Previous approaches to dispatching nuclear integrated energy systems (NIES) have focused on the profitability and flexibility of these systems to operate on energy grids with highly variable pricing. However, due to the complexity involved in modeling and designing these systems, there has been less emphasis on ensuring that these dispatch strategies are physically achievable. It is imperative to develop methods that allow the system to remain within the desired NIES operating conditions and perform this based on realistic limited forecasted information. This research employs next generation artificial intelligence, namely deep reinforcement learning (DRL), and a dynamic system model written in Modelica to find a safe and profitable dispatch strategy for a solar nuclear hybrid design. The DRL agent is shown to find a novel dispatch strategy that manages both power ramping and power levels while respecting operational limits. This DRL-based dispatch is compared to other dispatching strategies including an optimal design solution from mixed integer linear programming (MILP). It is found that incorporating the physics of such a tightly coupled NIES limits the profitability of the MILP-based dispatch strategy. As a result, the MILP solution overestimates the design’s generated revenue. In contrast, DRL significantly reduces the number of breaches of safe operational conditions during energy arbitrage while maintaining profitability. This work paves the way for a more detailed assessment of NIES profitability and could be used to aid operator decisions on future NIES projects.

Phase change materials (PCMs) are widely used in thermal energy storage (TES) devices due to their high energy density, but their performance is limited by thermal conductivity. Incorporating parallel fins enhances the effective conductivity and alters the role of natural convection in the liquid PCM region. Low-fidelity models can capture key melting features at small fin pitches (<2mm), but their accuracy declines when natural convection becomes dominant. This study experimentally investigates the influence of fin spacing on PCM melting behavior under constant temperature and constant heat flux boundary conditions to quantify the importance of natural convection. Temperature measurements, phase front tracking, and particle image velocimetry (PIV) were used to identify the mechanisms governing TES performance. Results show that increasing heat flux and fin pitch leads to stronger convective currents, with velocities rising from near-zero for a 3-mm fin pitch at 4.9 kW/m2 to 3.5 mm/s for a 12-mm fin pitch at 14.8 kW/m2. Conditions with strong natural convection also exhibited increasingly non-linear base temperature profiles with time, indicating shifts in the dominant heat transfer mechanisms. Experimental results were compared against three models of varying fidelity: (1) a 1-D effective medium approximation (EMA) model assuming homogeneous properties, (2) a 2-D conduction model accounting for fins and PCM but neglecting fluid motion, and (3) a 2-D convection model that considered natural convection effects. The 2-D convection model maintained relatively high accuracy across most conditions, while the 1-D and 2-D conduction models were only reliable when the characteristic Rayleigh number was below 103. These findings provide critical guidance for selecting appropriate modeling approaches in PCM-based thermal storage systems, enabling more accurate models for design and optimization across applications.

This paper considers a topology-optimized primary heat exchanger (PHX) manufactured from silicon carbide (SiC) using additive manufacturing technology for integration into a Gen3 concentrated solar power (CSP) plant, which facilitates heat transfer between particles and supercritical carbon dioxide (sCO(2)). This primary heat exchanger technology provides the possibility of both reducing the cost per conductance in the primary heat exchanger due to the advanced heat transfer surfaces that can be achieved as well as increasing the turbine inlet temperature of the cycle due to the high temperature capability of SiC. Preliminary findings suggest that a 10% decrease in the cost per unit of conductance in the primary heat exchanger will result in a 1 $/MWh decrease in the levelized cost of energy, and the power block has a cost-optimal thermal efficiency of around 47% for a turbine inlet temperature of 680 degrees C. The optimization methods presented in this research have also identified gains in the tradeoff between the cost of the PHX and the thermal performance of the power cycle, further reducing the levelized cost of energy. Performance of the primary heat exchanger - including cost per unit conductance, operating temperature, and lifetime - can be a strong determinant of where capital should be allocated in a concentrated solar power system. Such an indicator is helpful for the future design and techno-economic analysis of these systems. The presented results indicate the importance of carefully considering PHX design in meeting DOE cost targets for concentrated solar power.

