Aaron Epiney’s research while affiliated with Idaho National Laboratory and other places

Integrated energy systems (IESs) seek to minimize power generating costs in future power grids through the coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, and even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear. If a unit is flexibly operated without respecting limits on materials, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the bounds on power variations (explicit constraints),the solution of the unit dispatch problem needs to meet the limits on the variation of key process variables, including temperature, pressure and flow rate (implicit constraints).The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher–validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., balance of plant, gas turbine, and high-temperature steam electrolysis. Finally, the FARM module provides dedicated tools for visualizing the response of the constrained variables of interest during operational transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an ecological interface design (EID) for IES units.

Integrated energy systems (IES) seek to minimize power generating costs in future power grids through the dynamic coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, i.e., even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear of components. If a unit is flexibly operated without respecting limits on materials and components, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the limits on power variations (explicit constraints), the solution of the unit dispatch problem needs to meet the limits on temperature, pressure and flow rate variations (implicit constraints). The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher-validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., Balance of Plant, Gas Turbine, High-Temperature Steam Electrolysis. Finally, FARM module provides dedicated tools for visualizing the response of the process variables of interest during operation transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an Ecological Interface Design (EID) for IES units.

Decarbonizing electricity primarily from wind and solar resources will likely require other low- or no-carbon alternatives to reliably meet electricity demands. This paper examines the competitiveness of two nuclear energy technologies, large Light Water Reactors (LWRs) and LWR-type Small Modular Reactors (SMRs) under a variety of plausible future scenarios. Furthermore, this paper suggests where nuclear energy should be operated and what performance characteristics make nuclear energy economically viable in locations with extensive additions of intermittent energy sources. Through 24 cases, our modeling of the least-cost grid systems highlights the potential of LWR-thermal energy storage coupling and SMRs in making deep decarbonization technically feasible and affordable. Focusing on the seasonal variations in balancing electricity supply and demand, we also capture their widely varying performance along with costs and grid constraints. The implications of our modeling findings can provide tailored design space for future technology investment decisions.

The objective of this study is to demonstrate and validate the Dynamic Energy Transport and Integration Laboratory (DETAIL) preliminary scaling analysis using Modelica language system-code Dymola. The DETAIL preliminary scaling analysis includes a multisystem integral scaling package between thermal-storage and hydrogen-electrolysis systems. To construct the system of scaled equations, dynamical system scaling (DSS) was applied to all governing laws and closure relations associated with the selected integral system. The existing Dymola thermal-energy distribution system (TEDS) facility and high-temperature steam electrolysis (HTSE) facility models in the Idaho National Laboratory HYBRID repository were used to simulate a test case and a corresponding scaled case for integrated system HYBRID demonstration and validation. The DSS projected data based on the test-case simulations and determined scaling ratios were generated and compared with scaled case simulations. The preliminary scaling analysis performance was evaluated, and scaling distortions were investigated based on data magnitude, sequence, and similarity. The results indicated a necessity to change the normalization method for thermal storage generating optimal operating conditions of 261 kW power and mass flow rate of 6.42 kg/s and the possibility of reselecting governing laws for hydrogen electrolysis to improve scaling predictive properties. To enhance system-scaling similarity for TEDS and HTSE, the requirement for scaling validation via physical-facility demonstration was identified.

Decarbonizing electricity primarily from wind and solar resources will likely require other low-or no-carbon alternatives to reliably meet electricity demands. This paper examines the competitiveness of two nuclear energy technologies, large Light Water Reactors (LWRs) and LWR-type Small Modular Reactors (SMRs) under a variety of plausible future scenarios. Furthermore, this paper suggests where nuclear energy should be operated and what performance characteristics make nuclear energy economically viable in locations with extensive additions of intermittent energy sources. Through 24 cases, our modeling of the least-cost grid systems highlights the potential of LWR-thermal energy storage coupling and SMRs in making deep decarbonization technically feasible and affordable. Focusing on the seasonal variations in balancing electricity supply and demand, we also capture their widely varying performance along with costs and grid constraints. The implications of our modeling findings can provide tailored design space for future technology investment decisions.

