Case Study: Nuclear-Renewable-Water Integration in Arizona

Epiney, Aaron; Rabiti, Cristian; Talbot, Paul; Kim, Jong Suk; Bragg‐Sitton, Shannon; Richards, James

doi:10.2172/1495196

Cited by 8 publications

(17 citation statements)

References 14 publications

(18 reference statements)

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“…A sensitivity analysis was carried out at the end of the investigation by varying different system parameters, such as discount rate, wholesale electricity price, project lifetime, net demand projection, and amount of salty water. However, N-R HES modeling was not presented in the study [23]. A similar case study has been conducted by Kim and Garcia (2015) in [24].…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Planning of Nuclear-Renewable Micro-Hybrid Energy System by Particle Swarm Optimization

Abdussami

Adham

2020

IEEE Access

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To minimize the anticipated shocks to economic, environmental, and social systems for developing and least developed countries, the reduction of Greenhouse Gas (GHG) emissions is mandatory to a large extend. The nuclear-renewable integrated system is proficient in optimal energy distribution to multiple production schemes to reduce GHG emissions and maximize profit. This paper addresses the hybridization of the micronuclear reactor and Renewable Energy Sources (RESs) Energy Sources (RESs) to develop a flexible, cost-effective, sustainable, and resilient off-grid Hybrid Energy System (HES). The paper presents three types of hybridization methods, termed "Direct Coupling," "Single Resource and Multiple products-based Coupling," and "Multiple Resources and Multiple products-based Coupling." The hybridization techniques are used to plan and identify the most efficient Nuclear-Renewable Micro-Hybrid Energy System (N-R MHES). The sizing, performance, and characterization of N-R MHES solely depend on the RES and load characteristics' availability. Based on proposed hybridization techniques, mathematical modeling of N-R MHES's economy is carried out in the MATLAB environment. An artificial intelligence optimization algorithm, namely Particle Swarm Optimization (PSO), is used to minimize the Net Present Cost (NPC) and achieve the optimal system configurations of different N-R MHESs. The simulation results determine that "Multiple Resources and Multiple Products-based N-R MHES" provides around 1.8 times and 1.3 times lower NPC than "Single Resource and Multiple products-based Coupling" and "Multiple Resources and Multiple products-based Coupling," respectively, with an acceptable margin of reliability. A sensitivity analysis has also been conducted in this paper to strengthen the findings of the study.

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Section: B Literature Reviewmentioning

confidence: 99%

Optimal Planning of Nuclear-Renewable Micro-Hybrid Energy System by Particle Swarm Optimization

Abdussami

Adham

2020

IEEE Access

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“…Most of the work in this respect is focused on reduction of the necessary number of samples to ensure a reliable convergence of the stochastic optimization under probabilistic constraints. For further details on the simulation framework see references (Rabiti et al 2017;Epiney et al 2018;Talbot et al 2018).…”

Section: Analysis Approach and Toolsmentioning

confidence: 99%

Integrated Energy Systems: 2020 Roadmap

Bragg‐Sitton

Rabiti

O’Brien

et al. 2020

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Many cities, states, utilities, and public commissions are setting energy standards that aim to reduce carbon emissions. In order to realize a clean and resilient energy future, new methods of energy production, distribution, and use will be required. Renewable energy technologies are currently being deployed in significant numbers around the world in response to the desire to reduce emissions, coupled with decreasing costs for these technologies. However, despite this growth, data reported in the International Energy Agency (IEA) Future of Nuclear report that was released in May 2019 indicate that the fraction of clean energy contributions to electricity generation has not changed over the last 20 years. This unexpected trend results from the increasing penetration of variable sources driving nuclear energy out of the market in some regions, resulting in the shutdown of some large-scale, non-emitting plants when non-emitting renewable resources are added to the grid. The advent of historically low-cost renewable generation sources, alongside low cost and high availability of natural gas, has driven down the price of electricity, decreasing the minimum baseload generation required to meet load at certain times of the day or year. These factors serve to diminish the role of traditionally baseload nuclear generation. Many nuclear plants have responded to increasing volatility in net demand by operating flexibly, reducing power output to reduce the financial impact to the plant from very low or negative market prices. This practice preserves the contribution of nuclear energy to grid stability and reduces economic losses associated with negatively-priced electricity sales, but it does not reduce the plant operating costs. Nuclear energy is a proven low-emission option that can provide consistent, dispatchable power to meet electricity demands while also providing high quality heat that can meet energy demands beyond the electricity sector-energy system design should seek to maximize these assets. This roadmap defines proposed integrated nuclear-renewable energy systems and identifies key technology gaps to realizing deployment of commercial scale systems for the production of a variety of electric and non-electric products. Integrated energy systems (IES) under consideration could incorporate multiple energy generation resources and energy use paths, with a focus on low-emission technologies such as nuclear and renewable generators. Together these technologies provide affordable, reliable, and resilient energy while simultaneously reducing environmental emission of CO2 and greenhouse gases (GHGs). IES are cooperatively-controlled systems that dynamically apportion thermal and/or electrical energy to provide responsive generation to the power grid. They are composed of multiple subsystems, which may or may not be geographically co-located, including a thermal energy generation source (e.g., nuclear), a turbine that converts thermal energy to electricity, additional electricity generation source(s) (e.g., renewable ge...

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“…] to construct an analysis framework using RAVEN for systems of energy producers and consumers in a method that allows statistically-meaningful decisions to be made with regards to the economic viability of energy grid portfolios and configurations. The results of this effort have shown value both in a theoretical sense [1,2,4,5,7,[9][10][11][12] as well as for specific applications when paired with industrial partners in the energy space [3,16]. However, the development and maintenance of these RAVEN workflows has been burdensome, and often involves complicated copy-and-modify operations that significantly slow the user experience.…”

Section: Tablesmentioning

confidence: 99%

“…HERON's primary utility is simplifying the creation of complex RAVEN workflows for grid-based stochastic technoeconomic analysis. In the past [1,3] these workflows were created manually, often with significant difficulty for the user due to the interconnected and complex nature of the inputs. Instead, HERON provides a surrogate input via RAVEN's Templating system.…”

Section: Workflow Generationmentioning

confidence: 99%

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HERON as a Tool for LWR Market Interaction in a Deregulated Market

Talbot

Gairola

Prateek

et al. 2019

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This document reports the development of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to technoeconomic analysis in a deregulated market. A short description of the HERON plugin is provided, and the release process is described. HERON as a plugin enables RAVEN to perform stochastic technoeconomic analysis of grid-energy systems in a generic approach. The primary function of HERON is to generate the complex RAVEN workflows necessary to optimize component capacities under stochastic systems. HERON is capable of analyzing systems with complex components transferring a variety of commodities, including production components and varied markets. HERON is capable of optimizing highresolution dispatch for such systems and guiding stochastic optimization algorithms in RAVEN for finding optimal component capacities. In particular, this document demonstrates the application of HERON to systems with deregulated markets.

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Case Study: Nuclear-Renewable-Water Integration in Arizona

Cited by 8 publications

References 14 publications

Optimal Planning of Nuclear-Renewable Micro-Hybrid Energy System by Particle Swarm Optimization

Optimal Planning of Nuclear-Renewable Micro-Hybrid Energy System by Particle Swarm Optimization

Integrated Energy Systems: 2020 Roadmap

HERON as a Tool for LWR Market Interaction in a Deregulated Market

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