Matching Items (19)
Description
The dynamics of urban water use are characterized by spatial and temporal variability that is influenced by associated factors at different scales. Thus it is important to capture the relationship between urban water use and its determinants in a spatio-temporal framework in order to enhance understanding and management of urban water demand. This dissertation aims to contribute to understanding the spatio-temporal relationships between single-family residential (SFR) water use and its determinants in a desert city. The dissertation has three distinct papers to support this goal. In the first paper, I demonstrate that aggregated scale data can be reliably used to study the relationship between SFR water use and its determinants without leading to significant ecological fallacy. The usability of aggregated scale data facilitates scientific inquiry about SFR water use with more available aggregated scale data. The second paper advances understanding of the relationship between SFR water use and its associated factors by accounting for the spatial and temporal dependence in a panel data setting. The third paper of this dissertation studies the historical contingency, spatial heterogeneity, and spatial connectivity in the relationship of SFR water use and its determinants by comparing three different regression models. This dissertation demonstrates the importance and necessity of incorporating spatio-temporal components, such as scale, dependence, and heterogeneity, into SFR water use research. Spatial statistical models should be used to understand the effects of associated factors on water use and test the effectiveness of certain management policies since spatial effects probably will significantly influence the estimates if only non-spatial statistical models are used. Urban water demand management should pay attention to the spatial heterogeneity in predicting the future water demand to achieve more accurate estimates, and spatial statistical models provide a promising method to do this job.
ContributorsOuyang, Yun (Author) / Wentz, Elizabeth (Thesis advisor) / Ruddell, Benjamin (Thesis advisor) / Harlan, Sharon (Committee member) / Janssen, Marcus (Committee member) / Arizona State University (Publisher)
Created2013
Description
Today's energy market is facing large-scale changes that will affect all market players. Near the top of that list is the rapid deployment of residential solar photovoltaic (PV) systems. Yet that growing trend will be influenced multiple competing interests between various stakeholders, namely the utility, consumers and technology provides. This study provides a series of analyses--utility-side, consumer-side, and combined analyses--to understand and evaluate the effect of increases in residential solar PV market
penetration. Three urban regions have been selected as study locations--Chicago, Phoenix, Seattle--with simulated load data and solar insolation data at each locality. Various time-of-use pricing schedules are investigated, and the effect of net metering is evaluated to determine the optimal capacity of solar PV and battery storage in a typical residential home. The net residential load profile is scaled to assess system-wide technical and economic figures of merit for the utility with an emphasis on intraday load profiles, ramp rates and electricity sales with increasing solar PV penetration. The combined analysis evaluates the least-cost solar PV system for the consumer and models the associated system-wide effects on the electric grid. Utility revenue was found to drop by 1.2% for every percent PV penetration increase, net metering on a monthly or annual basis improved the cost-effectiveness of solar PV but not battery storage, the removal of net metering policy and usage of an improved the cost-effectiveness of battery storage and increases in solar PV penetration reduced the system load factor. As expected, Phoenix had the most favorable economic scenario for residential solar PV, primarily due to high solar insolation. The study location--solar insolation and load profile--was also found to affect the time of
year at which the largest net negative system load was realized.
penetration. Three urban regions have been selected as study locations--Chicago, Phoenix, Seattle--with simulated load data and solar insolation data at each locality. Various time-of-use pricing schedules are investigated, and the effect of net metering is evaluated to determine the optimal capacity of solar PV and battery storage in a typical residential home. The net residential load profile is scaled to assess system-wide technical and economic figures of merit for the utility with an emphasis on intraday load profiles, ramp rates and electricity sales with increasing solar PV penetration. The combined analysis evaluates the least-cost solar PV system for the consumer and models the associated system-wide effects on the electric grid. Utility revenue was found to drop by 1.2% for every percent PV penetration increase, net metering on a monthly or annual basis improved the cost-effectiveness of solar PV but not battery storage, the removal of net metering policy and usage of an improved the cost-effectiveness of battery storage and increases in solar PV penetration reduced the system load factor. As expected, Phoenix had the most favorable economic scenario for residential solar PV, primarily due to high solar insolation. The study location--solar insolation and load profile--was also found to affect the time of
year at which the largest net negative system load was realized.
