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DOE OSTI2014

Scenarios for Deep Carbon Emission Reductions from Electricity by 2050 in Western North America using the Switch Electric Power Sector Planning Model: California's Carbon Challenge Phase II, Volume II

This study used a state-of-the-art planning model called SWITCH for the electric power system to investigate the evolution of the power systems of California and western North America from present-day to 2050 in the context of deep decarbonization of the economy. Researchers concluded that drastic power system carbon emission reductions were feasible by 2050 under a wide range of possible futures. The average cost of power in 2050 would range between $149 to $232 per megawatt hour across scenarios, a 21 to 88 percent increase relative to a business-as-usual scenario, and a 38 to 115 percent increase relative to the present-day cost of power. The power system would need to undergo sweeping change to rapidly decarbonize. Between present-day and 2030 the evolution of the Western Electricity Coordinating Council power system was dominated by implementing aggressive energy efficiency measures, installing renewable energy and gas-fired generation facilities and retiring coal-fired generation. Deploying wind, solar and geothermal power in the 2040 timeframe reduced power system emissions by displacing gas-fired generation. This trend continued for wind and solar in the 2050 timeframe but was accompanied by large amounts of new storage and long-distance high-voltage transmission capacity. Electricity storage was used primarily to move solar energy from the daytime into the night to charge electric vehicles and meet demand from electrified heating. Transmission capacity over the California border increased by 40 - 220 percent by 2050, implying that transmission siting, permitting, and regional cooperation will become increasingly important. California remained a net electricity importer in all scenarios investigated. Wind and solar power were key elements in power system decarbonization in 2050 if no new nuclear capacity was built. The amount of installed gas capacity remained relatively constant between present-day and 2050, although carbon capture and sequestration was installed on some gas plants by 2050.

14 SOLAR ENERGY↗

DOE OSTI2024

Multiscale Flow for robust and optimal cosmological analysis

We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Multiscale Flow uses hierarchical decomposition of cosmological fields via a wavelet basis and then models different wavelet components separately as Normalizing Flows. The log-likelihood of the original cosmological field can be recovered by summing over the log-likelihood of each wavelet term. This decomposition allows us to separate the information from different scales and identify distribution shifts in the data such as unknown scale-dependent systematics. The resulting likelihood analysis can not only identify these types of systematics, but can also be made optimal, in the sense that the Multiscale Flow can learn the full likelihood at the field without any dimensionality reduction. We apply Multiscale Flow to weak lensing mock datasets for cosmological inference and show that it significantly outperforms traditional summary statistics such as power spectrum and peak counts, as well as machine learning–based summary statistics such as scattering transform and convolutional neural networks. We further show that Multiscale Flow is able to identify distribution shifts not in the training data such as baryonic effects. Finally, we demonstrate that Multiscale Flow can be used to generate realistic samples of weak lensing data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

District-Scale Analysis of Electricity Load and Strategies to Improve Energy Reliability Using Prototype District Models

Projected increases in electricity demand in the U.S. highlight the urgent need for effective load management to ensure grid reliability. As the building sector accounts for approximately 75% of electricity usage, enhancing energy efficiency and flexibility in this sector is crucial. Adopting district-level approaches offers significant advantages over traditional individual building analyses by enabling shared infrastructure and economies of scale. To navigate the data and computational challenges associated with modeling energy at the district level, prototype district models have been proposed as holistic, system-level solutions that capture complex interactions within typical configurations. This study presents these models as a reference tool for analyzing district-scale energy systems across various climate zones in the U.S. Developed with input from stakeholders, these models integrate varied building characteristics, inter-building connections, and energy system interactions. A case study utilizing the Urban Edge prototype district model, implemented on the URBANopt™ platform, evaluates multiple demand scenarios and the impact of distributed energy resources such as fuel-fired backup generators, photovoltaic systems, and batteries. Findings suggest that while new electric systems can significantly reduce annual energy use, they may also elevate peak electricity loads, with a notable 43% increase in heating-dominant climate zone 5B. The optimal backup power solutions vary based on location, influenced by factors such as utility rates and incentives. For example, PV and batteries perform well in high-cost regions like New York City, while diesel backup generators are more suitable for backup needs in climate zone 3A, such as Atlanta. Thus, this research highlights the importance of prototype district models for future district-scale energy planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

