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  <title><![CDATA[Ph.D. Proposal Oral Exam - Gad Ilunga]]></title>
  <body><![CDATA[<p><strong>Title:&nbsp; </strong><em>Autonomous Quadratized Optimal Power Flow via Convex Solution - Sequential Linear Programming (CS-SLP)</em></p>

<p><strong>Committee:&nbsp; </strong></p>

<p>Dr. Meliopoulos, Advisor&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>

<p>Dr. Molzahn, Chair</p>

<p>Dr. Grijalva</p>

<p><strong>Abstract: </strong>The objective of the proposed research is to develop new computational methods for the economical and reliable operation of power systems in the presence of FACT devices. Specifically, a generalized convexification method for the high fidelity quadratized optimal power flow and an OPF solution algorithm named Convex Solution-Sequential Linear Programming (CS-SLP). CS-SLP starts by solving the convex quadratic optimal power flow with a commercial convex solver. Then starting from the convex solution, the initial problem is solved using SLP. Achievement of the objective will occur through the following contributions: (1) High fidelity Physically-based device modeling that is cast into a universal object-oriented syntax, allowing for the integration of detailed models of power electronics-based resources (PER) such as a UPFC;&nbsp;&nbsp;(2) The use of the universal object-oriented syntax allowing for the seamless integration of protection, optimization, and control, as well as the integration of the network model in different OPF applications; (3) The use of the universal syntax to form the OPF allows for the convexification of the problem around the current operating point by&nbsp;minimal relaxation (minimal term additions). (4) SLP based on the co-state method is used to correct for the convexification error. It efficiently linearizes the problem around the present operating conditions, resulting in a problem in terms of control variables.&nbsp;&nbsp;It features dynamic limits on control movement (ensuring that the linearized model remains in the valid region) and adaptive introduction/removal of model constraints.&nbsp;&nbsp;</p>
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