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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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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Evaluate Distributed Energy Technologies for Cost Savings and Resilience With REopt Lite

NREL's REopt Lite TM web tool evaluates the economics of grid connected photovoltaics (PV), wind, and battery storage at a site. It allows users to identify the system sizes and battery dispatch strategy that minimize a site's life cycle cost of energy, and it estimates the amount of time a PV, wind, battery, and diesel generator system can sustain the site's critical load during a grid outage.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Real time feedback-based optimization of distributed energy resources

An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Dall'Anese, Emiliano↗
DOE OSTI2023

Power system restoration incorporating diverse distributed energy resources

An example system includes an aggregator configured to receive a service collaboration request and iteratively determine, based on minimum and maximum power values for DERs under its management, an optimized operation schedule. The aggregator may also be configured to iteratively determine, based on the optimized operation schedule, an estimated flexibility range for devices under its management and output an indication thereof. The system may also include a power management unit (PMU) configured to iteratively receive the indication and determine, based on a network model that includes the estimated flexibility range, a reconfiguration plan and an overall optimized operation schedule for the network. The PMU may also be configured to iteratively cause reconfiguration of the network based on the plan. The PMU and aggregator may also be configured to iteratively, at a fast timescale, cause energy resources under their management to modify operation based on the overall optimized operation schedule.

Ding, Fei↗
DOE OSTI2023

Real time feedback-based optimization of distributed energy resources

An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Dall'Anese, Emiliano↗
DOE OSTI2024

Modifications to the Thermal Energy Distribution System (TEDS)

INL installed a new controls system and a flame detection system on TEDS as part of upgrades that will eventually allow unattended operation and integration with accessory systems, such as digital twins, or facilities in other locations. These upgrades resolve some corrective actions that came about when oil was spilled from a high-point vent during system startup testing.

42 ENGINEERING↗
DOE OSTI2026

Techno-Economic Assessment of Data Center Load Demand Powered by Small Modular Reactors and Distributed Energy Resources

The rapid increase in data center energy demand, driven by AI and large-scale data processing, poses significant challenges to global energy infrastructure. Data centers require substantial and reliable energy for continuous operations and high-performance computing. Current electrical grids face issues such as transmission bottlenecks and aging infrastructure, making it difficult to meet these demands. Integrating inverter-based-resources (IBRs) like solar and wind presents both opportunities and challenges due to their intermittent nature. Small Modular Reactors (SMRs) offer a promising solution with their enhanced safety, modularity, reliability, and scalability, providing consistent base load power ideal for data center operations. This study presents a comprehensive techno-economic assessment of powering data center load demand using a combination of SMRs and IBRs with grid-connected and islanded mode. This study utilized Idaho National Laboratory’s (INL) HPC data center hourly load profiles and Xendee microgrid optimization platform to conduct the analysis. In this configuration, SMRs serves as the primary base load power source, consistently providing a steady supply of electricity necessary to meet the minimum load demand of the data center with support from the IBRs. Key performance indicators such as Levelized Cost of Electricity (LCOE), Net Present Value (NPV) has been calculated to assess the economic feasibility. The findings from this research will underscore the strategic benefits of integrating SMR plant with DERs – particularly for critical infrastructure load such as data centers.

14 - SOLAR ENERGY↗
DOE OSTI2023

Risk-Informed Condition Evaluation of Solar-centered Energy Generation and Distribution Networks through Bayesian Learning and Inference

We develop a methodology based on Bayesian inference over Probabilistic Graphical Models (PGMs) to understand and quantify risk in solar-centered grids using targeted measurements and learned system behavior. Being non-prescriptive but, rather, able to infer system behavior and, ultimately, address risk queries from data, our machine learning-type paradigm is tailored for diverse topologies and threat scenarios often associated with distributed energy generation and photovoltaic distributed energy resources (PV-DERs) in particular. We describe algorithmic processes for: (i) learning the structure of PGMs that result from attack-prone PV-DER-proliferated distribution systems, (ii) quantifying cause-effect relationships, and (iii) evaluating risk queries based on diverse evidence. The contributions are illustrated on a residential grid subject to output impairment attacks on its PV-DER infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