Resilient Scheduling Portfolio of Residential Devices and Plug-In Electric Vehicle by Minimizing Conditional Value at Risk

Home energy management systems (HEMSs) encourage participation of residential consumers into the demand response programs. This paper proposes a robust Conditional Value at Risk (CVaR) optimization approach for day ahead HEMS to reduce the effect of risk of real-time exposure to energy price and solar power generation uncertainties. Initially, the CVaR method is integrated with the two-point Estimation (2PE) analysis to approximate the solar power, modeled as Beta probability distribution function, in low computation effort compared to conventional Monte Carlo simulation (MCS) based CVaR approach. Then the optimization constraints are revised to their robust counterparts by accounting a certain amount of uncertainty in the energy prices from their nominal values. Unlike the previous literatures, the optimization problem is developed to minimize the risk value of the energy cost. Again to maximize the life of the plug-in electric vehicle (PEV) a pseudo cost function for the PEV battery degradation is proposed. The entire optimization portfolio is developed as a mixed integer linear programming for its easy execution. Simulation is demonstrated on a smart home, designed as an ac-dc microgrid, having practical appliance data sets, to prove the efficacy of the proposed method.