Coordinated Energy Management Using Agents in Neighborhood Areas with RES and Storage

This study proposes a novel coordination mechanism for smart homes in a small-scale neighborhood. This mechanism deploys a price and incentive-based energy management algorithm that considers renewable energy sources and battery storage. The objective of the proposed coordination algorithm is to decrease the electricity bills of the smart homes by increasing renewable energy usage inside the neighborhood, and enabling electricity trading among smart homes. The neighborhood is modeled using multi-agent systems, and heuristic problem solving is used by deploying a genetic optimization and rolling-horizon technique for day-ahead scheduling of the electricity appliances. Simulation results show that neighborhood coordination combined with incentives enable decreasing the total cost of neighborhood electricity consumption by increasing local renewable energy usage. Also, consumer results show that each individual smart home benefits from using the coordination mechanism.