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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Stand-Alone and Hybrid Electric Thermal Energy Storage in the System Advisor Model

This project developed stand-alone electric-thermal energy storage (ETES), stand-alone pumped thermal energy storage (PTES), and hybrid molten-salt power tower (MSPT)-ETES performance and dispatch optimization techno-economic models. The models are available to the public through the System Advisor Model (SAM) software, scripting, and as open-source code. We compared results of the dispatch model to PLEXOS dispatch of a similar generator using the same initial grid pricing signal and found our dispatch model performed well, but closer agreement between the models was limited by the inherent differences between price-taker and unit commitment models. Nevertheless, the price-taker models developed in this project are useful to analyze proposed ETES and PTES technologies because they provide more detailed system and component models, solve several orders of magnitude faster, and are available as free open-source software. The model results represent the most optimistic returns considering grid arbitrage from the input electricity pricing, so the financial results can be applied as a feasibility stage-gate. We also submitted a journal draft paper that describes the ETES dispatch model methodology and demonstrates model functionality. An accepted journal article will serve as peer-reviewed documentation for the models, along with the open-source code, SAM help-menu content, and eventually this final project report.

14 SOLAR ENERGY↗
DOE OSTI2023

Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operations (SUMMER-GO): Project Final Report

The Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operation (SUMMER-GO) project was recently completed through a collaboration among the National Renewable Energy Laboratory, Maxar, the Electric Reliability Council of Texas (ERCOT), the University of Texas at Dallas, the University of California Berkeley, and the University of Colorado Boulder. The project made significant advances in probabilistic solar power forecasting, both through the development of Bayesian model averaging methods for ensemble forecasting and in bringing these and other advancements into practice with Maxar's delivery of operational forecasts to ERCOT. In addition to creating more reliable solar power forecasts, the project developed methods for their utilization in power system operations. These include the development of risk-aware unit commitment and economic dispatch algorithms and methods to reformulate probabilistic forecasts to be used in these power system operational models. Dynamic power system reserve methods were also developed, which have been shown in silico to create economic savings and reliability improvements on an ERCOT-like system as well as financial savings in the ERCOT system through more granular consideration of the uncertainty associated with solar power forecasts. Finally, a situational awareness tool to help grid operators better understand solar power forecast uncertainty in daily operations was developed and extensively vetted.

14 SOLAR ENERGY↗
DOE OSTI2023

Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids: Data Plan - Princeton

We collected and cleaned the synthetic grid data produced by NREL for the Texas and New York synthetic grids. We developed a high dimensional joint stochastic model for load at the zone level, and solar and wind power productions at the asset level, capturing the spatial and temporal dependencies between all the variables, and demonstrated how such a model could be fitted to historical data. We designed and implemented a simulation engine which can produce Monte Carlo scenarios for the hourly day-ahead values of load, and solar and wind power productions at the spatial and temporal resolutions of the historical data used to fit the model. Finally we developed an open-source Python package which can, from an input grid model, efficiently use forecasts and large numbers of Monte Carlo scenarios to provide unit commitment and economic dispatch for each of these scenarios. The high dimensional stochastic model and the subsequent Monte Carlo simulation engine were implemented in the package PGscen and the corresponding UC and ED optimization programs in the package Vatic.

14 SOLAR ENERGY↗
DOE OSTI2024

Bid-in Price-Sensitive Load: A Comparison with Fixed Load in a Realistic Test System

Bulk power systems operations, in both simulation and practice, are often built on the principle of minimizing production costs, which assumes to first order that demand is fixed and that the only degrees of freedom for balancing generation and load are supply-side unit commitment and dispatch decisions. The purpose of this report is to interrogate the value of welfare-maximizing markets that not only physically balance demand and supply but also ensure that market outcomes provide consumer value by directly maximizing welfare, defined as the value of consumption minus production costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

IM3 GO WEST Parameter Search Dataset

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It is developed to address weather and water dynamics, and associated vulnerabilities in this bulk power system. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. Therefore, GO WEST can help researchers to strike a balance between model fidelity (i.e. accuracy) and computational complexity (i.e. runtime). This dataset includes model inputs and outputs from 600 model versions for each 2019, 2020, and 2021. The folder naming convention is as follows: Exp{Number of Nodes}_{Mathematical Formulation}_{Transmission Line Limit Scaling Factor in MW}_{Hurdle Rate Scaling Factor in %}_{Year}. Linear programming is designated with "simple" label whereas mixed-integer linear programming is designated with "coal" label. For example, "Exp100_simple_1000_50_2019" folder contains inputs and outputs from 2019 model version with 100 nodes, linear programming, +1000 MW transmission line limit scaling factor, and +50% hurdle rate scaling factor. GO WEST GitHub repository hosts all raw datasets, processing scripts, and model scripts. Please refer to the README file for a detailed description of the included files.