HTTR 3 D Cross Section Generation with Serpent and MAMMOTH

The High Temperature Test Reactor (HTTR) is a graphite moderated thermal reactor with a nominal power output of 30 MWth. The purpose of this work is to prepare a set of neutron cross sections for use in the transient analyses of three Loss of Forced Cooling (LOFC) events planned for this reactor. The cross sections are generated with the Serpent 2 3 D Monte Carlo code to include the axial heterogeneity and the strong axial coupling of this core. The approach yields good agreement of results when compared to previously used 2 D lattice models, which require a fine spatial discretization and, surprisingly, comes at a higher computational cost. In addition, the temperature distribution must be considered to obtain reasonable cross sections and the axial leakage component dominates the spectral effects in the core. Consequently, a temperature model as a function of the fuel and moderator temperatures is adopted, and the core is depleted to 390 effective full-power days (EFPDs). A full tabulation of cross sections is prepared at this burnup point for a variety of fuel and moderator temperatures. Next, the multiphysics reactor application MAMMOTH is used to evaluate the quality of these cross sections using a diffusion solver. As expected, the homogenization error in the cross sections is significant and the Super Homogenization (SPH) correction from MAMMOTH is necessary to preserve key reaction rates. The SPH corrected MAMMOTH results are in excellent agreement with the Serpent results and reproduce both the reference power profile and temperature coefficients at each tabulation point. This fact confirms the accuracy of the SPH corrected cross sections and demonstrates the maturity of using Serpent 2 and MAMMOTH for 3 D cross section generation, even for reactors as complex as the HTTR. Finally, the authors recommend that the cross sections should be parametrized with the local burnup and that a full core burnup calculation with coupled thermal fluids would provide a better estimate of the initial condition for any subsequent transient analysis. Unfortunately, this was not within the scope of this work, and direct analysis with these tabulations should be considered as a first order approximation, at best.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Joint X-Ray, Kinetic Sunyaev–Zeldovich, and Weak Lensing Measurements: Toward a Consensus Picture of Efficient Gas Expulsion from Groups and Clusters

There is no consensus on how baryon feedback shapes the underlying matter distribution from either simulations or observations. We confront the uncertain landscape by jointly analyzing new measurements of the gas distribution around groups and clusters—DESI+ACT kinetic Sunyaev–Zel’dovich (kSZ) effect profiles and eROSITA X-ray gas masses—with mean halo masses characterized by galaxy–galaxy lensing. Across a wide range of halo masses ( M 500 = 10 13−14 M ⊙ ) and redshifts (0 < z < 1), we find evidence of more efficient gas expulsion beyond several R 500 than predicted by most state-of-the-art simulations. A like-with-like comparison reveals all kSZ and X-ray observations are inconsistent with the fiducial 1 Gpc 3 hydrodynamical FLAMINGO simulation, which was calibrated to reproduce pre-eROSITA X-ray gas fractions: eROSITA X-ray gas fractions are 2 × lower than the simulation, and the kSZ measurements are combined >8σ discrepant. The FLAMINGO simulation variant with the most gas expulsion, and therefore the most suppression of the matter power spectrum relative to a dark-matter-only simulation, provides a good description of how much gas is expelled and how far it extends; the enhanced gas depletion is achieved by more powerful but less frequent AGN outbursts. Joint kSZ, X-ray, and lensing measurements form a consistent picture of gas expulsion beyond several R 500 , implying a more suppressed matter power spectrum than predicted by most recent simulations. Complementary observables (e.g., thermal Sunyaev–Zel’dovich effect and fast radio bursts) and next-generation simulations are critical to understanding the physical mechanism behind this extreme gas expulsion and mapping its impact on the large-scale matter distribution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Enhancing power grid resilience to winter storms via generator winterization with equity considerations

Here we develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Transient Stability of the US Western Interconnection with High Wind and Solar Generation

The addition of large amounts of wind and solar generation to bulk power systems that are traditionally subject to operating constraints set by transient limitations is the subject of considerable concern in the industry. The US Western Interconnection (WI) is expected to experience substantial additional growth in both wind and solar generation. These plants will, to some extent, displace large central station thermal generation, both coal and gas-fired, which have traditionally helped maintain stability. This paper reports the results of a study that investigated the transient stability of the WI with high penetrations of wind and solar generation. The main goals of this work were to (1) create a realistic, baseline model of the WI, (2) test selected transient stability events, (3) investigate the impact of large amounts of wind and solar generation, and (4) examine means to improve performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

Transient Stability and Frequency Response of the US Western Interconnection Under Conditions of High Wind and Solar Generation

