Home energy management system embedded with a multi objective demand response optimization model to benefit customers and operators

This thesis aims to develop a Home Energy Management System (HEMS) that optimizes the load demand and distributed energy resources considering utility price signal, customer satisfaction, and distribution transformer condition. The electricity home demand considers Electric Vehicles (EVs), PV-based renewable energy resources, Energy Storage Systems (ESSs), and all types of fixed, shiftable, and controllable appliances. A multi-objective demand/generation response is presented to optimize the scheduling of various loads/supplies based on the pricing schemes. The customers' behavior comfort-level and a degradation cost that reflects the distribution transformer Loss-of-Life (LoL) are integrated into the multi-objective optimization problem. First, conventional optimization approaches are utilized to solve the multi objective optimization problem. To overcome the conventional optimization limitations, a data-driven analysis, which utilizes deep reinforcement learning (DRL),is used. The results show that the DRL-based HEMS is more efficient in minimizing the energy cost while adapting to the user comfort within the desired level