Date of Award


Document Type


Degree Name


Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science

First Advisor

Amin Khodaei, Ph.D.

Second Advisor

Andrew Goetz, Ph.D.

Third Advisor

David Gao, Ph.D.

Fourth Advisor

Mohammad Matin, Ph.D.


Co-optimization generation, Distribution planning, Distribution system, Hybrid AC/DC microgrid, Microgrid planning, Robust optimization, Uncertainty consideration


The traditional approach for microgrid design and deployment has been mainly focused on AC systems. DC microgrids, however, are gaining attention due to numerous advantages they provide over AC microgrids, such as removing the need for synchronization and frequency adjustment as well as appropriateness in supporting DC loads and distributed energy resources (DERs). Moreover, considering that both AC and DC DERs are utilized in microgrids, hybrid microgrids would provide viable and economic solutions as they can potentially eliminate the need for AC-to-DC or DC-to-AC voltage conversions. This dissertation focuses on a hybrid microgrid planning model with the objective of minimizing the microgrid total planning cost. The model determines the optimal DER size and generation mix, the point of connection of DERs, and the type of each feeder, i.e., AC or DC. Moreover, it identifies threshold ratios of AC/DC loads at each feeder causing one type of feeder to be more economical than the other. It also proposes a co-optimization generation and distribution planning model in microgrids in which simultaneous investment in generation, i.e., distributed generation (DG) and distributed energy storage (DES), and distribution, i.e., upgrading the existing distribution network, is considered. Since uncertainty considerations in microgrid operation and planning are of high importance and uncertain factors can potentially alter the microgrid planner's decisions, this dissertation investigates a detailed discussion and analysis of prevailing uncertainties in microgrid operation and planning. New mathematical approaches, such as robust optimization, are commonly adopted to capture uncertainties and ensure practicality. However, this added practicality is at the expense of increased problem size and computational complexity. This dissertation accordingly proposes a new preprocessing approach to integrate uncertainties while reducing computational requirements.

Numerical simulations exhibit the merits of the proposed microgrid planning and co-optimization generation and distribution planning models in microgrid by analyzing the sensitivity of solutions on various decisive planning factors and reveal the effectiveness of the proposed preprocessing approach over the commonly used robust optimization method from the execution time and practicality perspectives.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Hossein Lotfi


Received from ProQuest

File Format




File Size

100 p.


Electrical engineering