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At least 1,081 records · Page 60

Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

ResStock Measure Documentation: Reference Space Heating and Air Conditioning Upgrade Circa 2025

This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Reference Space Heating and Air Conditioning Upgrade Circa 2025" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

De-Risking Exploration for Geothermal Plays in Magmatic Environments Through Open-Source Tools: An Open-Source Python Framework for 2D and 3D Play Fairway Analysis

The De-Risking Exploration for Geothermal Plays in Magmatic Environments (DEEPEN) project seeks to accelerate superhot geothermal development by reducing exploration risk through advanced open-source modeling tools. This work presents a novel Python-based framework, geoPFA, for conducting 2D and 3D play fairway analysis (PFA) tailored to superhot geothermal systems. Building on previous methodologies, the framework integrates thermo-hydro-mechanical-chemical simulation outputs from TReactMech, resulting in improved representation of subsurface properties that are critical to superhot resource producibility. The workflow has been applied to the Nesjavellir field in Iceland, a candidate site for the third Iceland Deep Drilling Project's superhot production scenarios. This application demonstrates the value of modular, transparent, and extensible workflows for integrating geological, geophysical, and simulation-derived datasets in high-enthalpy environments. Preliminary results indicate favorable zones consistent with known hydrothermal activity and suggest possible upflow from the Hengill volcanic system. The geoPFA library is publicly available, offering a scalable and reproducible approach to geothermal exploration across varied geological contexts.

15 GEOTHERMAL ENERGY↗

Data Center Cybersecurity, Supply Chain Risk Management, and Emerging Regulation Cohort Summary: Takeaways and Action Plans

This report summarizes the outcomes of the Data Center Cohort under the Department of Energy’s Technical Assistance for Digital Assurance (TADA) initiative, aimed at enhancing grid resilience through cybersecurity, supply chain risk management (SCRM), and Cyber-Informed Engineering (CIE). The cohort engaged 17 organizations across utilities, data center operators, vendors, and technology providers in three sessions combining presentations, discussions, and exercises. Key topics included AI-driven load behavior, cybersecurity vulnerabilities in UPS/BESS and cooling systems, governance gaps at utility–data center boundaries, and supply chain integrity. Five cross-cutting themes emerged: interconnection architecture vulnerabilities, fragmented governance, AI-driven stability risks, lack of regulatory frameworks, and long-term supply chain concerns. Actionable recommendations were developed, including implementing DMZ segmentation, formalizing vendor access agreements, designing AI workload limits, and advancing standards through NERC and state-level programs. These strategies aim to strengthen resilience, clarify responsibilities, and ensure secure integration of data centers into the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid

The convergence of Information Technologies and Operational Technology systems in industrial networks presents many challenges related to availability, integrity, and confidentiality. In this paper, we evaluate the various cybersecurity risks in industrial control systems and how they may affect these areas of concern, with a particular focus on energy-sector Operational Technology systems. There are multiple threats and countermeasures that Operational Technology and Information Technology systems share. Since Information Technology cybersecurity is a relatively mature field, this paper emphasizes on threats with particular applicability to Operational Technology and their respective countermeasures. We identify regulations, standards, frameworks and typical system architectures associated with this domain. We review relevant challenges, threats, and countermeasures, as well as critical differences in priorities between Information and Operational Technology cybersecurity efforts and implications. These results are then examined against the recommended National Institute of Standards and Technology framework for gap analysis to provide a complete approach to energy sector cybersecurity. We provide analysis of countermeasure implementation to align with the continuous functions recommended for a sound cybersecurity framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

What's New in System Advisor Model

This talk will give an overview of recent and planned model improvements in the NLR System Advisor Model, including improved spectral model options, improved access to snow data for PV modeling across the United States, work on hybrid PV, CSP, and thermal energy storage modeling, current research into accurately modeling tandem cells, and more.

14 SOLAR ENERGY↗

Cyber-Informed Engineering (CIE) Power Generation Guide [Slides]

This guide offers suggestions for applying CIE principles to technologies used to generate electric power. It addresses issues of design, implementation, and maintenance, preemptively addressing cybersecurity threats to electric generation. The intended audience for this guide includes practitioners across the energy and cybersecurity sectors, such as energy industry professionals (e.g., engineers, system designers, operators, and researchers) and cybersecurity experts (e.g., communication system designers, information technology/operational technology [IT/OT] administrators, and penetration testers).

13 HYDRO ENERGY↗

Powered by dGen Webinar [Slides]

NLR's Powered By Webinar Series featuring NLR's dGen Modeling Tool. The Distributed Generation Market Demand (dGenTM) model simulates customer adoption of distributed energy resources for residential, commercial, and industrial entities in the United States or other countries through 2050. The model enables analysis at multiple geographic levels (national, state, and utility, or below) and offers sophistication in representation of decision-making regarding economic and behavioral considerations. Analysts have used dGen to answer questions about load forecasting and integrated resource planning, policy analysis, locational value of distributed energy resources, and more. dGen is open source, and various energy organizations - including independent system operators, regional transmission organizations, and the California Energy Commission - use the model internally.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

plexosdb: A Modular Library for Programmatic PLEXOS Model Construction

plexosdb is a lightweight Python library for constructing PLEXOS models using a SQLite-backed data structure. It provides a clear, modular interface that maps relational data directly to model components. By leveraging SQLite and idiomatic Python, it enables fast iteration and reproducible workflows. The result is a performant, composable foundation for scalable PLEXOS model development.