Economics↗
DOE OSTI2024

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗
DOE OSTI2025

IM3 Projected U.S. Western Interconnection Grid Stress Dataset

This dataset provides projected grid stress and reliability results (including all model inputs and outputs from GO WEST and TEP) for Integrated Multisector, Multiscale Modeling (IM3) Phase 2 simulations across eight different scenarios for the U.S. Western Interconnection through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States from a set of Thermodynamic Global Warming (TGW) simulations. These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 GO WEST is an open-source power grid modeling framework for the U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BAs) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of the U.S. Western Interconnection (ACTIVSg10k). Users can select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on the GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within the GO WEST framework. The TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. In order to use TEP model, users need to create scenarios with the GO WEST framework. Please refer to README file for a detailed description of the dataset including individual files and references.

Capacity Expansion Model↗
DOE OSTI2018

Preliminary Findings of the South Africa Power System Capacity Expansion and Operational Modelling Study: Preprint

Wind and solar power contract prices have recently become cheaper than many conventional new-build alternatives in South Africa and trends suggest a continued increase in the share of variable renewable energy (vRE) on South Africa's power system with coal technology seeing the greatest reduction in capacity, see 'Figure 6: Percentage share by Installed Capacity (MW)' in [1]. Hence it is essential to perform a state-of-the-art grid integration study examining the effects of these high penetrations of vRE on South Africa's power system. Under the 21st Century Power Partnership (21CPP), funded by the U.S. Department of Energy, the National Renewable Energy Laboratory (NREL) has significantly augmented existing models of the South African power system to investigate future vRE scenarios. NREL, in collaboration with Eskom's Planning Department, further developed, tested and ran a combined capacity expansion and operational model of the South African power system including spatially disaggregated detail and geographical representation of system resources. New software to visualize and interpret modelling outputs has been developed, and scenario analysis of stepwise vRE build targets reveals new insight into associated planning and operational impacts and costs. The model, built using PLEXOS, is split into two components, firstly a capacity expansion model and secondly a unit commitment and economic dispatch model. The capacity expansion model optimizes new generation decisions to achieve the lowest cost, with a full understanding of capital cost and an approximated understanding of operational costs. The operational model has a greater set of detailed operational constraints and is run at daily resolutions. Both are run from 2017 through 2050. This investigation suggests that running both models in tandem may be the most effective means to plan the least cost South African power system as build plans seen to be more expensive than optimal by the capacity expansion model can produce greater operational cost savings seen only in the operational model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Examining the Net Revenue and Downstream Flow Impact Trade-Offs for a Network of Cascading, Small-Scale Hydropower Facilities: Preprint

In this work, we used a price-taker model to investigate the trade-offs between net revenue and downstream flow impacts for a network of small, cascading hydro facilities. The network consisted of 36 facilities, each with a small amount of local storage (between 2 and 45 minutes). Generator sizes ranged between 0.5 and 1 MW, nominal, and the total capacity of the network was 33.5 MW. We used a multi-integer linear programing model to maximize the net revenue of the combined network subject to operating and environmental constraints. Net revenue optimizations relied on historic price data, and dry, typical, and wet years were studied to help ensure robustness. Energy and ancillary service sales were included in the net revenue calculations, and both unit commitment and dispatch simulations were performed. We found that limiting the downstream flows to ±50% of the river’s natural flows had a negligible impact on net revenues (<1% reduction), irrespective of hydrologic conditions, and even when downstream flows were limited to ±5%, net revenues were only impacted by 4%. These outcomes are significant because they demonstrate how an array of small-scale hydropower facilities can be operated to have minimal impact on natural stream flows—addressing a critical environmental concern.

downstream flow↗
DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗
DOE OSTI2021

A hub and spoke approach to optimizing energy wheeling of renewable resources

The deployment of zero carbon renewable energy sources needs to increase significantly to support the goal of net zero greenhouse gas emissions by 2050. At the same time energy end use needs to decarbonize. This will change both energy supply and energy demand patterns, requiring the energy delivery infrastructure (grid-based transmission circuits) to become increasingly flexible to maintain security of supply everywhere and always. The integration of zero carbon renewable energy requires cross-border and cross energy system coupling and a fit-for-purpose design. Nowadays, energy systems are planned, designed and operated in silos with a strong national focus. However, large-scale offshore wind production needs to be transported to deep inland locations, across country borders. The increased peak generation capacity of renewable energy sources will, at times, significantly exceed demand (Matthew Langholtz, 2020). The traditional solution of continuously reinforcing and extending the electricity grid is not sustainable from a cost and societal perspective. This paper will, however, propose a deterministic approach on how networked (interconnected grid) Points of receipt (POR) to Points of Delivery (POD) can be optimized for wheeling renewable energy resources while minimizing energy cost with a hub and spoke approach. The statistical approach will be done via using existing daily energy market clearing prices, available transmission capacity and firm daily transmission prices in open access energy markets. Renewable energy targets, including specific offshore wind targets, need to be in line with the ramp-up as implied by the Paris Agreement. These targets are required to provide industry with a secure market outlook that allows them to build up supply chains accordingly. Optimizing wheeled energy paths from carbon neutral resources such as renewables make them not only cost competitive on the unit commitment stack, but also more accessible on the dispatch stack to other carbon heavy forms of generation such as coal and natural gas turbines (Matthew Langholtz, 2020). This correlates to maximizing renewable resource inertia (wind, solar, biomass) within an interconnected grid without having to consider additional expansion of resources via land purchases and de-forestation.

