Heuristic-Based Packetized Energy Management for Residential Electricity Demand

Abstract

The unprecedented growth of distributed energy resources (DERs) has brought limitless opportunities to reshape the energy infrastructure and instigate urgent reforms for technoeconomic advancements, business models, sustainability, environmental impacts, and resource management. The incorporation of DERs is a remarkable choice to replace fossil fuels-based generation and transform the electric power grid from a centralized into a decentralized network, unlocking the opportunities for a green, sustainable, and costeffective energy ecosystem. To realize this transformation, the concept of the Energy Internet (EI) has emerged aiming to combine DERs, such as renewable energy resources, energy storage systems, flexible loads, and other energy networks, such as heat and gas networks, through advanced information and communication technologies (ICTs). In this context, this doctoral dissertation first puts forward the basic foundation of the EI and proposes a universal definition through an extensive review of the state of the art. The EI powers a revolutionary technological transformation in the power system by integrating multiple energy networks, intelligent devices, smart metering infrastructure), and flexible management of energy resources by packetized energy management (PEM). What is more, the potential challenges and key requirements, such as system complexity, system security, and social acceptance, are identified for establishing the EI framework.

Second, this dissertation investigates the two essential features of the EI in the context of managing energy resources in smart homes: the home energy management system (HEMS) and the energy router (ER). Primarily, the hierarchical structure of the HEMS is described comprehensively considering the key components, demand response (DR) benefits, and energy management solutions based on heuristic optimization methods (HOMs). The main objectives accomplished by the HEMS are efficient energy management plans for smart homes, minimizing the electricity bill for smart home users, and reducing the peak-to-average ratio. This accomplishment of the HEMS benefits smart home users and maintains stable power grid operation in peak demand. Moreover, energy management solutions provided by the HEMS are tested in the case of a cyberattack to validate the performance of the HEMS in terms of the resilience index.

Last but not least, a comprehensive ER system is designed to provide efficient PEM plans for smart homes based on the energy packet scheduling parameters, energy packet transaction parameters, grid-connected photovoltaic systems, and energy storage systems. First, the key features of the ER-based PEM are comprehensively described and a system model is developed for single and multiple smart homes including their formulation and respective constraints to jointly minimize the average aggregate system cost. The joint optimization problem is mathematically solved through the implemented HOMs. The simulation-based results are analyzed, and it is demonstrated that the designed ER-based PEM system is capable of minimizing the average aggregated cost and providing efficient PEM plans for a single home or multiple homes in varying weather conditions.