Optimization of Energy Storage System for PV House using Particle Swarm Optimization.

The residential sector account for 23.1% of Thailand energy consumption. Solar rooftop has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since the new policy of government will focus on self-consumption model. While, solar energy are intermittent and uncontrollable, full time employment must still rely, in part, on the electric grid for power. In this project, we explore a combines residential TOU pricing models with on-site PV and modest energy storage to incentivize solar rooftops in Thailand. We propose a system architecture and forecast based control algorithm to efficiently manage the renewable energy and storage to minimize grid power costs at individual residential.