A risk-based energy management system design for grid-connected smart homes

One of the energy management system (EMS) goals in the smart home (SH) is to achieve cost reduction besides consumer risk minimization. For these purposes, SH will be equipped with combined heat and power (CHP) generation, wind turbine (WT), and photovoltaic (PV) resources. EMS in SH faces uncertainty due to variable generation of these resources and in-operation of the switch connected to the network. In this article, proposed comprehensive algorithm for EMS of SH including WT, PV, battery energy storage system, CHP considering probability of mal-operation of tie-switch between SH and grid. In this regard suggested algorithm provides consumer risk reduction in SH EMS problem regarding to uncertainty of market price. It is so crucial that all of triple uncertainties of PV and WT resources and tie switch mal-operation are considered as residential consumer risk constraints to achieve accurate results. Genetic algorithm (GA) is used as optimization method for solving of risk-based SH EMS problem. Proposed EMS algorithm is implemented for test SH via simulation studies using MATLAB software. Results indicate presented risk-based GA increases the thermal and energy storage by 20.25% and 14.28% and reduces the consumer risk when a blackout occurs by increasing the spinning reserve.