Smart Home Energy Management Considering Worst-Case Scenarios

Home energy management system (HEMS) enlarges participation of consumers into the demand response program. Day ahead energy management algorithms are simple and require less calculation effort, but mal-operates if any fluctuation occurs at real time from the day ahead schedule. Therefore day ahead HEMSs need special attention at design level to make them adaptive for any uncertainties. This paper investigates worst-case scenario for any smart home, equipped with different ac and dc domestic loads, under different worst conditions regarding market energy price, rooftop solar generation and islanding from the upper utility. The entire idea is implemented on a smart home, designed as an ac/dc microgrid (MG), having practical appliance data sets to prove the efficacy of the proposed MILP based worst condition management for a smart home.