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Modeling Occupant Core Temperatures Across the Boston Building Stock to Advance Public Health

In recent history, extreme heat has been the cause of most deaths from a natural disaster. Exposure to extreme heat can aggravate preexisting conditions, increase hospitalization, and even cause death. Thus, modeling the thermal resilience of households across the United States will allow for a quantitative assessment of the health and safety risks posed by extreme heat. For the first time, we simulate occupant comfort in representative households across Boston by combining the granular results of the ResStock(TM) model with a two-node heat strain model. This model calculates occupants' core body temperature in each simulated household over a year. We compare the simulated core temperatures to two public health metrics: hyperthermia and heat stroke. Our results show for households in Boston that do not have or use aid conditioning, thousands potentially experience many dangerous heat events each summer and these events can last for more than a day at a time. These heat events peak during the late afternoon and evening, just as residents are coming home, cooking meals, and trying to go to sleep. We have found that multi-family buildings, renters, and low-income homes in more airtight and insulated homes are the most at risk for these events. These results demonstrate the scale and urgency of exposure to extreme heat.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Powered By SAM [Slides]

The System Advisor Model(TM) (SAM) is a free, open-source desktop application for techno-economic analysis of energy technologies. By combining detailed performance modeling with financial analysis, SAM allows users to assess technology trade-offs, explore future scenarios, and make informed decisions about energy investments. Users also have access to model details and the ability to embed SAM's core models in their own applications. This webinar, hosted by National Laboratory of the Rockies researchers Janine Keith and Matt Prilliman, highlights how this widely used modeling tool supports data-driven decision-making for energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Updating PV and Battery Bill Savings Calculations for Net Billing: New Best Practices for Input Data and Uncertainty

Jurisdictions are increasingly adopting compensation structures for distributed PV and PV-battery systems that price exported energy lower than energy consumed onsite (net billing rates). Standard methods for calculating the bill savings from PV and PV-battery systems were developed for net metering structures, and applying these same methods to net billing structures (such as using Typical Meteorological Year weather with actual year load) introduces bias errors that underestimate PV-battery system bill savings by between 1.5\% and 9\%, depending on the utility rate. We assess the magnitude of these errors and compare them to other sources of uncertainty when estimating the bill savings from PV and PV-battery systems under more complex utility rates.

14 SOLAR ENERGY↗

Marine Fuel Innovation in the Great Lakes and St Lawrence Seaway

NLR collaborated with five Great Lakes ports to conduct analysis on potential risks and opportunities for making additional fuels available at their ports. The project included: Identifying existing and anticipated fuel production within the region; examining current port infrastructure and activities; exploring potential siting feasibility of additional energy infrastructure based on available area; introducing the Alternative Energy for Ports Explorer (APEX) tool to help ports understand existing energy resources and infrastructure. This presentation summarizes the work we did and our key findings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Integrated Reliability and Economic Modeling for Transmission Across Large Regions: A Space Odyssey

Power flow modeling and stability analysis are needed to more-comprehensively assess system reliability but the development of the system portfolios and conditions require use of economic models (e.g., production cost). What are the state of art methods for efficiently linking economic and reliability models to enable examination of multiple snapshots and perform detailed nodal analyses?

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Validation of the NLR Pumped Storage Hydropower Cost Model

The National Laboratory of the Rockies (NLR) first released its pumped storage hydropower (PSH) cost model in 2023 as the most detailed bottom-up PSH cost model available to the public. It is available both as a spreadsheet and an interactive web tool, enabling users with a variety of PSH interests to transparently characterize costs of alternative PSH sites and designs. The PSH cost model cannot replace detailed site-level studies and design, but it is important to validate it against other industry PSH cost estimates. The initial model methodology report validated the cost model for a single proposed site, the Eagle Mountain Project in California. This slide deck documents an expanded validation exercise using cost data from six other sites: Goldendale (Washington), Seminoe (Wyoming), Gordon Butte (Montana), Swan Lake (Oregon), White Pine (Oregon), and Lewis Ridge (Kentucky). It compares itemized costs from Federal Energy Regulatory Commission (FERC) applications and other reported costs with NLR PSH cost model outputs after customizing inputs for each site. The validation exercise finds that the NLR model's conservative indirect cost assumptions often drive overall cost overestimation, with direct cost comparisons typically agreeing more closely. All cost model estimates are well within an Association for the Advancement of Cost Engineering (AACE) Class 5 estimation range (-50% to +100%), with five within the AACE Class 4 range (-30% to +50%) and four being within 15%. This result is considered reasonable performance for a parametric model applied at a preliminary design stage.

13 HYDRO ENERGY↗

The Hawaii Meteorology, Energy and Transmission (MET) Toolkit

Reliable long-term resource adequacy and grid planning in Hawaii require precise, multi-decadal meteorological records. This paper introduces the Hawaii Meteorology, Energy and Transmission (MET) Toolkit, a 26-year (2000-2025) high-fidelity atmospheric dataset developed by the National Laboratory of the Rockies (NLR). We present a validation study of the underlying WRF model configurations, comparing the legacy MYNN PBL scheme against an alternative YSU formulation. Using vertical lidar profiles and surface buoy data, our analysis identified a foundational geometric distortion in the legacy NOW-23 Hawaii dataset caused by an incorrect grid projection. When evaluated on a corrected, zero-distortion grid, the YSU scheme demonstrated superior performance in bias and cRMSE compared to the legacy setup. To address these findings, the MET Toolkit has been re-produced as a unified 26-year record using the optimized YSU setup and corrected geometry. This dataset offers the Hawaii power sector a robust, validated, and homogenous reference for future grid resilience and energy integration.

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