RAVEN has been developed in a highly modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and, as already mentioned, coupling with any system code.

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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Hybrid energy systems consisting of multiple energy inputs and multiple energy outputs have been proposed to be an effective element to enable ever increasing penetration of clean energy. In order to better understand the dynamic and probabilistic behavior of hybrid energy systems, this paper proposes a model combining Fourier series and autoregressive moving average (ARMA) to characterize historical weather measurements and to generate synthetic weather (e.g., wind speed) data. In particular, Fourier series is used to characterize the seasonal trend in historical data, while ARMA is applied to capture the autocorrelation in residue time series (e.g., measurements with seasonal trends subtracted). The generated synthetic wind speed data is then utilized to perform probabilistic analysis of a particular hybrid energy system configuration, which consists of nuclear power plant, wind farm, battery storage, natural gas boiler, and chemical plant. Requirements on component ramping rate, economic and environmental impacts of hybrid energy systems, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated.

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The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates. In order to evaluate the impact of these two factors on the safety of the plant, the Risk Informed Safety Margin Characterization (RISMC) project aims to provide insight to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This report focuses, in particular, on the impact of power uprate on the safety margin of a boiling water reactor. The case study considered is a loss of off-site power followed by the possible loss of all diesel generators, i.e., a station black-out (SBO) event. Analysis is performed by using a combination of thermo-hydraulic codes and a stochastic analysis tool currently under development at the Idaho National Laboratory, i.e. RAVEN.Starting from an understanding of possible SBO accident sequences for a typical boiling water reactor, we built the input file for the mechanistic thermal-hydraulics code that models system dynamics under SBO conditions. We also interfaced RAVEN with these codes so that it would be possible to run multiple RELAP simulation runs by changing specific portions of the input files. We both employed classical statistical tools, i.e. Monte-Carlo, and more advanced machine learning based algorithms to perform uncertainty quantification in order to quantify changes in system performance and limitations as a consequence of power uprate. We also employed advanced data analysis and visualization tools that helped us to correlate simulation outcomes such as maximum core temperature with a set of input uncertain parameters.Results obtained give a detailed investigation of the issues associated with a plant power uprate including the effects of SBO accident scenarios. We were able to quantify how the timing of specific events was impacted by a higher nominal reactor core power. Such safety insights can provide useful information to the decision makers to perform risk-informed margins management.ii CONTENTS

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