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<div><span class="x_elementToProof" style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 14.6667px; font-weight: 400; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);"><img alt="Dissertation Defense Announcement at the Cullen College of Engineering" id="x_x__x0000_i1026" style="width:6.25in; height:1.7812in; max-width:2240px; max-height:1008px; margin:0px" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation1.png"></span></div>
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<div class="x_elementToProof" style="line-height:28px"><span style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 24px; color: rgb(200, 16, 46);"><b>Optimal Energy Management for</b></span></div>
<div class="x_elementToProof"><span style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 24px; color: rgb(200, 16, 46);"><b>Battery Energy Storage System-Integrated Microgrids</b></span></div>
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<div class="x_elementToProof" style="line-height:20px; margin-bottom:5px"><span style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 18px; color: rgb(0, 0, 0);"><b>Cunzhi Zhao</b></span></div>
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<span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; font-weight: 400; color: rgb(0, 0, 0);">Nov 7, 2023; 10:00 AM - 12:00 AM (CST)<br>
Zoom: </span><span style="letter-spacing: normal; font-size: 14px; font-weight: 400; color: rgb(12, 59, 94); background-color: rgb(255, 255, 255);"><u><a href="https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/97669241989__;!!LkSTlj0I!DRd0fYjii2YJG3NDdWCibtKY5z9jzmBjI5ZgEziqe9xK-dkJLUny3PT2opTKgTmI5kwtpP9WnMMSG9r-tGLiB2eVED4$" data-auth="NotApplicable" id="OWAa24bd237-8df3-e208-8f01-79ecdb4dc3c5" class="x_OWAAutoLink" style="margin-top:0px; margin-bottom:0px">https://uh-edu-cougarnet.zoom.us/j/97669241989</a></u></span></p>
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<span style="letter-spacing: normal; font-size: 14px; color: rgb(0, 0, 0);"><b>Committee Chair:</b></span></p>
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<span style="letter-spacing: normal; font-size: 14px; font-weight: 400; color: rgb(0, 0, 0);">Xingpeng Li, Ph.D.</span></p>
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<span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; color: rgb(0, 0, 0);"><b>Committee Members:</b></span></p>
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<span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; color: rgb(0, 0, 0);">Kaushik Rajashekara</span><span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; font-weight: 400; color: rgb(0, 0, 0);">,
Ph.D. | Hao Huang, Ph.D. | Zhu Han, Ph.D. | David Jackson, Ph.D.</span></p>
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<span style="letter-spacing:normal"><b>Abstract</b></span></p>
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<span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; color: rgb(0, 0, 0);">This dissertation explores optimal energy management strategies for battery energy storage system (BESS)-integrated microgrids, addressing
both grid-connected and isolated microgrid scenarios. The research is motivated by the increasing integration of renewable energy sources, such as wind and solar, which introduce uncertainty and challenges in maintaining grid stability. As a result, an increasing
number of BESS are being incorporated into the system to facilitate the seamless integration of renewable energy sources. However, degradation remains an inevitable challenge, posing difficulties in accurately modeling and predicting the condition of key BESS
components, especially the widely used Lithium-ion batteries. </span></p>
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<span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; color: rgb(0, 0, 0);">To address this issue, a data driven method to predict the battery degradation per a given scheduled battery operational profile. Particularly,
a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. A battery degradation based MDS (BDMDS) model is proposed when incorporating the proposed NNBD model into
microgrid day-ahead scheduling (MDS) that can consider the equivalent battery degradation cost precisely. Since the proposed NNBD model is highly non-linear and non-convex due to the rectified linear unit (ReLU), BDMDS would be very hard to solve. To address
this issue, a neural network and optimization decoupled heuristic algorithm is proposed here to effectively solve this neural network embedded optimization problem by the iteration method. </span></p>
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<span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; color: rgb(0, 0, 0);">An alternative way to solve the BDMDS model is to linearize the NNBD model by converting the nonlinear activation function at each neuron
into linear constraints, which enables BDMDS to become a linearized BDMDS model. In addition, ReLU linearization approximation methods and sparse computation models are implemented to further improve the algorithm efficiency while retaining solution quality.</span><span style="letter-spacing: normal; font-family: Arial, Helvetica, sans-serif; font-size: 14px; font-weight: 400; color: rgb(0, 0, 0);">.</span></p>
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