Robust Day-Ahead Energy Scheduling of a Smart Residential User Under Uncertainty

This paper develops a robust optimization framework for the day-ahead energy scheduling of a grid-connected residential user. The system incorporates a renewable energy source (RES), a battery energy storage system (BESS) as well as elastic controllable and critical noncontrollable electrical appliances. The proposed approach copes with the fluctuation and intermittence of the RES generation and non-controllable load demand by a tractable robust optimization scheme requiring minimum information on the sources of uncertainty. The main objective is minimizing the total energy payment for the user considering operational/technical constraints and a contractual constraint penalizing the excessive use of energy. The presented framework allows the decision maker to define different robustness levels for uncertain variables, and to flexibly establish an equilibrium between user's payment and price of robustness. To validate the effectiveness of the proposed framework under uncertainty, we simulate the dynamics of a residential user as a case study. A comparison between the proposed robust approach and the same method with deterministic RES and loads profiles is carried out and discussed.