Conditional value-at-risk model for smart home energy management systems

This paper presents a self-scheduling framework, using risk-constrained optimization model for the home energy management system (HEMS), considering fixed, controllable, and interruptible loads, as new contribution to earlier studies. The objectives are reducing electricity bill managing risk of purchasing over on-peak hours prosumer's discomfort index (DI) due shifting load undesired hours. In this regard, problem formulation is represented mixed-integer linear programming (MILP) model. Afterward, proposed HEMS promoted conditional value-at-risk (CVaR) prosumer equipped with an storage solar photovoltaic (PV) panel. A substantial fraction demand there inverter-based heating, ventilation, air conditioning (HVAC), where HVAC modeled variable-capacity load. optimal scheduling loads supposed be done by HEMS, time-of-use (TOU) mechanism utilized, including three price steps day. results, obtained from thoroughly simulating household data, validate performance presented in mitigating amount bill, while keeping at desired level.