Mukherjee, Srijib↗
DOE OSTI2022

System Resilience Benefits of Dual-Fuel Capable Generators

The growing dependency on natural gas (NG) for power generation raises challenges for ensuring the resilience of power systems during extreme cold weather. Dual-fuel capable generators that can switch from burning NG to distillate fuel oil during an NG shortage over one way to alleviate these challenges. In this study, the impacts of gas unavailability on the IEEE 73-bus reliability test system (RTS) with 2019 updated generation mixture are explored. An extension to the standard production cost model formulation of unit commitment and economic dispatch is proposed to allow the representation of dual-fuel capable generators that can switch fuels between NG and distillate oil with a specified oil tank capacity and tank refueling capability. The operation of the system under gas unavailability with 0%, 25%, 50%, 75%, and 100% of combined cycle and combustion turbine generators as dual-fuel capable with a one-day supply of fuel oil are simulated using PLEXOS, a production cost model. The dual-fuel generator performance, unserved energy, and system costs are fully assessed.

dual-fuel capable generator↗
DOE OSTI2022

Fast simultaneous feasibility testing and applications for electrical power grids

Fast simultaneous feasibility testing (SFT) for management of an electrical power grid is achieved through various innovations. The computation problem relates to evaluation of candidate solutions for external power flows into a power grid, with respect to predetermined constraints and contingencies. Storage and computations are reduced by formulating the problem in terms of transactional nodes (e.g. third party connections for generators and loads) instead of the larger number of bus nodes. Further advantages are achieved by precomputing matrices that can be reused across multiple SFT invocations, organizing matrices and operations to reduce storage and computation, and eliminating branches that have no contingency violations. The fast SFT further enables new applications for integrating SFT with a Security-Constrained Unit Commitment (SCUC) optimizer for efficient single pass solutions; operating SFT as a service to multiple SCUC solvers; or integration with Security-Constrained Economic Dispatch (SCED) optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Improved Evaluation of Large Network Matrices for Linear Power Flow Within Optimization Problems: Preprint

This work discusses methods for evaluating the Power Transfer Distribution Factor (PTDF) and Line Outage Distribution Factor (LODF) matrices by employing sparse linear algebra for large-scale computing applications. These matrices are critical in many power systems applications, such as the Unit Commitment Problem (UC), pre- and post-contingency power flow analysis, and transmission expansion. These matrices are typically dense, which means they require a significant amount of time and memory to be computed for large networks. However, by analyzing the structure of the matrices and their computation method, it is possible to use reduced memory methods based on sparse matrix operations. This paper shows that sparse linear algebra algorithms are faster and require less memory and time than traditional dense approaches. Additionally, we explore the effect of matrix sparsification by eliminating trailing digits on power flow calculations.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A Model for Hybrid Systems for Production Cost Modeling Studies Considering Ancillary Services: Preprint

This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-themeter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.

ancillary services↗
DOE OSTI2024

Impact of Hydrological Data on Power System Operational Studies: Preprint

Hydropower is expected to play an important role in maintaining grid reliability and flexibility as the share of of variable renewable energy increases. While the current hydropower operational models have been studied and used widely, they haven't been updated for decades to meet new performance standards. For example, current steady state and dynamic models often neglect hydrological conditions, which may lead to unrealistic expectations when relying on hydropower for energy and ancillary services. To study this impact, a multi-timescale hydrological model was created by leveraging the National Renewable Energy Laboratory-developed Multi-timescale Integrated Dynamics and Scheduling (MIDAS) tool. Using MIDAS, we compare the impact of considering hydrological conditions in a day-ahead unit commitment (DAUC) schedule on the reduced 240-bus Western Interconnect (WI) test system under winter and summer case studies. We show that neglecting current hydrological conditions of hydropower plants in power system models can lead to an overestimation of hydropower capabilities, which could lead to power balancing issues. For example, power system DAUC simulation results of our reduced test system show that in the case where hydrological conditions are not considered in the model, an approximate 31% overestimation of hydropower capabilities occurs in the summer case and approximately 60% occurs in the winter case compared to what is available. Additionally, results show an underestimation of WI day-ahead power system generation costs by approximately $54M - $80M in the weekly summer scenario and $116M - $126M in the weekly winter scenario. This analysis helps to underscore the importance of considering hydrological data in power system operational studies.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗
DOE OSTI2025

Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios

This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.

De Souza, Reubun [School of Electrical Engineering↗