The growing use of intermittent renewables in electrical grids increasingly motivates load- following operations as a crucial capability of dispatchable power plants. However, frequent load variations in steam generation equipment can cause premature heat exchanger failure. This paper simulates the dynamic behavior of a high pressure, U-tube/U-shell, salt- to-steam superheater typically found in tower-type concentrating solar power subcritical Rankine cycles. Results focus on responses during load-following and inlet temperature changes. The proposed model is a finite volume method, and thermodynamic and heat transfer properties of both fluids are allowed to vary spatially and temporally. Several flow ramping schemes are investigated, including proportionally equal ramps and proportionally dissimilar ramping, where one fluid reaches its mass flow setpoint faster than the other. Results indicate that salt outlet temperature overshoot can occur if ramp rates are of sufficiently high magnitude, and that U-bend metal temperature rate of change can be approximately 2.5x that observed at either outlet. If proportionally-matched ramping is not possible, ramping steam more slowly than the salt is preferred over the alternative, as cold side and U-bend temperature responses are better regulated. Additionally, mass flowrate and inlet temperature changes are shown to elicit unique responses in the tube bundle metal.

Concentrating Solar Power (CSP) molten-salt central receivers are subject to high, transient incident flux during daily operation. The resulting creep-fatigue damage impacts the receiver’s reliability and restricts the permissible incident flux distribution for a given receiver. This paper aims to reduce CSP plants’ levelized cost of electricity by developing a methodology to predict lifetime and identifies the primary damage mechanism (creep vs fatigue) for any given fluid temperature and temperature gradient. Results are presented in the form of a damage map that serves as a valuable operation guide and design tool. Damage maps can be used to reduce maintenance costs by improving reliability and reduce receiver capital costs by better utilizing the receiver area. FEA simulation and damage modeling of tubes subject to asymmetrical flux conditions is performed in the open-source receiver design tool srlife. Parametric studies are performed over a range of inner tube temperatures and thermal gradients for A230, 316H, 740H, A282, A617, and 800H high temperature alloys. Damage maps are presented for each alloy. A parametric, FEA-based methodology is presented for comparison of fatigue-creep ratios and prediction of tube lifetime based on the critical thermal operating conditions. Fatigue is found to be negligible compared to creep for almost every case. This finding suggests that fatigue effects associated with cloud events are insignificant compared to creep at these high temperature operating conditions. Additionally, lifetime predictions identify thermal conditions where small changes in operating conditions can result in large changes in predicted lifetime.

Concentrating Solar Power plants face challenges in achieving and sustaining high performance levels partially due to complexities in plant operations. This study addresses these challenges by developing a computationally efficient, high-fidelity parabolic trough solar field model capable of emulating CSP plant dynamics for use as an operator training simulator and as a tool for optimizing operation strategies. Leveraging a neural network methodology, the model efficiently computes heat absorbed by heat transfer fluid in a solar field with various receiver conditions. The trained neural network model achieves heat absorption error of 0.3% compared to a detailed model while increasing the simulation speed by a factor of 100. The solar field model is validated with data from the operational Solana Solar Generating Station near Gila Bend, AZ (US), and computes temperatures resulting in a mean absolute error of 2.2 [degrees C] over an entire day including startup and shut down. The model is further validated with respect to net optical efficiency that accounts for time-varying collector defocusing. Lastly, this work concludes with case studies that demonstrate the model's capabilities both as the engine for a training simulator and as an tool for optimizing solar field control strategies.

For nuclear power plants to remain competitive in energy markets increasingly penetrated by variable renewable energy sources, designs that allow flexible operation or incorporate additional revenue streams should be considered. This study models a nuclear reactor decoupled from a supercritical steam Rankine cycle through a two-tank thermal storage system using molten salt as the heat transfer fluid. The model allows steam extraction from the power cycle's low-pressure turbine to provide thermal energy to a thermal desalination facility. The desalination facility likewise includes a two-tank thermal storage system. This study aims to determine the conditions under which thermal storage integrated with nuclear-desalination systems increases economic competitiveness compared to standalone nuclear power plants. We built a mixed-integer linear program that determines optimal dispatch schedules and subsystem sizing of the energy storage components given current price parameters in the literature. We then performed sensitivity analyses to turbine size, thermal storage system cost, and desalinated water price. We found that multi-effect distillation increased the revenue generation of the system beyond standalone conditions except when the price of desalinated water decreased beyond 30% of its nominal 2021 price. We also found that when the turbine is oversized, high-temperature and low-temperature thermal storage is dispatched in a complementary fashion that allows for load-following and continuous distillate production.

