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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗
DOE OSTI2025

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

aggregates↗
DOE OSTI2019

Evaluating the Impact of Price-Responsive Load on Power Systems Using Integrated T&D Simulation

This paper explores the differences between simulating price-responsive load (PRL) interactions with power systems using integrated transmission and distribution (T&D) models and transmission-only (T-only) models. This analysis uses the Integrated Grid Modeling System (IGMS) software to capture 'ISO-to-appliance' simulations using a synthetic T&D model built on the PJM 5-Bus transmission network with multiple full-scale taxonomy feeders that include physics-based models of thousands of customers and PRLs. The results show important differences in the impacts of PRLs between integrated T&D and T-only models. Experiments with the synthetic integrated T&D dataset demonstrated that integrated T&D simulation revealed notably larger differences between the PRL and no-PRL cases for load, and prices compared to T-only simulation. Similarly, differences are observed between the price response of individual buildings and distribution feeders and the corresponding transmission bus in the integrated T&D simulations, which are difficult to capture in traditional T-only simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

MCNP Coupling with BISON

This report describes the coupling scheme involving power distribution from MCNP to Bison.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Interdependent Water And Power Infrastructure Model

The approach used is the Multi-Agent System (MAS) paradigms, where systems components are represented as agents, interacting both with each other, and with the environment in which they evolved. Agents behaviors correspond to components in the real Integrated Water-Power System. The model simulate actions and interactions of these (autonomous) agents to analyze their effects on the overall system. Agents in the water system capture components of water collection, treatment, transportation, distribution and use (e.g. pipe, canal, pump, water demands for agriculture, etc.). Agents in the power system capture components of power generation, transportation, distribution and use (electricity demands, sources, etc.).

Toba, Danho Ange Lionel [Idaho National Laboratory↗
DOE OSTI2022

Forming Interphase Microgrids in Distribution Systems Using Cooperative Inverters

Interphase-microgrids formed by three islanded single-phase feeders in distribution systems are proposed in this paper. Loss of utility caused by natural and human-made disasters may isolate each residential subdivision from the utility. In this case, the household loads are supplied by distributed generation (DG) units which may not be enough to meet the load demand. This can lead to deviations in voltage and frequency, and the deviations may vary for each phase. The proposed solution is to form an interphase-microgrid by seamlessly interconnecting the islanded single-phase feeders. This interphase-microgrid can effectively balance the load demands and DG generation in distribution systems after losing the utility supply. This paper presents different case studies to demonstrate the viability of forming interphase-microgrids for residential distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
DOE OSTI2021

Disaggregating Future Retail Electricity Rate Growth [Slides]

Recent Berkeley Lab research found that modest retail rate increases over the past 10 years were mostly driven by large increases in capital expenditures (CapEx) that were offset in part by substantial wholesale price reductions. Decision-makers are increasingly concerned about the potential future rate impacts of a number of policies and industry trends that support rapid decarbonization, electrification, and grid modernization. Using historical FERC Form 1 data and the existing literature on policies and industry trends that are likely to affect utility-incurred costs and retail sales, Berkeley Lab researchers developed ranges of forecasted growth rates for cost-related rate drivers (i.e., fuel and purchased power; transmission, distribution, generation, and other categories of both non-fuel operations & maintenance and CapEx) and non-cost related rate drivers (i.e., retail sales, peak demand, and customers). These were then used as inputs to a pro-forma utility financial model (FINDER) that estimated the growth in retail electric rates between 2020 and 2030 for a prototypical vertically-integrated investor-owned utility in the United States. The analysis produced the following results: 1. Assuming average growth rates in all rate drivers, future retail rate growth is driven by sizable increases in all CapEx costs, where fuel and purchased power costs are replaced by generation CapEx as the largest rate component between 2020 and 2030. 2. Growth in sales/peak demand/customers, generation CapEx costs, and fuel and purchased power (FPP) costs, in isolation, produce the most uncertainty in rate growth. Specifically, a 1% increase in the compound annual growth rate (CAGR) of retail sales, coincident peak demand (CP), and customers (Sales-CP-Cust) results in a 0.88-0.93% decrease in the CAGR of rates, in isolation. However, a 1% increase in the CAGR of generation CapEx budgets results in a 0.07-0.14% increase in the CAGR of rates, while a 1% increase in the CAGR of FPP costs causes a 0.10-0.14% increase in the CAGR of rates, all else being equal. 3. Taking into account the correlation and variability of the growth in all rate drivers jointly, generation CapEx is expected to be both the largest and most uncertain rate component by 2030 (20-25% share of the retail rate). Transmission and distribution CapEx, along with fuel and purchased power costs are each expected to comprise between 12% and 17% of retail rates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

MG-RAVENS: Working Group Update [Slides]

Problem Statement: Users in industry, who are planning the deployment and operation of microgrids, face a multi-domain problem that requires multiple engineering tools to solve; Existing tools lack interoperability, which requires tedious recreation and conversion of equipment models, and creates opportunities for errors in translation or through inconsistent assumptions; Microgrid modelers waste time reinventing the wheel because it is challenging to reuse existing distribution, load, generation, power flow, optimization models for new use cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Combined Heat and Power Market Potential for Opportunity Fuels

This report estimates the potential for opportunity fuel combined heat and power (CHP) applications in the United States, and provides estimates for the technical and economic market potential compared to those included in an earlier report. An opportunity fuel is any type of fuel that is not widely used when compared to traditional fossil fuels. Opportunity fuels primarily consist of biomass fuels, industrial waste products and fossil fuel derivatives. These fuels have the potential to be an economically viable source of power generation in various CHP applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

E1 Illumination of a Substation Protection System

Sandia’s CRADA partnership with EPRI for calendar year 2017 helped further the technical basis of understanding on the effects to the electric power distribution system infrastructure (i.e., the grid), from a high altitude electromagnetic pulse (HEMP) event. This joint effort yielded evaluation of multiple SCADA relays to very high transient electric fields that were comparable in frequency content to a HEMP event, in multiple equipment configurations that are representative to how the units are fielded and utilized. Both Sandia and EPRI benefit from the data collected. Sandia directly benefits the LDRD effort to address EMP on electrical system infrastructures. EPRI direct benefits the stakeholders they are representing in the power industry. Stakeholders are power generation and distribution companies and equipment manufactures. The results would be used for product improvement and EMP hardening as well as informing the customer base of potential shortfalls of the product to a HEMP event.To better understand the impacts of HEMP on supervisory control and data acquisition (SCADA) systems in typical use at bulk power facilities, Sandia and EPRI has brought together their unique skills and equipment/facilities. EPRI, representing the stakeholders of the power industry, brings technical experience of how supervisory control and data acquisition (SCADA) systems are typically fielded and utilized in real-world applications. With this, EPRI will provides field hardware relating to the SCADA systems. Sandia National Laboratories (Sandia) provides unique test facilities (Sandia’s Electromagnetic Environment Simulator (EMES) and Large Gigahertz Transverse Electromagnetic (GTEM)) and test equipment capabilities to evaluate the SCADA systems to a simulated HEMP event. Sandia brings hands on experience of conducting susceptibility and vulnerability assessments for critical components and systems to high power incident HEMP illumination.

Molina, Luis↗
DOE OSTI2018

Optimal self-healing strategy for microgrid islanding

Renewable resource based microgrids provide reliable and cost-effective electricity with low carbon emissions. The flexibility of operating in grid-connected or islanded modes enables a microgrid to serve loads reliably. In the case of unexpected events happening to the main grid, the microgrid will isolate itself and operate in islanded mode to prevent any adversary impacts. The availability of renewable generation in the microgrid has significant impacts on the islanding strategy and different scenarios need to be considered. This study proposes a comprehensive microgrid self-healing strategy under different circumstances. The proposed strategy encompasses generation re-dispatch, network reconfiguration, and load shedding. The microgrid self-healing problem is formulated as a mixed-integer quadratic programming problem, which provides a globally optimal solution to facilitate smooth islanding of the microgrid. A modified Consortium for Electric Reliability Technology Solutions microgrid is used to conduct simulation under various scenarios. The simulation results demonstrate the efficiency of the proposed self-healing strategy in minimising costs of load shedding and generation with optimal switching actions.

optimal self-healing strategy↗
DOE OSTI2015

Interconnection Assessment Methodology and Cost Benefit Analysis for High-Penetration PV Deployment in the Arizona Public Service System

