Zhaohao Ding

Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical Engineering

First Advisor

Wei-Jen Lee


With its technological and regulatory innovation of scale and structure, microgrids have been developed all over the world as a mean to address the high penetration level of renewable generation, reduce the greenhouse gas emission, and provide economical solutions for the currently non-electrified area. The operation of microgrid requires resource planning for those fossil-fuel based generators, energy storage systems, and demand resources if demand side management is implemented. Due to the stochastic nature of renewable energy resources, load behaviors and market prices, enormous uncertainties are involved in the microgrid operation and scheduling problems for both short-term and longer term. These uncertainties may result in a non-optimal operation or even jeopardizing the reliability of the microgrid if they are not fully considered in the scheduling stage.This dissertation applies stochastic modeling and optimization techniques to address the challenges brought by uncertainties in the microgrid operation through. The microgrid day-ahead scheduling problem, demand side management scheduling problem, and medium-term operation scheduling problem are modelled via stochastic approaches to achieve the optimal operation decisions under an environment with high degree of uncertainties. Meanwhile, a microgrid carbon emission co-optimized scheduling algorithm is also proposed to address the carbon emission in the microgrid operation. Correspondingly, the uncertainty models and solving methods for those formulations are also proposed by this dissertation and numerical results are presented for verification and illustration purpose.


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington