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 generation 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 was tested in a demonstration case. The case chosen is an optimization driven by profitability for a system comprised of a nuclear plant and a hydrogen production industrial process. This type of economic analysis is aimed at assessing 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 intended to show the flexibility of the developed framework, e.g. for the evaluation of retrofitting projects of already existing plants.

Economic Data and Modeling Support for the Two Regional Case Studies: Nuclear-Renewable Hybrid Energy Systems: Analysis of Technical & Economic Issues

“…The LCOE is then computed so that the resulting Net Present Value (NPV) of all cash flows is zero. The cash flow calculation is described in detail in Epiney et al (2017). Positive cash flows, such as the revenue resulting from hydrogen sale, have the effect to reduce LCOE.…”

“…The ARMA is trained on a demand with a mean of about 1.1 GW. Once this demand is normalized, it is multiplied by an approximate mean demand, called "scaling demand" in Epiney et al (2017), which is used by the dispatch module. Therefore, a demand having an approximate mean demand of 100 MWe has a mean value of about 110 MWe.…”

HERON as a Tool for LWR Market Interaction in a Deregulated Market

“…] to construct an analysis framework using RAVEN for systems of energy producers and consumers in a method that allows statistically-meaningful decisions to be made with regards to the economic viability of energy grid portfolios and configurations. The results of this effort have shown value both in a theoretical sense [1,2,4,5,7,[9][10][11][12] as well as for specific applications when paired with industrial partners in the energy space [3,16]. However, the development and maintenance of these RAVEN workflows has been burdensome, and often involves complicated copy-and-modify operations that significantly slow the user experience.…”

“…HERON also requires use of the RAVEN plugin CashFlow [12], a utility for economic analysis. As of this writing, CashFlow is an officially supported plugin for RAVEN and is installed standard when plugins are included in RAVEN installation.…”

“…After specifying the activity of a component, an "economics" node is used to declare the economic interactions of the component. These are specified by defining "CashFlow" nodes, corollaries to those found in the CashFlow [12] plugin of RAVEN. Each CashFlow node is specified as a "one-time" type or "repeating" type, corresponding to capital investments and regular transactions respectively.…”