An Innovative Heuristic Algorithm for IoT-Enabled Smart Homes for Developing Countries

Over the past few years, active research on algorithm development for the optimal operations of home energy management systems (HEMSs) has been performed. The objective is to compute optimized schedules for shiftable home appliances. This is based on the demand response (DR) synergized with renewable energy sources and energy storage system optimal dispatch (DRSREOD). An improved algorithm for a DRSREOD-based HEMS is proposed in this paper. This heuristic-based algorithm considers DR, photovoltaic availability, the state of charge and charge/discharge rates of the storage battery and the sharing-based parallel operation of more than one power source to supply the required load. The HEMS problem has been solved to minimize the cost of energy (
$CE$
) and time-based discomfort (
$TBD$
) with conflicting tradeoffs. The mixed scheduling of appliances (delayed scheduling for some appliances and advanced scheduling for others) is introduced to improve the
$CE$
and
$TBD$
performance parameters. An inclining block rate scheme is also incorporated to reduce the peak load. A set of optimized tradeoffs between
$CE$
and
$TBD$
has been computed to address multi-objectivity using a multi-objective genetic algorithm (MOGA) with Pareto optimization (PO) to perform the tradeoff analysis and to enable consumers to select the most feasible solution. Due to the rapid increase in demand for electricity, developing countries are facing large-scale load shedding (LS). An innovative algorithm is also proposed for the optimal sizing of a dispatchable generator (DG) that can supply the DRSREOD-based HEMS during LS hours to ensure an uninterrupted supply of power. The proposed MOGA/PO-based algorithm enables consumers to select a DG of the optimal size from among a number of optimal choices based on tradeoffs between the DG size,
$CE$
, and
$TBD$
.