Load Scheduling of Smart Net-Zero Residential Buildings Based on Pandemic Situation

Abstract

Load scheduling is an effective way of utilizing loads of smart residential buildings according to the preferences of the inhabitants or grid demands, while reducing the cost of energy. This work proposes objective functions for load scheduling to confine the cost of energy within the consumers’ preference range while keeping the load consumption closer to the load demand as possible, to minimize system loss during normal and pandemic condition such as COVID-19 periods, fulfilling the unique features of a net-zero energy building. The proposed objective function is implemented by considering the realistic grid power cost, levelized cost of renewable sources, battery, and incentives offered by the utility system existing in California, USA. In addition to three different types of days such as normal working days, weekends and pandemic situations, brown out power outages are considered as operating conditions. Particle swarm optimization (PSO) is utilized in all considered operating conditions. Two terms that account for the total energy cost savings and the total delayed/scheduled load over a fixed time horizon are formulated as performance indices to illustrate the effectiveness of the proposed objective functions for load scheduling. All of the cases are optimized by the Paticle Swarm Optimization (PSO) and non-optimized systems are simulated in the MATLAB environment. It is evident from the simulation results that the proposed objective function is very efficient in tackling the energy resources, loads and grid power to maximize cost savings and minimize shifting of loads for later hours for normal and pandemic situations in net-zero energy buildings. Moreover, it is equally effective in responding to any emergency situations such as brown out energy crisis situations, which are not considered in the literature so far. In all cases, the performance index also validates the effectiveness of the proposed objective function-based scheduling system for net-zero energy buildings.