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Description
The inherent intermittency in solar energy resources poses challenges to scheduling generation, transmission, and distribution systems. Energy storage devices are often used to mitigate variability in renewable asset generation and provide a mechanism to shift renewable power between periods of the day. In the absence of storage, however, time series

The inherent intermittency in solar energy resources poses challenges to scheduling generation, transmission, and distribution systems. Energy storage devices are often used to mitigate variability in renewable asset generation and provide a mechanism to shift renewable power between periods of the day. In the absence of storage, however, time series forecasting techniques can be used to estimate future solar resource availability to improve the accuracy of solar generator scheduling. The knowledge of future solar availability helps scheduling solar generation at high-penetration levels, and assists with the selection and scheduling of spinning reserves. This study employs statistical techniques to improve the accuracy of solar resource forecasts that are in turn used to estimate solar photovoltaic (PV) power generation. The first part of the study involves time series forecasting of the global horizontal irradiation (GHI) in Phoenix, Arizona using Seasonal Autoregressive Integrated Moving Average (SARIMA) models. A comparative study is completed for time series forecasting models developed with different time step resolutions, forecasting start time, forecasting time horizons, training data, and transformations for data measured at Phoenix, Arizona. Approximately 3,000 models were generated and evaluated across the entire study. One major finding is that forecasted values one day ahead are near repeats of the preceding day—due to the 24-hour seasonal differencing—indicating that use of statistical forecasting over multiple days creates a repeating pattern. Logarithmic transform data were found to perform poorly in nearly all cases relative to untransformed or square-root transform data when forecasting out to four days. Forecasts using a logarithmic transform followed a similar profile as the immediate day prior whereas forecasts using untransformed and square-root transform data had smoother daily solar profiles that better represented the average intraday profile. Error values were generally lower during mornings and evenings and higher during midday. Regarding one-day forecasting and shorter forecasting horizons, the logarithmic transformation performed better than untransformed data and square-root transformed data irrespective of forecast horizon for data resolutions of 1-hour, 30-minutes, and 15-minutes.
ContributorsSoundiah Regunathan Rajasekaran, Dhiwaakar Purusothaman (Author) / Johnson, Nathan G (Thesis advisor) / Karady, George G. (Thesis advisor) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2016
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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, and releasing energy through sensible and chemical potential. This study

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.
ContributorsGorman, Brandon Tom (Author) / Johnson, Nathan G (Thesis advisor) / Stechel, Ellen B (Committee member) / Chester, Mikhail V (Committee member) / Arizona State University (Publisher)
Created2017