Theses and Dissertations

ORCID

https://orcid.org/0009-0003-0500-9303

Advisor

Fu, Yong

Committee Member

Karimi, Masoud

Committee Member

Choi, Seungdeog

Date of Degree

5-15-2026

Original embargo terms

Visible MSU Only 6 months

Document Type

Graduate Thesis - Campus Access Only

Major

Electrical & Computer Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

This thesis addresses intelligent residential demand response (DR) in distribution systems with high renewable energy source (RES) penetration, aiming to enhance voltage stability, resilience, and operational efficiency while preserving consumer comfort. A consumer‑centric framework is proposed using a multi‑criteria optimization approach that integrates technical objectives with user preferences. Solar and wind generation are modeled using a Levenberg–Marquardt Artificial Neural Network (LM‑ANN) for realistic system representation. An intelligent DR strategy based on a modified Binary Teaching–Learning‑Based Optimization (BTLBO) algorithm determines optimal appliance switching schedules while incorporating comfort constraints. Objective weighting is refined using the Criteria Importance Through Intercriteria Correlation (CRITIC) method. Additional realism is achieved by considering varying consumer densities, probabilistic usage patterns, and weekday/weekend behavior. The proposed approach is validated on a modified IEEE 33‑bus distribution system, demonstrating improved voltage profiles, enhanced resilience, reduced network losses, and sustained consumer satisfaction.

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