Hybrid optimization for integration of renewable energy systems into smart grids

Abstract

The significant increase in the human population, energy consumption in daily living is increasing. One approach is to raise the amount of power produced to match the growth in human population, although this is typically practically unfeasible. By controlling demand, electricity consumption and associated expenses may be reduced. As a consequence, we employ Demand-side Management (DSM) to plan different gadgets on loads to reduce energy usage. Incorporating renewable energy sources and reducing energy costs are two significant roles that the smart grid plays. As a result, we suggest a hybrid swarm based Harris Hawks' optimization (S–HHO) algorithm to overcome the optimization difficulties. The suggested approach includes loads of home and business variety. The proposed technique was compared to binary particle swarm optimization (BPSO), multi-objective particle swarm optimization (MOPSO), wind driven optimization (WDO), and multi-objective genetic algorithms (MOGA). Results from simulations were produced using MATLAB software. The results show that the improved hybrid optimization S–HHO reduces the Peak-to-average ratio (PAR) and computational time better than other methods such as WDO, BPSO, MOPSO, and MOGA.