Energy Management Strategies for Residential Distribution System Using Smart Meter Data

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. This dissertation introduces four important research problems that provide the power distribution system operator with crucial cybersecurity insights. These insights facilitate real-time monitoring of the Distribution Transformer (DT) kVA load, prediction of its end of remaining-useful life, and disaggregation of behind-the-meter solar generation — all using just smart meter data from residential customers processed at the electric utility’s server.

The first research 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 research 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 up on the second research work, the third research 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.

The three aforementioned research problems are seamlessly integrated into the fourth, presenting a fuzzy logic-based real-time energy management control system, from the perspective of an electric utility. The primary objectives of the fourth research work 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 DT overloading. This dissertation aims to propose a fast, efficient, and real-time energy management strategy for smart residential distribution systems. The three integrated research problems 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 real-time energy management control strategy with its integrated parts makes a valuable contribution to the advancement of smart grid technologies and various distribution automation applications.