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<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:18.0pt;color:#C8102E;mso-ligatures:none">Faster Optimal Power Flow Using
<o:p></o:p></span></b></p>
<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:18.0pt;color:#C8102E;mso-ligatures:none">Graph Neural Network-Assisted Methods<o:p></o:p></span></b></p>
<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:18.0pt;color:#C8102E;mso-ligatures:none"> </span></b><span style="mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:13.5pt;color:black;mso-ligatures:none">Thuan Pham</span></b><span style="mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">December 3<sup>rd</sup>, 2024; 9:00 AM - 11:00 AM</span><span style="mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Location: Zoom<o:p></o:p></span></p>
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<span style="font-family:"Aptos",sans-serif;color:black"><a href="https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/97669241989__;!!LkSTlj0I!CxjldWXkq3FdIIImbiRg0mrWDXU4HUUuTUt-w5LYHzaRWSZUAWdbfOTP1b64n4cf1PMl9y93ugVcdhmUr-3STCu9vGc$"><span style="color:#467886">https://uh-edu-cougarnet.zoom.us/j/97669241989</span></a></span><span style="font-family:"Aptos",sans-serif"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;line-height:106%;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Chair:</span></b><span style="font-size:10.5pt;line-height:106%;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Xingpeng Li, Ph.D.</span><span style="mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Members:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Kaushik Rajashekara, Ph.D. | David Jackson, Ph.D. | Harish Krishnamoorthy, Ph.D. | Lei Fan, Ph.D.</span><span style="mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal" style="margin-bottom:11.25pt;line-height:16.5pt"><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none">Abstract</span></b><span style="mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Optimal Power Flow (OPF) is a critical tool for power system operations, analysis, and scheduling, including real-time economic dispatch. It involves optimizing an
objective function, such as minimizing generation costs, while adhering to physical and operational constraints like generation limits and line thermal ratings. The applications of OPF have broadened significantly, addressing challenges in grid management
and resource allocation. <o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Traditional power system optimization models often fall short, either due to insufficient predictive capabilities or slow computation times. Advanced machine learning
approaches, such as Graph Neural Networks (GNNs), have emerged as a promising solution. By leveraging the topology of the power network, GNNs enable the flow of information between adjacent nodes and edges across multiple layers, capturing both local and global
relationships within the graph.<o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">This dissertation investigates the application of GNNs to optimize OPF problems. GNNs are utilized to simplify the complexities of OPF by predicting line congestion
and the maximum generation capacity of resources. Furthermore, the adoption of GNNs for
<i>N</i>-1 contingency prediction has demonstrated significant reductions in computational time. GNNs have also been applied to network-reconfigured OPF scenarios, where they optimize network topology to improve solution efficiency and operational performance.</span><span style="mso-ligatures:none"><o:p></o:p></span></p>
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