Matching Items (3)

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A Steady State Thermodynamic Model of Concentrating Solar Power with Thermochemical Energy Storage

Description

Fluids such as steam, oils, and molten salts are commonly used to store and transfer heat in a concentrating solar power (CSP) system. Metal oxide materials have received increasing attention for their reversible reduction-oxidation (redox) reaction that permits receiving, storing,

Fluids such as steam, oils, and molten salts are commonly used to store and transfer heat in a concentrating solar power (CSP) system. Metal oxide materials have received increasing attention for their reversible reduction-oxidation (redox) reaction that permits receiving, storing, and releasing energy through sensible and chemical potential. This study investigates the performance of a 111.7 MWe CSP system coupled with a thermochemical energy storage system (TCES) that uses a redox active metal oxide acting as the heat transfer fluid. A one-dimensional thermodynamic model is introduced for the novel CSP system design, with detailed designs of the underlying nine components developed from first principles and empirical data of the heat transfer media. The model is used to (a) size components, (b) examine intraday operational behaviors of the system against varying solar insolation, (c) calculate annual productivity and performance characteristics over a simulated year, and (d) evaluate factors that affect system performance using sensitivity analysis. Time series simulations use hourly direct normal irradiance (DNI) data for Barstow, California, USA. The nominal system design uses a solar multiple of 1.8 with a storage capacity of six hours for off-sun power generation. The mass of particles to achieve six hours of storage weighs 5,140 metric tonnes. Capacity factor increases by 3.55% for an increase in storage capacity to eight hours which requires an increase in storage volume by 33% or 737 m3, or plant design can be improved by decreasing solar multiple to 1.6 to increase the ratio of annual capacity factor to solar multiple. The solar reduction receiver is the focal point for the concentrated solar energy for inducing an endothermic reaction in the particles under low partial pressure of oxygen, and the reoxidation reactor induces the opposite exothermic reaction by mixing the particles with air to power an air Brayton engine. Stream flow data indicate the solar receiver experiences the largest thermal loss of any component, excluding the solar field. Design and sensitivity analysis of thermal insulation layers for the solar receiver show that additional RSLE-57 insulation material achieves the greatest increase in energetic efficiency of the five materials investigated.

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Date Created
2017

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Techno-economic analysis of a concentrating solar power plant using reduction/oxidation metal oxides for thermochemical energy storage

Description

Concentrating Solar Power (CSP) plant technology can produce reliable and dispatchable electric power from an intermittent solar resource. Recent advances in thermochemical energy storage (TCES) can offer further improvements to increase off-sun operating hours, improve system efficiency, and the reduce

Concentrating Solar Power (CSP) plant technology can produce reliable and dispatchable electric power from an intermittent solar resource. Recent advances in thermochemical energy storage (TCES) can offer further improvements to increase off-sun operating hours, improve system efficiency, and the reduce cost of delivered electricity. This work describes a 111.7 MWe CSP plant with TCES using a mixed ionic-electronic conducting metal oxide, CAM28, as both the heat transfer and thermal energy storage media. Turbine inlet temperatures reach 1200 °C in the combined cycle power block. A techno-economic model of the CSP system is developed to evaluate design considerations to meet targets for low-cost and renewable power with 6-14 hours of dispatchable storage for off-sun power generation. Hourly solar insolation data is used for Barstow, California, USA. Baseline design parameters include a 6-hour storage capacity and a 1.8 solar multiple. Sensitivity analyses are performed to evaluate the effect of engineering parameters on total installed cost, generation capacity, and levelized cost of electricity (LCOE). Calculated results indicate a full-scale 111.7 MWe system at $274 million in installed cost can generate 507 GWh per year at a levelized cost of $0.071 per kWh. Expected improvements to design, performance, and costs illustrate options to reduce energy costs to less than $0.06 per kWh.

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2017

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Optimizing Design Parameters of a Compact Linear Fresnel Reflector Solar Energy System with Machine Learning

Description

A Compact Linear Fresnel Reflector (CLFR) is a simple, cost-effective, and scalable option for generating solar power by concentrating the sun rays. To make a most feasible application, design parameters of the CLFR, such as solar concentrator design parameters, receiver

A Compact Linear Fresnel Reflector (CLFR) is a simple, cost-effective, and scalable option for generating solar power by concentrating the sun rays. To make a most feasible application, design parameters of the CLFR, such as solar concentrator design parameters, receiver design parameters, heat transfer, power block parameters, etc., should be optimized to achieve optimum efficiency. Many researchers have carried out modeling and optimization of CLFR with various numerical or analytical methods. However, often computational time and cost are significant in these existing approaches. This research attempts to address this issue by proposing a novel computational approach with the help of increased computational efficiency and machine learning. The approach consists of two parts: the algorithm and the machine learning model. The algorithm has been created to fulfill the requirement of the Monte Carlo Ray tracing method for CLFR collector simulation, which is a simplified version of the conventional ray-tracing method. For various configurations of the CLFR system, optical losses and optical efficiency are calculated by employing these design parameters, such as the number of mirrors, mirror length, mirror width, space between adjacent mirrors, and orientation angle of the CLFR system. Further, to reduce the computational time, a machine learning method is used to predict the optical efficiency for the various configurations of the CLFR system. This entire method is validated using an existing approach (SolTrace) for the optical losses and optical efficiency of a CLFR system. It is observed that the program requires 6.63 CPU-hours of computational time are required by the program to calculate efficiency. In contrast, the novel machine learning approach took only seconds to predict the optical efficiency with great accuracy. Therefore, this method can be used to optimize a CLFR system based on the location and land configuration with reduced computational time. This will be beneficial for CLFR to be a potential candidate for concentrating solar power option.

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2021