Hybrid Simulation Framework

RESOURCE

Abstract

HYBRID is a modeling toolset to assess the economic viability of Nuclear-Renewable Integrated Energy Systems (N-R IES). The frameworks enabling this toolset are INL’s RAVEN, its CashFlow plugin and the Modelica language. The toolset includes sample RAVEN workflows performing economic assessments. These workflows consist of: generation of stochastic time series and application of probabilistic analysis and optimization algorithms (RAVEN); a library of Modelica models representing the physical behavior of N-R IES; and the CashFlow plugin mapping physical performance to economic performance. The toolset allows assembling existing and new models such as nuclear reactors, renewable energy sources, energy storage, gas turbines, industrial processes, etc. into an N-R IES. The toolset workflows evaluate the dynamics of the N-R IES responding to stochastic conditions (electricity demand, price, etc.) and optimize the dispatch economics as well as N-R IES capacity planning.
Developers:
Epiney, Aaron [1] Rabiti, Cristian [2] Kim, Jong [1] Frick, Konor [1] Talbot, Paul [1] Kinoshita, Robert [1] Tang, Yu [1] Greenwood, Michael [3] Pnciroli, Roberto [4] Alfonsi, Andrea [1]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  2. Idaho National Laboratory
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Argonne National Lab. (ANL), Argonne, IL (United States)
Contributing Organizations:
Research Group: Oak Ridge National Laboratory
Research Group: Argonne National Laboratory
Release Date:
2021-02-09
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
Apache License 2.0
Sponsoring Org.:
Code ID:
53749
Research Org.:
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Country of Origin:
United States
Keywords:
Dispatch optimization; Capacity planning; Nuclear-Renewable Integrated Energy Systems

RESOURCE

Citation Formats

Epiney, Aaron, Rabiti, Cristian, Kim, Jong S., Frick, Konor L., Talbot, Paul W., Kinoshita, Robert A., Tang, Yu, Greenwood, Michael S., Pnciroli, Roberto, and Alfonsi, Andrea. Hybrid Simulation Framework. Computer Software. https://github.com/idaholab/HYBRID. USDOE Office of Nuclear Energy (NE). 09 Feb. 2021. Web. doi:10.11578/dc.20210402.1.
Epiney, Aaron, Rabiti, Cristian, Kim, Jong S., Frick, Konor L., Talbot, Paul W., Kinoshita, Robert A., Tang, Yu, Greenwood, Michael S., Pnciroli, Roberto, & Alfonsi, Andrea. (2021, February 09). Hybrid Simulation Framework. [Computer software]. https://github.com/idaholab/HYBRID. https://doi.org/10.11578/dc.20210402.1.
Epiney, Aaron, Rabiti, Cristian, Kim, Jong S., Frick, Konor L., Talbot, Paul W., Kinoshita, Robert A., Tang, Yu, Greenwood, Michael S., Pnciroli, Roberto, and Alfonsi, Andrea. "Hybrid Simulation Framework." Computer software. February 09, 2021. https://github.com/idaholab/HYBRID. https://doi.org/10.11578/dc.20210402.1.
@misc{ doecode_53749,
title = {Hybrid Simulation Framework},
author = {Epiney, Aaron and Rabiti, Cristian and Kim, Jong S. and Frick, Konor L. and Talbot, Paul W. and Kinoshita, Robert A. and Tang, Yu and Greenwood, Michael S. and Pnciroli, Roberto and Alfonsi, Andrea},
abstractNote = {HYBRID is a modeling toolset to assess the economic viability of Nuclear-Renewable Integrated Energy Systems (N-R IES). The frameworks enabling this toolset are INL’s RAVEN, its CashFlow plugin and the Modelica language. The toolset includes sample RAVEN workflows performing economic assessments. These workflows consist of: generation of stochastic time series and application of probabilistic analysis and optimization algorithms (RAVEN); a library of Modelica models representing the physical behavior of N-R IES; and the CashFlow plugin mapping physical performance to economic performance. The toolset allows assembling existing and new models such as nuclear reactors, renewable energy sources, energy storage, gas turbines, industrial processes, etc. into an N-R IES. The toolset workflows evaluate the dynamics of the N-R IES responding to stochastic conditions (electricity demand, price, etc.) and optimize the dispatch economics as well as N-R IES capacity planning.},
doi = {10.11578/dc.20210402.1},
url = {https://doi.org/10.11578/dc.20210402.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20210402.1}},
year = {2021},
month = {feb}
}