Date of Award

3-2024

Document Type

Masters Thesis

Degree Name

M.S.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering

First Advisor

Amin Khodaei

Second Advisor

Yun-Bo Yi

Third Advisor

Mohammad Matin

Fourth Advisor

Rui Fan

Keywords

Power distribution systems, Battery, Energy storage, Binary quadratic model (BQM)

Abstract

The rising need for exploiting a novel and evolved computation is an increasing concern in the power distribution system to address the exponential growth of distribution-connected devices. Scheduling numerous battery energy storage systems in an optimal way is one of the emerging challenges that will be more noticeable as the number of batteries, including residential, community, and vehicle batteries, increases in the grid. This thesis focuses on this topic and offers a necessary component in building the quantum-compatible distribution system of the future. Using a constrained quadratic model (CQM) on D-Wave’s hybrid solver as well as a binary quadratic model (BQM), this thesis solves the optimal battery scheduling problem for a large number of batteries. To formulate the BQM, a quadratic unconstrained binary optimization (QUBO) format was chosen and in order to fine-tune the QUBO model parameters, a sensitivity analysis was conducted. Numerical simulations, using Tesla Powerwalls, demonstrate promising results of model scalability for a large number of batteries. Additionally, the trend of computational time shows a linear pattern whereas in classical solvers this is exponential.

Copyright Date

3-2024

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Publication Statement

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

Rights Holder

Diba Ehsani

Provenance

Received from ProQuest

File Format

application/pdf

Language

English (eng)

Extent

64 pgs

File Size

1.5 MB

Discipline

Electrical engineering

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