[CCoE Notice] Cullen College Dissertation Announcement- Cunzhi Zhao

[Hutchinson, Inez A]

[Dissertation Defense Announcement at the Cullen College of Engineering]
Optimal Energy Management for
Battery Energy Storage System-Integrated Microgrids
Cunzhi Zhao

Nov 7, 2023; 10:00 AM - 12:00 AM (CST)
Zoom: https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/97669241989__;!!LkSTlj0I!DRd0fYjii2YJG3NDdWCibtKY5z9jzmBjI5ZgEziqe9xK-dkJLUny3PT2opTKgTmI5kwtpP9WnMMSG9r-tGLiB2eVED4$ 

Committee Chair:

Xingpeng Li, Ph.D.

Committee Members:

Kaushik Rajashekara, Ph.D. | Hao Huang, Ph.D. | Zhu Han, Ph.D. | David Jackson, Ph.D.

Abstract

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.

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.

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..

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