess letdown elbow piping in the chemical and volume control system of a Pressurized Water Reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗

DOE OSTI2022

High-Octane Gasoline from Lignocellulosic Biomass via Syngas and Methanol/Dimethyl Ether Intermediates (2021 State of Technology)

The 2021 state of technology (SOT) assessment for the indirect liquefaction (IDL) of lignocellulosic biomass to high-octane gasoline (HOG) via methanol and dimethyl ether (DME) intermediates details the techno-economic analysis (TEA) results and technical progress of the pathway since the 2020 SOT report. A detailed process description and collection of underlying assumptions is given the 2015 design report. In 2021, research efforts rigorously quantified the selectivity of DME to aromatic compounds. As a result, the overall carbon selectivity to hydrocarbon products was updated based upon new findings. Additionally, research conducted over the past few years resulted in successful lower temperature regeneration of NREL's Cu/BEA catalyst with favorable implications on catalyst longevity. Discussions with an engineering firm resulted in an update to the excess air requirement for process combustors (including the char combustor, tar reformer combustor, and catalyst regenerator) in which excess air was reduced from 20% excess to 10%. The cumulative effect of the above efforts was used to calculate a minimum fuel selling price for the modeled pathway. Sensitivity cases examining federal carbon tax credits from CO 2 capture, product selectivity and yield, catalyst lifetime, and other key financial and process parameters were also considered in this assessment.

09 BIOMASS FUELS↗

DOE OSTI2025

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop

Geothermal cost and performance evaluation implemented via techno-economic assessment (TEA) modeling is critical for the U.S. Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for NREL's Annual Technology Baseline (ATB), which provides inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL's reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the U.S. generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on techno-economic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next-generation technologies such as closed-loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

annual technology baseline↗

DOE OSTI2024

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop: Preprint

Geothermal cost and performance evaluation implemented via technoeconomic assessment (TEA) modeling is critical for the Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for the Annual Technology Baseline (ATB). The ATB data are inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL’s reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the US generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on technoeconomic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next generation technologies such as closed loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

Annual Technology Baseline↗

DOE OSTI2021

Representing DC-Coupled PV+Battery Hybrids in a Capacity Expansion Model

Recent technology cost and performance improvements and the federal investment tax credit (ITC) have driven growing interest in coupling solar photovoltaic (PV) and battery systems. Combining these technologies into co-located or hybridized PV+battery systems has the potential to lower costs and increase energy output relative to multiple independent systems. In this work, we provide an overview of PV+battery systems and demonstrate methods for incorporating them into NREL’s Regional Energy Deployment System (ReEDS) capacity expansion model. Although the methods are applied to a specific model, we anticipate that the approaches used here can be useful for informing PV+battery method development for other capacity expansion models. The implemented method relies heavily on the native representations of PV and battery technologies; therefore, the focus of this work is on capturing and parameterizing the interactions between them for a configuration in which the PV and battery technologies share a single bi-directional inverter. This work also demonstrates the impacts of including PV+battery systems in the ReEDS optimization for the conterminous United States through 2050. In particular, we perform parametric sensitivities for input assumptions that are uncertain and expected to influence PV+battery deployment levels, including (a) the cost of PV+battery systems relative to independent PV and battery systems, (b) the battery component’s qualification for the ITC, and (c) future cost trajectories for PV and battery systems. We find that PV+battery deployment could occur throughout the conterminous United States if there are cost savings associated with DC coupling PV and battery technologies. If even modest (5%) cost savings can be achieved (through a shared inverter and balance-of-system costs), then approximately one-third of utility-scale PV deployment through 2050 adopts the DC-coupled hybrid configuration, resulting in total PV+battery deployment that exceeds the magnitude of PV+battery projects in U.S. interconnection queues in 2020. If greater cost savings can be achieved through DC coupling (e.g., due to a growing amount of shared balance-of-system costs, reduced financial risk, or modularity) or more rapid cost and performance improvements are realized for PV and battery technologies, then total PV+battery deployment and the share of PV and battery deployment that adopts the hybrid configuration grows (to >50%). In all cases, growing PV+battery deployment primarily displaces independent PV and battery technologies, indicating the strong competition between the hybrid and independent configurations comprising technologies with similar performance characteristics.

14 SOLAR ENERGY↗

DOE OSTI2024

How To Determine and Verify Operations and Maintenance Savings in Energy Savings Performance Contracts

Operations and maintenance (O&M) savings frequently occur in energy savings performance contracts (ESPCs). During FY 2022, 37% of reported annual cost savings for projects awarded under the U.S. Department of Energy (DOE) ESPC indefinite delivery indefinite quantity (IDIQ) contracts and in the performance period were due to O&M or other energy- and/or water-related cost savings, with the balance (63%) from utility cost savings (i.e., energy or water cost savings). Sometimes the energy- and water-related cost savings are acknowledged and included in payments within ESPCs; other times, for various reasons, they are not. As presented in this guide, FEMP recommends including energy- and water-related cost savings that are O&M (including related repair and replacement) savings in the financial aspects of an ESPC, to the extent such savings can be documented. Inclusion of these savings will help augment project scopes and/or lower interest costs (by shortening financing terms). However, there is a burden of proof as to what constitutes acceptability in O&M savings that needs to be carefully considered and documented in individual projects. Beyond promoting a key tenet used in U.S. federal performance contracting—that savings must be from actual budgets and therefore based on the level of O&M that is actually occurring, not what should have been performed—FEMP also recommends good practice in establishing and documenting O&M baselines, formulating the rationale for baseline adjustments during the performance period, and conducting ongoing verification activities. This document concludes with five examples of how O&M savings may be handled, in situations ranging from the partial displacement of O&M contracts to consolidation and “virtualization” of servers in data centers. A key theme that permeates this guide is the importance of thoroughly documenting all conditions and assumptions used in the development of and accounting for O&M costs and savings throughout the ESPC life cycle, from baseline-setting to measurement and verification (M&V) of the savings during each year of the performance period. Doing so not only prevents internal claims of non-performance (especially in the case of staff turnover during the contract term), but also simplifies ordering agency and energy service company (ESCO) response in the event of scrutiny from oversight organizations, such as government audits. While this guide focuses on federal ESPCs, it may also be applicable when O&M savings are included in utility energy service contracts (UESCs) and non-federal ESPCs.

Voss, Phil↗