ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Multistep Multivariate Scenario Generation and Forecasting for Power Systems using Machine Learning

Description
The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network. Fundamentally, the power system relies on a real-time balance of…

The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network. Fundamentally, the power system relies on a real-time balance of generation and demand. However, renewable resources such as solar and wind farms are not available throughout the day. Furthermore, they introduce temporal variability to the generation process due to metrological factors, making the balance of generation and demand precarious. Utilities use standby units with reserve power and high ramp-up, ramp-down capabilities to ensure balance. However, such solutions can be very costly. An accurate scenario generation and forecasting of the stochastic variables (load and renewable resources) can help reduce the cost of these solutions. The goal of this research is to solve the scenario generation and forecasting problems using state-of-the-art machine learning techniques and algorithms. The training database is created using publicly available data obtained from NREL and the Texas-2000 bus system. The IEEE-30 bus system is used as the test system for the analysis conducted here. The conventional generators of this system are replaced with solar farms and wind farms. The ability of four machine learning algorithms in addressing the scenario generation and forecasting problems are investigated using appropriate metrics. The first machine learning algorithm is the convolutional neural network (CNN). It is found to be well-suited for the scenario generation problem. However, its inability to capture certain intricate details about the different variables was identified as a possible drawback. The second algorithm is the long-short term memory-variational auto-encoder (LSTM-VAE). It generated scenarios that are very similar to the actual scenarios indicating that it is suitable for solving the forecasting problem. The third algorithm is the conditional generative adversarial network (C-GAN). It was extremely effective in generating scenarios when the number of variables were small. However, its scalability was found to be a concern. The fourth algorithm is the spatio-temporal graph convolutional network (STGCN). It was found to generate representative correlated scenarios effectively.
ContributorsAlhazmi, Mohammed Ahmed (Author) / Pal, Anamitra (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith (Committee member) / Arizona State University (Publisher)
Created2021
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Correlated Scenario Generation Using Generative Models

Description
With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more challenging task for the modern power system. This is because with higher penetration of renewable generation, the system has to bear a higher degree of variability and uncertainty. One way…

With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more challenging task for the modern power system. This is because with higher penetration of renewable generation, the system has to bear a higher degree of variability and uncertainty. One way to address this problem is by generating realistic scenarios that complement and supplement actual system conditions. This thesis presents a methodology to create such correlated synthetic scenarios for load and renewable generation using machine learning. Machine learning algorithms need to have ample amounts of data available to them for training purposes. However, real-world datasets are often skewed in the distribution of the different events in the sample space. Data augmentation and scenario generation techniques are often utilized to complement the datasets with additional samples or by filling in missing data points. Datasets pertaining to the electric power system are especially prone to having very few samples for certain events, such as abnormal operating conditions, as they are not very common in an actual power system. A recurrent generative adversarial network (GAN) model is presented in this thesis to generate solar and load scenarios in a correlated manner using an actual dataset obtained from a power utility located in the U.S. Southwest. The generated solar and load profiles are verified both statistically and by implementation on a simulated test system, and the performance of correlated scenario generation vs. uncorrelated scenario generation is investigated. Given the interconnected relationships between the variables of the dataset, it is observed that correlated scenario generation results in more realistic synthetic scenarios, particularly for abnormal system conditions. When combined with actual but scarce abnormal conditions, the augmented dataset of system conditions provides a better platform for performing contingency studies for a more thorough reliability planning. The proposed scenario generation method is scalable and can be modified to work with different time-series datasets. Moreover, when the model is trained in a conditional manner, it can be used to synthesise any number of scenarios for the different events present in a given dataset. In summary, this thesis explores scenario generation using a recurrent conditional GAN and investigates the benefits of correlated generation compared to uncorrelated synthesis of profiles for the reliability planning problem of power systems.
ContributorsBilal, Muhammad (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2022