The addition of large amounts of wind and solar generation to bulk power systems that are traditionally subject to operating constraints set by transient stability and frequency response limitations is the subject of considerable concern in the industry. The US Western Interconnection (WI) is expected to experience substantial additional growth in both wind and solar generation. These plants will, to some extent, displace large central station thermal generation, both coal and gas-fired, which have traditionally helped maintain stability. This paper reports the results of a study that investigated the transient stability and frequency response of the WI with high penetrations of wind and solar generation. The main goals of this work were to (1) create a realistic, baseline model of the WI, (2) test selected transient stability and frequency events, (3) investigate the impact of large amounts of wind and solar generation, and (4) examine means to improve performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Impact of the 2017 Solar Eclipse on the Smart Grid

With the increasing interest in using solar energy as a major contributor to the use of renewable generation, and with the focus on using smart grids to optimize the use of electrical energy based on demand and resources from different locations, the need arises to know the moons position in the sky with respect to the sun. When a solar eclipse occurs, the moon disk might totally or partially shade the sun disk, which can affect the irradiance level from the sun disk, consequently affecting a resource on the electric grid. The moons position can then provide smart grid users with information about how potential total or partial solar eclipses might affect different locations on the grid so that other resources on the grid can be directed to where they might be needed when such phenomena occurs. At least five solar eclipses occur yearly at different locations on Earth, they can last 3 hours or more depending on the location, and they can affect smart grid users. On August 21, 2017, a partial and full solar eclipse occurred in many locations in the United States, including at the National Renewable Energy Laboratory in Golden, Colorado. Solar irradiance measurements during the eclipse were compared to the data generated by a model for validation at eight locations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Pre-commercial Demonstration of Direct Non-iterative State Estimator for Operational Use of Synchrophasor Management Systems

The objective of this project was to demonstrate functionality and performance of a Direct Non-iterative State Estimator (DNSE) integrated with NYPA’s Energy Management System (EMS) and with enhanced Real Time Dynamics Monitoring System (RTDMS) synchrophasor platform from Electric Power Group (EPG). DNSE is designed to overcome a major obstacle to operational use of Synchro-Phasor Management Systems (SPMS) by providing to synchrophasor management systems (SPMS) applications a consistent and a complete synchrophasor data foundation in the same way that a traditional EMS State Estimator (SE) provides to EMS applications. Specifically, DNSE is designed to use synchrophasor measurements collected by a central PDC, Supervisory Control and Data Acquisition (SCADA) measurements, and Energy Management System (EMS) network model, to obtain the complete state of the utility’s operating model at rates that are close to the synchrophasor data rates. In this way, the system is comprehensive in that it does not only cover the part of the network that is “visible” via synchrophasors, but also the part that is only “visible” through the SCADA measurements. Visualization needs associated with the use of DNSE results are fulfilled through suitably enhanced Real Time Dynamics Monitoring System (RTDMS), with the enhancements implemented by EPG. This project had the following goals in mind: To advance the deployment of commercial grade DNSE software application that relies on synchrophasor and SCADA data ; Apply DNSE at other utilities, to address a generic and fundamental need for “clean” operational data for synchrophasor applications; Provide means for “live” estimated data access by control system operators; Enhance potential for situational awareness through full system operational model coverage; Sub-second execution rate of the Direct Non-iterative State Estimator, eventually at a near-phasor data rate execution speed, i.e. < 0.1 sec. Anticipated benefits from this projects are: Enhanced reliability and improvements in the economic efficiency of bulk power system planning and operations; Providing “clean” data to other synchrophasor applications; Enhancement of situational awareness by providing the full operational model updated at near synchrophasor rate; A production-grade software tool that incorporate synchrophasor and SCADA data; Provides a basis for development of next generation monitoring and control applications, based on both SCADA and PMU data. Quanta Technology (QT) team worked in collaboration with Electric Power Group (EPG) whose team has enhanced its commercial Real Time Dynamics Monitoring System (RTDMS) to accommodate the requirements posed by DNSE application. EPG also provided its ePDC and Model-less Data Conditioning (PDVC) software for integration with DNSE+. QT developed the system requirements for DNSE; developed system architecture and defined interfaces between internal DNSE components. The core DNSE algorithm with all surrounding interfaces was named DNSE+. Since the DNSE development was done in a simulated system environment, QT used its PMU simulator that was enhanced during this project for development and factory acceptance testing (FAT). SCADA data in this stage was simulated by commercial PSS/e software. The output of DNSE are estimates of System states in C37.118-2 format, sent to RTDMS for further processing and display. As the number of these states is large, it was necessary to expand the C37.111-2 standard to accommodate large data sets. This enhancement was implemented in RTDMS. The demonstration of pre-production DNSE technology was done at NYPA using streaming field data from NYPA PMUs and from its RTUs through their SCADA system. NYPA provided ICCP interface as well as Common Information Model (CIM). The relevance of the DNSE+ application is that it provides state estimation of the power system based on hybrid set of data, consisting of both available PMU data and SCADA measurements. As this is a direct, non-iterative method of calculation of the system states, if does not suffer from convergence issues which is potential problem for conventional state estimators. Also, it can take any available PMU measurements, so it does not need to have a high percentage of PMU coverage needed in the case of Linear State Estimator. As the DNSE calculates synchrophasors of the system states (both phase and absolute value) as sub-second rate, this application can provide a basis for development of next generation of applications based both on SCADA and PMU data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Gas tube-switched high voltage DC power converter