ContributorsArnold, Michael (Author) / Johnson, Nathan G (Thesis advisor) / Rogers, Bradley (Committee member) / Ruddell, Benjamin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Ponderosa pine forests are a dominant land cover type in semiarid montane areas. Water supplies in major rivers of the southwestern United States depend on ponderosa pine forests since these ecosystems: (1) receive a significant amount of rainfall and snowfall, (2) intercept precipitation and transpire water, and (3) indirectly influence runoff by impacting the infiltration rate. However, the hydrologic patterns in these ecosystems with strong seasonality are poorly understood. In this study, we used a distributed hydrologic model evaluated against field observations to improve our understandings on spatial controls of hydrologic patterns, appropriate model resolution to simulate ponderosa pine ecosystems and hydrologic responses in the context of contrasting winter to summer transitions. Our modeling effort is focused on the hydrologic responses during the North American Monsoon (NAM), winter and spring periods. In Chapter 2, we utilized a distributed model explore the spatial controls on simulated soil moisture and temporal evolution of these spatial controls as a function of seasonal wetness. Our findings indicate that vegetation and topographic curvature are spatial controls. Vegetation controlled patterns during dry summer period switch to fine-scale terrain curvature controlled patterns during persistently wet NAM period. Thus, a climatic threshold involving rainfall and weather conditions during the NAM is identified when high rainfall amount (such as 146 mm rain in August, 1997) activates lateral flux of soil moisture and frequent cloudy cover (such as 42% cloud cover during daytime of August, 1997) lowers evapotranspiration. In Chapter 3, we investigate the impacts of model coarsening on simulated soil moisture patterns during the NAM. Results indicate that model aggregation quickly eradicates curvature features and its spatial control on hydrologic patterns. A threshold resolution of ~10% of the original terrain is identified through analyses of homogeneity indices, correlation coefficients and spatial errors beyond which the fidelity of simulated soil moisture is no longer reliable. Based on spatial error analyses, we detected that the concave areas (~28% of hillslope) are very sensitive to model coarsening and root mean square error (RMSE) is higher than residual soil moisture content (~0.07 m3/m3 soil moisture) for concave areas. Thus, concave areas need to be sampled for capturing appropriate hillslope response for this hillslope. In Chapter 4, we investigate the impacts of contrasting winter to summer transitions on hillslope hydrologic responses. We use a distributed hydrologic model to generate a consistent set of high-resolution hydrologic estimates. Our model is evaluated against the snow depth, soil moisture and runoff observations over two water years yielding reliable spatial distributions during the winter to summer transitions. We find that a wet winter followed by a dry summer promotes evapotranspiration losses (spatial averaged ~193 mm spring ET and ~ 600 mm summer ET) that dry the soil and disconnect lateral fluxes in the forested hillslope, leading to soil moisture patterns resembling vegetation patches. Conversely, a dry winter prior to a wet summer results in soil moisture increases due to high rainfall and low ET during the spring (spatially averaged 78 mm ET and 232 mm rainfall) and summer period (spatially averaged 147 mm ET and 247 mm rainfall) which promote lateral connectivity and soil moisture patterns with the signature of terrain curvature. An opposing temporal switch between infiltration and saturation excess runoff is also identified. These contrasting responses indicate that the inverse relation has significant consequences on hillslope water availability and its spatial distribution with implications on other ecohydrological processes including vegetation phenology, groundwater recharge and geomorphic development. Results from this work have implications on the design of hillslope experiments, the resolution of hillslope scale models, and the prediction of hydrologic conditions in ponderosa pine ecosystems. In addition, our findings can be used to select future hillslope sites for detailed ecohydrological investigations. Further, the proposed methodology can be useful for predicting responses to climate and land cover changes that are anticipated for the southwestern United States.
ContributorsMahmood, Taufique Hasan (Author) / Vivoni, Enrique R. (Thesis advisor) / Whipple, Kelin X. (Committee member) / Shock, Everett (Committee member) / Heimsath, Arjun M. (Committee member) / Ruddell, Benjamin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Electricity infrastructure vulnerabilities were assessed for future heat waves due to climate change. Critical processes and component relationships were identified and characterized with consideration for the terminal event of service outages, including cascading failures in transmission-level components that can result in blackouts. The most critical dependency identified was the increase in peak electricity demand with higher air temperatures. Historical and future air temperatures were characterized within and across Los Angeles County, California (LAC) and Maricopa County (Phoenix), Arizona. LAC was identified as more vulnerable to heat waves than Phoenix due to a wider distribution of historical temperatures. Two approaches were developed to estimate peak demand based on air temperatures, a top-down statistical model and bottom-up spatial building energy model. Both approaches yielded similar results, in that peak demand should increase sub-linearly at temperatures above 40°C (104 °F) due to saturation in the coincidence of air conditioning (AC) duty cycles. Spatial projections for peak demand were developed for LAC to 2060 considering potential changes in population, building type, building efficiency, AC penetration, appliance efficiency, and air temperatures due climate change. These projections were spatially allocated to delivery system components (generation, transmission lines, and substations) to consider their vulnerability in terms of thermal de-rated capacity and weather adjusted load factor (load divided by capacity). Peak hour electricity demand was projected to increase in residential and commercial sectors by 0.2–6.5 GW (2–51%) by 2060. All grid components, except those near Santa Monica Beach, were projected to experience 2–20% capacity loss due to air temperatures exceeding 40 °C (104 °F). Based on scenario projections, and substation load factors for Southern California Edison (SCE), SCE will require 848—6,724 MW (4-32%) of additional substation capacity or peak shaving in its LAC service territories by 2060 to meet additional demand associated with population growth projections.
