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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
A mathematical approach was developed to evaluate the resilience of coupled power-water networks using a variant of contingency analysis adapted from electric transmission studies. In particular, the “what if” scenarios explored in power systems research were extended and applied for coupled power-water network research by evaluating how stressors and failures

A mathematical approach was developed to evaluate the resilience of coupled power-water networks using a variant of contingency analysis adapted from electric transmission studies. In particular, the “what if” scenarios explored in power systems research were extended and applied for coupled power-water network research by evaluating how stressors and failures in the water network can propagate across system boundaries and into the electric network. Reduction in power system contingency reserves was the metric for determining violation of N-1 contingency reliability. Geospatial considerations were included using high-resolution, publicly available Geographic Information System data on infrastructure in the Phoenix Metropolitan Area that was used to generate a power network with 599 transmission lines and total generation capacity of 18.98 GW and a water network with 2,624 water network lines and capacity to serve up to 1.72M GPM of surface water. The steady-state model incorporated operating requirements for the power network—e.g., contingency reserves—and the water network—e.g., pressure ranges—while seeking to meet electric load and water demand. Interconnections developed between the infrastructures demonstrated how alternations to the system state and/or configuration of one network affect the other network, with results demonstrated through co-simulation of the power network and water network using OpenDSS and EPANET, respectively. Results indicate four key findings that help operators understand the interdependent behavior of the coupled power-water network: (i) two water failure scenarios (water flowing out of Waddell dam and CAP canal flowing west of Waddell dam) are critical to power-water network N-1 contingency reliability above 60% power system loading and at 100% water system demand, (ii) fast-starting natural gas generating units are necessary to maintain N-1 contingency reliability below 60% power system loading, (iii) Coolidge Station was the power plant to most frequently undergo a reduction in reserves amongst the water failure scenarios that cause a violation of N-1 reliability, (iv) power network vulnerability to water network failures was non-linear because it depends on the generating units that are dispatched, which can vary as line thermal limits or unit generation capacities are reached.
ContributorsGorman, Brandon (Author) / Johnson, Nathan G (Thesis advisor) / Seager, Thomas P (Committee member) / Chester, Mikhail V (Committee member) / Arizona State University (Publisher)
Created2020