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<strong><span style="font-size:18.0pt;font-family:DengXian;color:#C8102E">Enhancing Frequency Stability of Low-Inertia Grids
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<strong><span style="font-family:DengXian;color:black">WITH NOVEL SECURITY CONSTRAINED UNIT COMMITMENT APPROACHES</span></strong><o:p></o:p></p>
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<strong><span style="font-size:13.5pt;font-family:DengXian;color:black">Mingjian Tuo</span></strong><o:p></o:p></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">June 19, 2023; 13:00 PM - 15:00 PM (CST)<br>
Location: Virtual Meeting<br>
Microsoft Teams Meeting: <a href="https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_MWE3MmY2YjYtZDliZC00N2NkLTgwMDUtZDM1YTRiMTIzOGIz*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*228782712f-7caf-4701-8c61-d446f75dbdc4*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!GddwHM6jgnnLunvhlKXaqDBl90DP1BEAmSCbqoHkPOiV5GG2vbuscbrUKgKB8oxa7PkDRbdNtYAYZUDs0fqVkwiQ5rI$">https://teams.microsoft.com/l/meetup-join/19%3ameeting_MWE3MmY2YjYtZDliZC00N2NkLTgwMDUtZDM1YTRiMTIzOGIz%40thread.v2/0?context=%7b%22Tid%22%3a%22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259%22%2c%22Oid%22%3a%228782712f-7caf-4701-8c61-d446f75dbdc4%22%7d</a></span><o:p></o:p></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">Committee Chair:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black"><br>
Xingpeng Li, Ph.D.</span><o:p></o:p></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">Committee Members:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black"><br>
Kaushik Rajashekara, Ph.D. | Zhu Han, Ph.D. | Lei Fan, Ph.D. | David R. Jackson, Ph.D.</span><o:p></o:p></p>
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<strong><span style="font-family:"Arial",sans-serif;color:#C8102E">Abstract</span></strong><o:p></o:p></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">Conventional synchronous generators are gradually being replaced by low-inertia inverter-based resources. Such transition introduces more complicated operation conditions. Insufficient
system inertia would lead to dramatical change in rate of change of frequency (RoCoF) and further results in under frequency load shedding as well as tripping of generator protection devices. Inertia estimation can ensure the accountability and reliability
of inertia response through implementation of frequency control ancillary services</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:ZH-CN">. Existing power system inertia estimation approaches do not take
such heterogeneity into consideration, and the risk of estimation error introduced by uniform model-based approach is underestimated. Hence, strategies such as dynamic inertia estimation method and machine learning-assisted inertia estimation approaches are
introduced.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black"> Frequency related constraints have been imposed in the conventional security-constrained unit commitment (SCUC) model to keep the minimum amount of synchronous inertia
online and secure system frequency stability. </span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:ZH-CN">However,
</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">in a large system inertia distribution may vary significantly, areas of low inertia are more susceptible to frequency deviation
</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:ZH-CN">f</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">ollowing G-1
</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:ZH-CN">contingency</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">, posing risks of load shedding and generation trip.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:ZH-CN">
To address these issues in frequency constrained SCUC, ensuring locational frequency stability, two novel strategies are proposed in this thesis:</span><o:p></o:p></p>
<p class="MsoBodyText" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in;line-height:normal;mso-list:l0 level1 lfo2">
<![if !supportLists]><span style="mso-list:Ignore">(1)<span style="font:7.0pt "Times New Roman"">
</span></span><![endif]><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:ZH-CN">A novel location based RoCoF constrained SCUC (LRC-SCUC) model that can capture the locational frequency response characteristics and
counteract the im-pact of system oscillation, guaranteeing the RoCoF security following a G-1 disturbance.
</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">A multiple-measurement-window method is introduced in this work to constrain highest value during oscillation</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:ZH-CN">.
Simulation results demonstrate the effectiveness of proposed LRC-SCUC model. The results also show that deploying virtual inertia techniques not only reduces the total cost, but also improves the system market efficiency.</span><o:p></o:p></p>
<p class="MsoBodyText" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in;line-height:normal;mso-list:l0 level1 lfo2">
<![if !supportLists]><span style="mso-list:Ignore">(2)<span style="font:7.0pt "Times New Roman"">
</span></span><![endif]><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">A generic data-driven frequency and RoCoF predictor is first trained to predict maximal frequency deviation and the highest locational RoCoF simultaneously based
on a high-fidelity simulation dataset. And the derived frequency related constraints are then incorporated into machine learning assisted SCUC to guarantee frequency stability following the worst generator outage case while ensuring operational efficiency.
In addition, sparse computation and an active rectified linear unit (ReLU) linearization method are implemented to further improve the algorithm efficiency while retaining solution quality</span><span style="color:black">.</span><o:p></o:p></p>
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