ContributorsBurillo, Daniel (Author) / Chester, Mikhail V (Thesis advisor) / Ruddell, Benjamin (Committee member) / Johnson, Nathan (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this project, I investigated the ecosystem services, or lack thereof, that landscape designs created in terms of microclimate modification at 11 residential homes throughout the Phoenix Metro Area. I also created an article for the homeowners who participated, explaining what I did and how they could apply my research. My research question was how a person can achieve a comfortable outdoor climate in their yard without over-using scarce water resources. I hypothesized that there would be a negative correlation between the maximum air temperature and the percent shade in each yard, regardless of the percent grass. I analyzed the data I collected using the program, R, and discovered that my hypothesis was supported for the month of July. These results are in line with previous studies on the subject and can help homeowners make informed decisions about the effects their landscaping choices might have.
ContributorsBarton, Erin Michaela (Author) / Hall, Sharon (Thesis director) / Ruddell, Benjamin (Committee member) / Spielmann, Katherine (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor)
Created2014-05
Description
There is an interest in citizen scientist networks such as CoCoRaHS to develop an air temperature sensor with a solar shield that is both extremely low cost and user friendly for use in widespread data collection in order to analyze urban microclimates. This paper outlines work done to develop a low cost micrometeorology instrument to fulfill the design requirements set by CoCoRaHS. While the first two revisions of this technology had significant changes in development, a third revision was created as a proof of concept that low cost temperature sensors could be used in an array to accurately measure air temperature without solar radiation interference. Another technology, described as revision four, called the iButton was also evaluated and displayed promising ability to log temperatures, but costs too much for the ultra-low cost design goal. Additionally, work was done to design a radiation shield that will be prototyped and tested alongside commercial radiation shields. This controlled experiment will also include further evaluation of the iButton and the next revision of a custom microclimate temperature sensing unit to determine the best option for widespread field testing.
ContributorsMarshall, Travis Keith (Author) / Jordan, Shawn (Thesis director) / Ruddell, Benjamin (Committee member) / Barrett, The Honors College (Contributor) / Department of Engineering (Contributor)
Created2014-05
Description
Alfalfa is a major feed crop widely cultivated in the United States. It is the fourth largest crop in acreage in the US after corn, soybean, and all types of wheat. As of 2003, about 48% of alfalfa was produced in the western US states where alfalfa ranks first, second, or third in crop acreage. Considering that the western US is historically water-scarce and alfalfa is a water-intensive crop, it creates a concern about exacerbating the current water crisis in the US west. Furthermore, the recent increased export of alfalfa from the western US states to China and the United Arab Emirates has fueled the debate over the virtual water content embedded in the crop. In this study, I analyzed changes of cropland systems under the three basic scenarios, using a stylized model with a combination of dynamical, hydrological, and economic elements. The three scenarios are 1) international demands for alfalfa continue to grow (or at least to stay high), 2) deficit irrigation is widely imposed in the dry region, and 3) long-term droughts persist or intensify reducing precipitation. The results of this study sheds light on how distribution of crop areas responds to climatic, economic, and institutional conditions. First, international markets, albeit small compared to domestic markets, provide economic opportunities to increase alfalfa acreage in the dry region. Second, potential water savings from mid-summer deficit irrigation can be used to expand alfalfa production in the dry region. Third, as water becomes scarce, farmers more quickly switch to crops that make more economic use of the limited water.
ContributorsKim, Booyoung (Author) / Muneepeerakul, Rachata (Thesis advisor) / Ruddell, Benjamin (Committee member) / Aggarwal, Rimjhim (Committee member) / Arizona State University (Publisher)
Created2015
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 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
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 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.
ContributorsLopes, Mariana (Author) / Johnson, Nathan G (Thesis advisor) / Stechel, Ellen B (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2017
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 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