Optimal load scheduling for residential renewable energy integration

We propose an optimal load scheduling algorithm to minimize energy cost for residential homes in smart grids. The algorithm is designed for smart grids with renewable energy sources, energy storage, and two-way communication and energy dispatch. Each appliance in a home has jobs that can be deferred but have deadlines. The algorithm takes into account day-ahead pricing with inclining block rates from energy retailers, local energy generation information from renewable sources, and future jobs to make decisions on when to buy or sell energy while still accomplishing the jobs before their deadlines. The algorithm achieves its optimality by formulating a linear optimization problem that can be solved efficiently. Simulation results show that our approach can reduce energy cost by 20% and peak energy consumption by 100% compared to other approaches.