Artificial Intelligent Based Energy and Demand Side Management for Microgrids and Smart Homes Considering Customer Privacy

Abstract

The rapid development of various power electronics applications facilitates the integration of many smart grid applications in recent years. However, integration of intermittent renewable energy sources, highly stochastic electric vehicles (EVs) activities on the grid and time-varying smart loads have increased the level of grid vulnerability to unusual and high complexity and quality-related problems. Among these problems is to accurately estimate the real contribution and consumption of household loads, in the era of smart appliances and interoperability operation, and its relative impact to the grid’s operation. Specifically, household loads represent a significant percentage of electrical energy consumption and, therefore, could offer great prosperity to the rise of the demand-side management (DSM) programs, which subsequently improve the stability of the grid’s operation. As a result, our main focus in this dissertation is to develop DSM strategies based on Artificial Intelligence (AI) techniques to properly model and estimate the amount of support smart homes could offer to the smart grids and microgrid’s operation. Throughout the way to achieve our goals, we develop an energy management framework for smart homes that operate in efficient and reliable microgrids with multiple energy sources and energy storage applications to meet the demands at a stable voltage and frequency limits. Furthermore, we develop a precise short-term load forecasting (STLF), which is a critical tool needed to manage a DSM program for residential loads that have very high uncertainty and volatility in load consumption. We also develop an energy exchange portal with communication sources, demands, and connectivity information between each consumer and the local power utility at the distribution level. Finally, creative AI methodologies have been developed throughout the way to facilitate the integration, control, and management of the DSM programs taking into account the consumers’ own privacy and security. The security of the DSM is provided by preserving the indoor privacy of the smart homes by sharing limited and encoded data among household appliances controllers.