A direct current (DC)-DC converter includes a transformer and a gas tube-switched inverter circuit. The transformer includes a primary winding and a secondary winding. The gas tube-switched inverter circuit includes first and second inverter load terminals and first and second inverter input terminals. The first and second inverter load terminals are coupled to the primary winding. The first and second inverter input terminals are couplable to a DC node. The gas tube-switched inverter circuit further includes a plurality of gas tube switches respectively coupled between the first and second inverter load terminals and the first and second inverter input terminals. The plurality of gas tube switches is configured to operate to generate an alternating current (AC) voltage at the primary winding.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗

DOE OSTI2022

1.2.4.404 - Data-Driven Approach for Hydropower Plant Controller Prototyping Using Remote Hardware in the Loop (DR-HIL)

Real-time prototyping of hydropower plant controls is important for reducing the cost and the risk of field deployment. This project will 1) collect design and operational data from actual hydro plants and 2) use a physics-informed machine learning approach for real-time emulation of hydropower plants, including hydro turbine and hydrodynamics. The data-driven models will be interfaced with digital real-time simulation at NREL’s Flatirons campus for hardware-in-the-loop (HIL) testing of the governor hardware device or controller-HIL (CHIL). The proposed approach will also establish the connectivity based remote CHIL testing capability using real-time data streams from an actual hydro plant. This integrated hydro-plant emulation with CHIL will be used to prototype hydro-governor controls and eventually provide an opportunity to test hydropower integrated with various technologies (e.g. conventional and renewable generation, energy conversion, etc.) as HIL.

controls prototyping↗

DOE OSTI2022

DOE OE 2021 Strategy White Papers on Microgrids: Program Vision, Objectives, and R&D Targets in 5 and 10 years–Topic Area #1

This white paper describes the program vision, objectives, and R&D targets in 5 to 10 years for the Department of Energy (DOE) Office of Electricity (OE) Microgrid R&D Program. The vision is to facilitate the nation’s transitions to (1) a more resilient and reliable, (2) more decarbonized electricity infrastructure, in which (3) microgrids have a reduced cost to implement. This strategy is developed in the context that the United States’ electricity delivery system is becoming more distributed in nature. The electricity generation capacity in 10 years may be 30-50% distributed energy assets.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Evaluation of Communication Issues in Primal-Dual-Based Distributed Energy Resource Management Systems (DERMS)

With the increasing adoption of distributed energy resources (DERs) in distribution networks, DERMS is becoming an attractive option to coordinate the control of DERs, especially primal-dual-based DERMS which is a well-developed class of them. In order to help lower the uncertainty in commercializing them, we evaluate and find the upper limits of severity for some common issues that can occur within the communication systems on which a primal-dual-based DERMS relies on; these include packet losses, link failures, and delays. This information can help a DERMS operator decide on the specifications for the communication infrastructure it will invest in for the DERMS. Our evaluation is based on numerical simulations of a real-world feeder with around 2,000 nodes and a DERMS that controls 163 PV generators and 140 energy storage batteries. In general, we found that the DERMS becomes less resistant to communication issues as the information flows closer to the DERs implementing the controllable power injections.

communication delay↗

DOE OSTI2023

Evaluation of Communication Issues in Primal-Dual-Based Distributed Energy Resource Management Systems (DERMS): Preprint

communication delay↗

DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