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Increased electricity production from renewable energy resources coupled with low natural gas prices has caused existing light-water reactors (LWRs) to experience ever-diminishing returns from the electricity market. Via a partnership among Idaho National Laboratory (INL), The National Renewable Energy Laboratory (NREL), Argonne National Laboratory (ANL), Exelon, and Fuel Cell Energy, a technoeconomic analysis of the viability of retrofitting existing pressurized water reactors (PWRs) to produce hydrogen (H2) via high-temperature steam electrolysis (HTSE) has been conducted. Such integration would allow nuclear facilities to expand into additional markets that may be more profitable in the long term.To accommodate such an integration, a detailed analysis of HTSE process operation, requirements, and flexibility was conducted. The technical analysis includes proposed nuclear system control scheme modifications to allow dynamic operation of the HTSE via both thermal and electrical connection to the nuclear plant. High-fidelity Modelica simulations showcase the viability of such control schemes. However, due to limited knowledge of solid oxide fuel cell (SOFC) stack degradation due to thermal gradients, thermal cycling of the HTSE was not included. Therefore, the control schemes proposed are only utilized to re-distribute steam at startup, and only the portion of electricity utilized in the electrolyzers is cycled.From the detailed analysis of the nuclear integration and the HTSE process design, a comprehensive cost estimation was conducted in the APEA and H2A models to elucidate capital and operational costs associated with the production, compression, and distribution of hydrogen from a nuclear facility. Alongside this costing analysis, market analyses were conducted by NREL and ANL on the electric and hydrogen markets, respectively, in the PJM interconnect.Utilizing the electricity data market projections in the PJM interconnect from NREL and hydrogen demand/pricing projections from ANL, a five-variable sweep over component capacities, discount rates, and hydrogen pricing was completed using the stochastic framework RAVEN (Risk Analysis Virtual ENvironment) through its resource dispatch plugin HERON (Heuristic Energy Resource Optimization Network). Each combination of variables was evaluated over a seventeen-year timespan, from 2026-2042 (inclusive), to determine the most economically advantageous solution. Following the five-variable sweep, an optimization was conducted to establish the best sweep point to determine optimal component sizing and setpoints.Results suggest positive gain is achievable at all projected hydrogen market pricing levels and at all discount rates. However, exact component sizing and net returns vary based on these values, and if incorrect sizing is selected, major net losses can occur. The optimal result occurred with set points as follows: high hydrogen prices, the largest possible HTSE unit in the sweep set at 7.47 kg/sec (645.4 tpd), a contractual hydrogen market agreement 7.29 kg/sec (...

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This document reports the application of the Nuclear-Renewable Hybrid Energy System (N-R HES) software framework to a case study conducted in collaboration with Arizona Public Service (APS). The study is a work in progress; this report presents a detailed description of the current model inputs and the corresponding results.APS is currently anticipating several operational challenges: First, APS is coping with the rapid growth of Variable Renewable Energy (VRE) sources on the grid in the APS service region. To mitigate the resulting demand volatility, APS is seeking to add more baseload. The second challenge to APS is that the cooling water acquisition contract with the Sub Regional Operating Group (SROG) will expire soon and a renewal is only available for a significantly higher price of water. An opportunity for less expensive water may be to pump brackish water from the regional ground water. One caveat is that the salinity of the brackish water is so high that it could, depending on the percentage used, need additional treatment via an on-site Reverse Osmosis (RO) desalination plant. The RO plant would help resolve both problems APS is facing, i.e. increasing the baseload to help mitigate VRE-induced demand volatility and, in addition, the clean water produced can be used by APS for plant cooling to lower their water acquisition cost.The analysis in this report considers three scenarios: (1) The status quo, where all cooling water is purchased from the SROG and no RO is built (CASE 0); (2) A case in which one RO is built on-site to treat the blend of SROG and brackish water (CASE 1); and (3) A case in which two ROs are built, one on-site and another one close to the brackish water well (CASE 2). The second RO could produce clean (potable) water that can be sold to generate additional revenue for APS. The analysis evaluates the differential Net Present Value (NPV) between the scenarios.To model the three APS cases, additional functionality for the N-R HES software framework was needed. In particular, the RAVEN CashFlow plugin was updated to add more flexibility in project and component definitions and the synthetic time history generator was updated to include the possibility to correlate the noise portion of different signals after Fourier de-trending. The report also includes description of how the reduced order RO model was derived from a high fidelity Modelica model. Furthermore, the physical models used for water flows and chemistry, as well as the economic models detailing the assumptions made and data used, are described.The report shows that the recently implemented correlated Auto-Regressive, Moving Average model (FVARMA) capability is working as intended. However, after removing the long-term trends and correlations by Fourier de-trending, no correlation could be found between the demand and the rooftop solar photovoltaics (rPV) or between the demand and the hub price in the stochastic portion of the signal, although it is suspected that such correlations exist and are important drivers for the econo...

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.

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As the contribution of renewable energy grows in electricity markets, the complexity of the energy mix required to meet demand grows, likewise the need for robust simulation techniques. While decades of wind, solar, and demand profiles can sometimes be obtained, this is too few samples to provide a statistically meaningful analysis of a system with baseload, peaker, and renewable generation. To demonstrate the viability of an energy mix, many thousands of samples are needed. Synthetic time series generation presents itself as a suitable methodology to meet this need.For a synthetic time series to be statistically viable, several conditions must be met. The series generator must produce independent, identically-distributed samples, each having the same fundamental properties as the original signal without duplicating it exactly. One approach for such a generator is training a surrogate model using Fourier series decomposition for seasonal patterns and Auto-Regressive Moving Average models (ARMA) to describe time-correlated statistical noise about the seasonal patterns. When combined, the Fourier plus ARMA (FARMA) model has been shown to provide an infinite set of independent, identically-distributed sample time series with the same statistical properties as the original data [1].When considering an energy mix with renewable electricity production, several time series of energy, grid, and weather measurements are needed for each synthetic year modeled to statistically comprehend the efficiency of any given energy mix. This includes measurements of solar exposure, air temperature, wind velocity, and electricity demand. These cannot be considered independent series in a given synthetic year; for example, in summer months demand may be higher when solar exposure and air temperature are high and wind velocity is low. To capture and reproduce the correlations that might exist in the measured histories, the ARMA can further be extended as a Vector ARMA (VARMA). In the VARMA algorithm, covariance in statistical noise is captured both within a history as part of the autoregressive moving average, and with respect to the other variables in the time series.