In an effort to better understand the impacts of high penetrations of photovoltaic (PV) generators on distribution systems, Arizona Public Service and its partners completed a multi-year project to develop the tools and knowledge base needed to safely and reliably integrate high penetrations of utility- and residential-scale PV. Building upon the APS Community Power Project-Flagstaff Pilot, this project investigates the impact of PV on a representative feeder in northeast Flagstaff. To quantify and catalog the effects of the estimated 1.3 MW of PV that will be installed on the feeder (both smaller units at homes and large, centrally located systems), high-speed weather and electrical data acquisition systems and digital 'smart' meters were designed and installed to facilitate monitoring and to build and validate comprehensive, high-resolution models of the distribution system. These models are being developed to analyze the impacts of PV on distribution circuit protection systems (including coordination and anti-islanding), predict voltage regulation and phase balance issues, and develop volt/VAr control schemes. This paper continues from a paper presented at the 2014 IEEE PVSC conference that described feeder model evaluation and high penetration advanced scenario analysis, specifically feeder reconfiguration. This paper presents results from Phase 5 of the project. Specifically, the paper discusses tool automation; interconnection assessment methodology and cost benefit analysis.

14 SOLAR ENERGY↗
DOE OSTI2017

Transient Stability Assessment of Power Systems With Uncertain Renewable Generation: Preprint

The transient stability of a power system depends heavily on its operational state at the moment of a fault. In systems where the penetration of renewable generation is significant, the dispatch of the conventional fleet of synchronous generators is uncertain at the time of dynamic security analysis. Hence, the assessment of transient stability requires the solution of a system of nonlinear ordinary differential equations with unknown initial conditions and inputs. To this end, we set forth a computational framework that relies on Taylor polynomials, where variables are associated with the level of renewable generation. This paper describes the details of the method and illustrates its application on a nine-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only when the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Stochastic Microgrid Scheduling With Chance‐Constrained Resilience Consideration

Traditionally, it is assumed that microgrids transition seamlessly from grid‐connected operation to islanded mode in the event of sudden main grid outages. In reality, the islanding process, especially unintentional islanding, is rarely seamless. Instead, it is subject to voltage and frequency fluctuations caused by the instantaneous disconnection of the point of common coupling (PCC) switch, variations in loads and renewable generation output and even the protection tripping of distributed energy resources (DERs). To mitigate these fluctuations and facilitate a smooth islanding process, we propose a stochastic microgrid scheduling model that incorporates chance‐constrained resilience measures. Specifically, the resilience measure is defined as the probability of successful islanding (PSI), that is, the probability that a microgrid can mitigate the generation‐demand imbalance caused by the disconnection of the PCC switch, variations in load and renewable generation and DER tripping. This measure is modelled using chance constraints. Unlike existing reliability and resilience indices, which typically neglect the possibility of microgrid/DER failure under extreme events and assume their survival while primarily focussing on reducing impact duration or magnitude, the proposed PSI‐based framework explicitly addresses microgrid and DER survival during the islanding transition. The formulated nonlinear chance constraints are approximated using a multiinterval approach and equivalently represented as a mixed‐integer linear programming (MILP) formulation. Case study results validate the proposed method, showing that the PSI estimation error is reduced to less than 8%, compared to approximately 28% with existing methods. Various sensitivity analyses on the DER tripping rate and PSI settings were performed to validate the robustness of the proposed method. In particular, the necessity of accounting for DER tripping in the PSI calculation was demonstrated.

chance constrained optimization↗
DOE OSTI2020

Predictive-Maintenance Practices: For Operational Safety of Battery Energy Storage Systems

Changes in the Demand Profile and a growing role for renewable and distributed generation are leading to rapid evolution in the electric grid. These changes are beginning to considerably strain the transmission and distribution infrastructure. Utilities are increasingly recognizing that the integration of energy storage in the grid infrastructure will help manage intermittency and improve grid reliability. This recognition, coupled with the proliferation of state-level renewable portfolio standards and rapidly declining lithium-ion (Li-ion) battery costs, has led to a surge in the deployment of battery energy storage systems (BESSs). Additionally, although BESSs represented less than 1% of grid-scale energy storage in the United States in 2019, they are the preferred technology to meet growing demand because they are modular, scalable, and easy to deploy across diverse use cases and geographic locations.

25 ENERGY STORAGE↗