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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif"><img width="600" height="171" style="width:6.25in;height:1.7812in" id="Picture_x0020_2" src="cid:image001.png@01DB3F52.F905D9E0" alt="Thesis Defense Announcement at the Cullen College of Engineering"></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-size:17.0pt;color:#C8102E;mso-ligatures:none">Advanced Forecasting and System Integration Impact Analysis of Renewable Energy and Electric Vehicles on Power Distribution Networks<o:p></o:p></span></b></p>
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<b><span style="font-size:13.5pt;color:black;mso-ligatures:none">Elias Raffoul</span></b><span style="font-family:"Aptos",sans-serif;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 2, 2024; 9:00 AM - 11:00 AM (CST)<br>
<b>Location: Online</b><br>
Teams: </span><u><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#0563C1;mso-ligatures:none"><a href="https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_NmViZmQ1MjItOTUwMy00NmZmLWJlY2MtMmZkN2ZjZjIxMzhl*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*2297723938-975b-475c-a245-ac8038e6f996*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!De9d2uF6g-EkOSVXY7XwRmcRQ1J2p8dHswPaJScFhTzR46p5MjiPF_WR7AV5Bb5bbPpSc763m5Pfx4Pefs86RKfqSzs$">https://teams.microsoft.com/l/meetup-join/19%3ameeting_NmViZmQ1MjItOTUwMy00NmZmLWJlY2MtMmZkN2ZjZjIxMzhl%40thread.v2/0?context=%7b%22Tid%22%3a%22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259%22%2c%22Oid%22%3a%2297723938-975b-475c-a245-ac8038e6f996%22%7d</a></span></u><span style="font-family:"Aptos",sans-serif;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">Meeting ID: 220 120 933 087
</span><span style="font-family:"Aptos",sans-serif;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">Passcode: h5rmJJ</span><span style="font-family:"Aptos",sans-serif;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 Chair:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Xingpeng Li, Ph.D.</span><span style="font-family:"Aptos",sans-serif;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>
Hao Huang, Ph.D. | Kaushik Rajashekara, Ph.D. | Tianxia Zhao, Ph.D.<o:p></o:p></span></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:#C8102E;mso-ligatures:none">Abstract</span></b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">The rapid evolution of modern power systems, driven by renewable energy integration
and transportation electrification, has introduced new challenges in grid reliability, efficiency, and sustainability. This thesis explores advanced modeling techniques to address these challenges, focusing on three interconnected themes: load forecasting,
renewable energy integration, and the impact of electric vehicle (EV) adoption on distribution grids.<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">First, a comparative analysis of machine learning (ML) models, including feedforward
neural networks, recurrent neural networks, long short-term memory networks (LSTM), gated recurrent units, and attention temporal graph convolutional networks, is conducted for short-term load forecasting. Using real-world data from Houston’s Energy Corridor
distribution system, the study identifies the most effective ML models for accurate load prediction, offering insights for optimizing grid operations.<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">Second, the thesis develops an LSTM-based deep learning framework for net load forecasting
in microgrids equipped with solar and wind power. Leveraging typical meteorological year datasets, the model accurately predicts net load dynamics, enabling improved energy management in renewable-based microgrids and addressing the variability inherent in
renewable energy sources.<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">Lastly, the impact of widespread EV charging on power distribution networks is assessed
using a detailed simulation of a 240-bus system with 1120 customers. By evaluating ampacity violations, line loading, and voltage stability under various EV penetration scenarios, the research identifies critical grid infrastructure challenges and proposes
strategies for grid reinforcement and voltage-level adjustments to ensure reliable operation.<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">Together, these studies provide a comprehensive framework for advancing power and energy
systems through predictive modeling, renewable energy forecasting, and infrastructure planning. By addressing the challenges of grid modernization, this work contributes to the development of resilient, efficient, and sustainable power systems capable of meeting
the demands of a decarbonized future.<o:p></o:p></span></p>
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