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Title: Report on the Economic Optimization of a Demonstration Case for a Static N-R HES Configuration using RAVEN

Abstract

The synthetic time history generation algorithm in RAVEN has been extensively tested and used to perform qualitative assessments of the statistical characteristics of the net demand vs. demand for different values of the penetration of wind generation. Reported results highlight the need to consider the impact of variable renewable on the electricity net demand profile, due to the increase in its volatility with increased wind penetration. This analysis provides a strong foundation for the modeling and simulation needs expressed under the "Nuclear-Renewable Hybrid Energy Systems" (NRHES) project. Following this confirmative analysis the entire stochastic optimization framework has been tested in a demo case. The case chosen is an optimization driven by profitability for a system composed by a nuclear plant and a hydrogen production industrial process. This type of economical analysis is aimed to assess the capability of a hybrid system to penetrate the current energy market. In this sense it differs from the approach suggested under NRHES, where a cost minimization approach is suggested. This analysis is instead aimed to show the flexibility of the developed framework, e.g. for the evaluation of retrofitting projects of already existing plants. The system considered is subject to variable prices of electricity, whichmore » are simulated using the algorithm for the production of synthetic time histories. The physical system is considered without inertia so that it can be replaced by dispatching rules based on the highest marginal profit. Two optimization problems have been considered, for the first the size of the nuclear power plant is fixed and the optimization variable is the capacity of the hydrogen production plant. For the second, both plant sizes are optimization parameters while the overall capital expenditure is caped. The economical figures of merits considered are the Internal Rate of Return (IRR) and/or Profitability Index (PI) for the first one (since no cap is placed on the investment size) and Net Present Value (NPV) and/or IRR for the second one where the capital expenditure is constrained. Economic input data concerning capital costs, size-scaling factors, and operational costs for both plants have been collected from literature in order to achieve a simulation as realistic as possible. More work will be needed to obtain even more realistic numbers, possibly from industrial partners, and considering market elasticity for the hydrogen market. In order to perform the optimization of the cases described above, the economical external RAVEN module (CashFlow) for financial analysis has been improved in flexibility and generality. Some improvements have also been made to the optimization algorithm for a more efficient treatment of the boundary constrains. In conclusion three major results have been obtained: a) collection of the economical parameters characterizing the nuclear power plants and hydrogen production plants, b) extension of the financial analysis module (CashFlow) to drive the calculation of all needed cash flow streams and c) the testing of the full framework in a nearly final configuration.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1483621
Report Number(s):
INL/EXT-17-41915-Rev000
DOE Contract Number:  
AC07-05ID14517
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; RAVEN; NRHES; Initial Rate of Return; Net Present Value; Profitability Index

Citation Formats

Epiney, Aaron, Rabiti, Cristian, Alfonsi, Andrea, Talbot, Paul, and Ganda, Francesco. Report on the Economic Optimization of a Demonstration Case for a Static N-R HES Configuration using RAVEN. United States: N. p., 2017. Web. doi:10.2172/1483621.
Epiney, Aaron, Rabiti, Cristian, Alfonsi, Andrea, Talbot, Paul, & Ganda, Francesco. Report on the Economic Optimization of a Demonstration Case for a Static N-R HES Configuration using RAVEN. United States. https://doi.org/10.2172/1483621
Epiney, Aaron, Rabiti, Cristian, Alfonsi, Andrea, Talbot, Paul, and Ganda, Francesco. Sat . "Report on the Economic Optimization of a Demonstration Case for a Static N-R HES Configuration using RAVEN". United States. https://doi.org/10.2172/1483621. https://www.osti.gov/servlets/purl/1483621.
@article{osti_1483621,
title = {Report on the Economic Optimization of a Demonstration Case for a Static N-R HES Configuration using RAVEN},
author = {Epiney, Aaron and Rabiti, Cristian and Alfonsi, Andrea and Talbot, Paul and Ganda, Francesco},
abstractNote = {The synthetic time history generation algorithm in RAVEN has been extensively tested and used to perform qualitative assessments of the statistical characteristics of the net demand vs. demand for different values of the penetration of wind generation. Reported results highlight the need to consider the impact of variable renewable on the electricity net demand profile, due to the increase in its volatility with increased wind penetration. This analysis provides a strong foundation for the modeling and simulation needs expressed under the "Nuclear-Renewable Hybrid Energy Systems" (NRHES) project. Following this confirmative analysis the entire stochastic optimization framework has been tested in a demo case. The case chosen is an optimization driven by profitability for a system composed by a nuclear plant and a hydrogen production industrial process. This type of economical analysis is aimed to assess the capability of a hybrid system to penetrate the current energy market. In this sense it differs from the approach suggested under NRHES, where a cost minimization approach is suggested. This analysis is instead aimed to show the flexibility of the developed framework, e.g. for the evaluation of retrofitting projects of already existing plants. The system considered is subject to variable prices of electricity, which are simulated using the algorithm for the production of synthetic time histories. The physical system is considered without inertia so that it can be replaced by dispatching rules based on the highest marginal profit. Two optimization problems have been considered, for the first the size of the nuclear power plant is fixed and the optimization variable is the capacity of the hydrogen production plant. For the second, both plant sizes are optimization parameters while the overall capital expenditure is caped. The economical figures of merits considered are the Internal Rate of Return (IRR) and/or Profitability Index (PI) for the first one (since no cap is placed on the investment size) and Net Present Value (NPV) and/or IRR for the second one where the capital expenditure is constrained. Economic input data concerning capital costs, size-scaling factors, and operational costs for both plants have been collected from literature in order to achieve a simulation as realistic as possible. More work will be needed to obtain even more realistic numbers, possibly from industrial partners, and considering market elasticity for the hydrogen market. In order to perform the optimization of the cases described above, the economical external RAVEN module (CashFlow) for financial analysis has been improved in flexibility and generality. Some improvements have also been made to the optimization algorithm for a more efficient treatment of the boundary constrains. In conclusion three major results have been obtained: a) collection of the economical parameters characterizing the nuclear power plants and hydrogen production plants, b) extension of the financial analysis module (CashFlow) to drive the calculation of all needed cash flow streams and c) the testing of the full framework in a nearly final configuration.},
doi = {10.2172/1483621},
url = {https://www.osti.gov/biblio/1483621}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {4}
}