IEEE WF-IoT Session: Modular Framework for Cost Optimization in Smart Grid
Thomas Kunz (Carleton University, Canada; Muhammad Raisul Alam, Carleton University, Canada; Marc St-Hilaire, Carleton University, Canada
A smart power grid transforms the traditional electric grid into a user centric, intelligent power network. This paper addresses the cost optimization problem in the smart grid from the users' perspective. A homeowner can install diverse energy generators and storage devices to reduce the dependency on external energy sources. The widespread utilization of green energy sources creates uncertainty in energy generation due to their unpredictable nature. A user can collaborate with the neighbors to participate in energy trading. The utility indirectly controls the energy consumption and generation in the system by utilizing a demand-oriented time varying price signal. The relationships between the participating components represent a complex unified system because of uncertain energy consumption and power generation disruption. Computational intelligence plays an essential role to coordinate the participating components. This paper proposes a cost optimization framework that breaks the dependencies between the components. The framework transforms the complex unified model into a simpler modular framework. Each module can be solved using different optimization approach, which implies a simple, flexible and traceable strategy for practical implementation.