Candidate: Hafiz Muhammad Usman Butt
Title: Real-Time Energy Management Strategies for Residential Distribution System Using Smart Meter Data
Date: September 7, 2023
Time: 9:30 AM
Place: EIT 3142
Supervisor(s): El Shatshat, Ramadan - El-Hag, Ayman
Abstract:
Energy management (EM) strategies for a power distribution system have attracted attention in the past few decades. EM within smart residential distribution systems is a long-standing challenge that involves effective scheduling of electric vehicle charging and discharging while utilizing available photovoltaic resources and efficiently drawing power from the electric grid to meet household energy demands. In this dissertation, we propose a fuzzy logic-based real-time energy management control (RTEMC) system, from the perspective of an electric utility. The primary objectives are to utilize available energy resources in a smart residential distribution system, optimize grid power consumption, minimize electricity costs for both the utility and customers, ensure reliable power grid operation, and mitigate distribution transformer (DT) overloading.
The proposed RTEMC framework comprises three subsets that utilize residential smart meter data. The first subset proposes a novel non-intrusive approach based on the Universal Adaptive Stabilization (UAS) algorithm for real-time assessment of behind-the-meter (BTM) solar generation using smart meter data from residential customers. This approach is characterized by its simplicity, robustness, and unsupervised operation, eliminating the need for complex system dynamics. The accuracy and convergence of the proposed method are mathematically justified and evaluated against advanced algorithms using publicly available datasets.
The second subset presents a hardware-free strategy for DT kVA load estimation using smart meter data from residential customers. The proposed DT kVA load estimation algorithm operates at the utility server level without requiring a fixed power factor assumption or reactive power load information across residential customers. The proposed strategy provides a simple, effective fixed-point iteration-based formulation for a balanced secondary distribution network, that is extended for an unbalanced three-phase underground secondary distribution network. Theoretical analysis on convergence and stability of the proposed DT kVA load estimation method is also provided.
Building upon the second subset, the third subset proposes a four-layer framework that utilizes the DT kVA load estimation algorithm for assessing the remaining useful life (RUL) of a DT. The first layer stores residential smart meter data used for DT kVA load estimation in the second layer. In the third layer, two powerful forecasting tools, Time Series Decomposition and Hidden Markov Model, are compared. The historical and forecast data, along with the DT’s thermal parameters, are employed to assess its RUL. Numerical validation is conducted using real-world data from fifteen households in London, Ontario, Canada.
This dissertation aims to propose a fast, efficient, and real-time energy management strategy for smart residential distribution systems. The integrated subsets of the proposed framework offer precise estimation of real-time BTM solar generation, ensuring system reliability while providing accurate DT kVA load estimation to mitigate DT overloading. As a result, the proposed strategy with its integrated subsets makes a valuable contribution to the advancement of smart grid technologies and various distribution automation applications.