This manuscript further develops a recent methodology, denoted by Physics-guided Coverage Mapping (PCM), to support model validation for neutronic depletion calculations. The overarching goal of model validation is to develop confidence in model predictions for the application of interest via fusion of both simulation results and measurements from scaled-down experiments, and whenever possible to improve predictions by explaining the observed discrepancies. This manuscript focuses on the isotopic depletion problem, that is how to improve the predictions of depleted fuel isotopic across the range of expected burnup based on a limited number of post-irradiation measurements. PCM employs an information theoretic approach, capable of directly transferring, i.e., without performing model inversion, biases and their uncertainties from the available measurements to the quantities of interest (QoIs), representing the isotopic concentrations at different burnup values and/or different irradiation spectra. It precludes the need for sensitivity coefficients and only requires forward model executions, and can be applied using non-informative priors, often required by Bayesian-based methods. This is achieved via a mapping kernel relating a number of predictor variables, the concentrations of single or multiple isotopes at certain burnup, to the QoIs, the isotopic concentrations at target burnup such as end of life. Proof-of-principle calculations are demonstrated using both representative PWR and BWR lattice models, where the goal is to employ measurements at given burnup value(s) from one lattice to predict the isotopic concentrations across burnup for the same or the other lattice. Results show 50% to 90% reduction in uncertainties of isotopic concentrations across burnup as compared to the prior uncertainty.

The purpose of this study was to develop a process to convert input signals from one facility into another by reflecting geometric and environmental settings. The Dynamic Energy Transport and Integration Laboratory (DETAIL) is a research facility in development. Its aim is to emulate the daily interactions among power production industry systems and receive real-time data from those systems as inputs. To convert signals and ensure that the temporal sequences and magnitudes reflect laboratory settings, the ability to scale and project data is essential. To demonstrate this ability, Dynamical System Scaling (DSS) and Hierarchical Two-Tiered Scaling (H2TS) (methodologies that enable systems to scale and project or extrapolate data sets to desired environments while conserving the observed behavior based on first principles) were applied to DETAIL’s thermocline thermal storage system in the Thermal Energy Distribution System (TEDS) facility and solid-oxide electrolysis cell in the High Temperature Hydrogen Electrolysis (HTHE) facility. Both thermocline and electrolysis cell systems were successfully scaled, and test cases were conducted to generate a doubly accelerated energy charge and discharge in reference to past experimental data from the facilities. The research results represented a case for the thermocline system that required signals to be accelerated without altering the stored energy. To enhance the quality of the accelerated data, error propagation analyses were conducted on DSS post-processing terms to determine the consequences of raw-data-associated errors.

We explore how the increasing penetration of wind power, and the resulting negative whole electricity price can affect the investment decisions and power mix of a fully-decarbonized grid. Centered on the Electric Reliability Council of Texas (ERCOT), we formulate the suppressed wholesale electricity prices (e.g., wind as a price maker) and optimize the least-cost portfolios in an ERCOT-like grid system in which the large-scale integration of wind power is achieved in the presence of nuclear power and a grid-level energy storage facility. In this research, we utilize the regression coefficient of the ERCOT market estimated by Tsai and Eryilmaz [1] in a Holistic Energy Resource Optimization Network (HERON) environment, a grid modeling toolset developed by the Idaho National Laboratory (INL). Surprisingly, in the short-run, least-cost portfolios that allow over-or under-production with a monetary penalty result in less wind (up to 55%) and more nuclear capacity to prevent negative price spikes. This is in comparison to models optimized with the same setting, but a fixed electricity price. This being the case, we find that the long-run effects are highly uncertain, as penalties associated with the missed demand offset the revenue from electric sales. This in turn implies that the upper limit of wind penetration in the market may be lower than the capacity previously calculated solely factoring in system reliability constraints. Thus, the findings from this research may offer a more feasible economic target ($/kW) for nuclear developers, with the new electricity pricing scheme driving the system-wide economics to preserve the same revenue levels from electricity.