Nuclear Hybrid Energy Systems Regional Studies: West Texas &amp; Northeastern Arizona

García, Humberto E.; Chen, Jun; Kim, Jong‐Suk; McKellar, Michael G.; Deason, Wesley; Vilim, Richard B.; Bragg‐Sitton, Shannon; Boardman, Richard

doi:10.2172/1236837

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Previous reports focused on tightly coupled N-R HESs. Two N-R HES scenarios were initially analyzed by INL to evaluate their dynamic performance characteristics (Garcia 2015). NREL subsequently conducted an assessment of optimal economic configurations and operation of similar N-R HESs (Ruth et al 2016a).…”

Section: Forewordmentioning

confidence: 99%

Generation and Use of Thermal Energy in the Industrial Sector and Opportunities to Reduce its Carbon Emissions

McMillan

Boardman

McKellar

et al. 2016

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water desalination plant when the combination of nuclear and solar power generation exceeds grid demand.The joint analyses by INL and NREL found that nuclear plants can effectively modulate heat between power production and heat use by an industrial consumer. The analyses by NREL indicate the optimal financial per forma occurs when the nuclear reactor is mainly supplying heat to industry. The nuclear reactor may switch to power generation if capacity payments for power production are adequate. These outcomes demonstrate that nuclear and renewable energy can fulfill power generation and thermal duties of the grid and industrial heat users in a complementary manner, but hybridization will depend on the future cost of natural gas power production, and clean energy investment and production incentives. This report quantifies greenhouse gas (GHG) emissions from the industrial sector and identifies opportunities for non-GHG-emitting thermal energy sources to replace the most significant GHG-emitting U.S. industries based on targeted, process-level analysis of industrial heat requirements. The intent is to provide a basis for projecting opportunities for clean energy use. This provides a prospectus for small modular nuclear reactors (including N-R HES), solar industrial process heat, and geothermal energy.

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Section: Forewordmentioning

confidence: 99%

Generation and Use of Thermal Energy in the Industrial Sector and Opportunities to Reduce its Carbon Emissions

McMillan

Boardman

McKellar

et al. 2016

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“…A detailed discussion on developing hybrid process models is provided in a companion report. 22 It is foreseeable that the reliability analysis will also utilize models running on RTDS hardware when the time scale of operational dynamics is needed to address LOLP and ACE. Simplified plant models, if needed, can be generated in PSCAD.…”

Section: Simulation Frameworkmentioning

confidence: 99%

Strategy and gaps for modeling, simulation, and control of hybrid systems

Rabiti

García

Hovsapian

et al. 2015

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This report establishes a strategy for modeling, simulation, and control of candidate hybrid energy systems. Modeling, simulation, and control are necessary to design, evaluate, and optimize the systems' technical and economic performance. This report first establishes modeling requirements to analyze candidate hybrid systems (a strict definition of "hybrid system" will be also provided). Modeling fidelity levels are based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit (FOMs). The associated computational and co-simulation resources needed are established, including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first describes the FOMs, systems requirements, and constraints necessary to characterize the grid and hybrid system behaviors and market interactions. Grid reliability assessment metrics and effective cost of energy (ECE), as opposed to the standard levelized cost of electricity (LCOE), are introduced as technical and economic indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluating nontraditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed.This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, control, co-simulate, and optimize:(1) energy system's components (e.g., power generation unit, chemical process, electricity management unit), (2) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (3) system control modules. Controlling and co-simulating complex, tightly coupled, dynamic energy systems requires multiple controls and simulation tools, potentially developed in several programing languages and resolved on separate time scales. Whereas further investigation and development of hybrid concepts will provide a more complete understanding of the joint computational and physical modeling and control needs, this report highlights areas where control and co-simulation capabilities are warranted. The current development status, quality assurance, availability, and maintainability of control and simulation tools available for hybrid systems modeling are presented. Existing gaps in the modeling, simulation, and control toolsets and development needs are subsequently discussed. This work will feed into broader efforts to design, develop, and demonstrate hybrid energy systems.

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“…This results in an involved, tightly-coupled system with various energy-producing elements including baseload, peaker, and renewable generators. Prior work [13] has been focused on dynamic modeling, simulation, control, and optimization for HES. However, such prior work models renewable generation using historical weather conditions, for which there are a limited number of measurements.…”

Section: Introductionmentioning

confidence: 99%

Correlated synthetic time series generation for energy system simulations using Fourier and ARMA signal processing

et al. 2020

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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.

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Nuclear Hybrid Energy Systems Regional Studies: West Texas & Northeastern Arizona

Cited by 8 publications

References 19 publications

Generation and Use of Thermal Energy in the Industrial Sector and Opportunities to Reduce its Carbon Emissions

Generation and Use of Thermal Energy in the Industrial Sector and Opportunities to Reduce its Carbon Emissions

Strategy and gaps for modeling, simulation, and control of hybrid systems

Correlated synthetic time series generation for energy system simulations using Fourier and ARMA signal processing

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