Integrated energy systems can improve flexibility on future energy grids with one option being Nuclear-Solar hybrid systems. Integrating solar generated heat from parabolic troughs into the feedwater line allows the plant to alleviate turbine bled steam and transiently power boost 15% above nominal power for a nominal small modular PWR cycle. This article presents a parametric study of the design of such a system and provides a full system dynamic model written in the Modelica language to analyse the dispatch in transient load following operation. The control of the system is presented, and the transient analysis is shown to help inform sizing of the concrete storage and parabolic trough arrays. The trade off in design between improved degrees of power boosting and system nominal efficiency is investigated with the work suggesting that higher steam generator entry temperatures offer improved opportunities of up to 42% for power boosting flexibility.

As energy system design moves to more complex methods of optimisation including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilised to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilising both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. It is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.

Nuclear power is typically deployed as a baseload generator. Increased penetration of variable renewables motivates combining nuclear and renewable technologies into Integrated Energy Systems (IES) to improve dispatchability, component synergies and, through cogeneration, address multiple markets. However, combining multiple energy resources heavily depends on the proper selection of each system’s location and design limitations. In this paper, co-siting options for IES that couple nuclear and concentrating solar power (CSP) with thermal desalination are investigated. A comprehensive siting analysis is performed that utilizes global information survey data to determine possible co-siting options for nuclear and solar thermal generation in the United States. Viable co-siting options are distributed across the Southwestern U.S., with the greatest concentration of siting options in the southern Great Plains, although siting with higher solar direct normal irradiance is possible in other states such as Arizona and New Mexico. Brackish water desalination is also attractive across the southwest U.S. due to high water stress, but for brackish water desalination reverse osmosis (an electricity driven process) is most cost- and energy-efficient, which does not require co-siting with the thermal generator. The most attractive state for nuclear and thermal desalination (which is more attractive when using seawater) is Texas, although other areas may become attractive as water stress increases over the coming decades. Co-siting of all CSP and thermal desalination is challenging as attractive CSP sites are not coastal.

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.

Renewable technologies using solar input have varied electrical power production during periods of low solar irradiance caused by cloud coverage, seasonal changes, and time of day. Nuclear power plants can load follow, but due to low operating costs and high fixed costs, this capability is often not financially appealing. Implementing thermal energy storage (TES) within a synergistic solar and nuclear power cycle allows for storage during low demand periods and increased power production during high demand periods, effectively increasing dispatchability. In this paper, we examine the thermodynamic performance of an integrated system that includes concentrating solar power (CSP) and a lead-cooled fast reactor (LFR). These technologies are selected due to their similar operating temperatures, allowing for utilization of established TES technologies. Both the CSP and LFR system produce thermal power that is sent to a supercritical steam-Rankine cycle (SSRC). The SSRC model is designed to be implemented into a larger integrated energy system (IES) which contains multiple communicating models. The IES generates CSP and LFR heat profiles then utilizes the SSRC to output calculated metrics including power generation, heat rejection, and cycle efficiency.

For the design of a new asphere measuring system, it is necessary to know transmission values for a system consisting of a source, an optional auxiliary filter and an optical filter, on which angle-tuning is performed. To generate these transmission values for different angles of incidence and polarizations a simulation program was created. Input data of the simulation were based on data provided by the manufacturers. Simulation results are presented for a sample of four systems. Simulation was deemed successful and accelerates the design process of the metrology system, since a large range of source and filter combinations can be evaluated swiftly. Limitations of the simulation are discussed as well.