OPTIMIZATION OF RENEWABLE ENERGY INTEGRATED MICROGRIDS USING MULTI-OBJECTIVE ENERGY MANAGEMENT AND BATTERY STORAGE SYSTEMS

Abstract

The rapid depletion of fossil fuel reserves, increasing electricity demand, and growing concerns regarding greenhouse gas emissions have accelerated the global transition toward renewable energy-based power generation. Microgrids integrated with renewable energy sources such as solar photovoltaic (PV) and wind turbines have emerged as sustainable alternatives for decentralized electricity generation. However, the intermittent and uncertain nature of renewable resources presents significant challenges in maintaining grid stability, energy reliability, and economic operation. Battery Energy Storage Systems (BESS) have become essential components of renewable energy integrated microgrids because they mitigate renewable power fluctuations, improve system reliability, and enhance operational flexibility.

This study proposes a multi-objective energy management framework for hybrid renewable microgrids incorporating solar PV, wind energy conversion systems, Battery Energy Storage Systems, and utility grid interaction. The proposed framework simultaneously minimizes operating cost, greenhouse gas emissions, battery degradation, and power losses while maximizing renewable energy utilization and supply reliability. Three evolutionary optimization techniques, namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO), are employed to determine optimal power dispatch among distributed energy resources.

The proposed Energy Management System continuously monitors renewable generation, battery state of charge, load demand, and electricity market prices to determine optimal scheduling decisions under varying operating conditions. Mathematical models for photovoltaic systems, wind turbines, battery storage, and load forecasting are integrated into the optimization framework. Simulation studies demonstrate that the hybrid optimization approach significantly improves renewable energy penetration, reduces operating costs, enhances battery lifetime, and maintains voltage and frequency stability under different load scenarios.

Comparative analysis indicates that the proposed framework outperforms conventional rule-based dispatch strategies by reducing operational costs by approximately 15–25%, increasing renewable energy utilization, minimizing battery degradation, and improving overall microgrid resilience. The proposed approach offers a practical solution for sustainable smart grid operation and supports future carbon-neutral energy systems.