An effective energy flow management in grid-connected solar–wind-microgrid system incorporating economic and environmental generation scheduling using a meta-dynamic approach-based multiobjective flower pollination algorithm

Abstract

In this research paper, we focus on developing a generation scheduling model using an intelligent soft-computing technique in a microgrid (MG) system. A multiobjective power management system with innovative features of the MG technology is presented The necessity and reason for undertaking this study is to optimize MG operation as well as address the uncertainty of random energy production from renewables by utilizing demand response (DR) programs. A meta-dynamic-approach-based multiobjective flower pollination algorithm is applied to solve this complex, nonlinear, multiobjective optimization (MOO) problem. Energy management in MGs utilizing renewable energy is a salient feature. DR schemes are conducted in residential, commercial, and industrial customers. Simulations are performed to achieve reduced prices and minimum emissions. Comparative studies were conducted wherein the metaheuristic algorithm demonstrated superior performance and higher efficiency compared to other technique. Operating costs reduced by 20.3% and emissions reduced by 5% after the implementation of DR programs using a meta-dynamic-approach-based flower pollination algorithm compared to particle swarm optimization (PSO). The results demonstrate the superiority of the proposed demand-side management modeling method.