# Cost-effective and sustainable operation of microgrids using Improved Whale Optimization Algorithm

## TL;DR

This paper introduces a new algorithm to manage energy in microgrids more efficiently, reducing costs and improving sustainability.

## Contribution

The novel Improved Whale Optimization Algorithm (IWOA) with Lévy flight and nonlinear swimming parameters enhances microgrid energy management.

## Key findings

- IWOA reduces operational costs by 39.66% compared to standard algorithms.
- The algorithm maintains a competitive runtime of 4.2 minutes for energy optimization.
- Dynamic energy trading with the main grid improves economic and environmental performance.

## Abstract

The global transition to sustainable energy demands efficient integration of renewable resources and resilient operation of microgrids (MGs). This study aims to develop a cost-effective and sustainable Energy Management System (EMS) for MGs operating in both grid-connected and islanded modes. The inherent variability of renewable generation and fluctuating grid prices pose significant challenges to maintaining supply-demand balance. To address this, the proposed EMS employs an Improved Whale Optimization Algorithm (IWOA), incorporating a nonlinear swimming parameter and Lévy flight mechanism to prevent premature convergence. Simulation results on a benchmark low-voltage MG reveal that IWOA achieves a 39.66% reduction in operational costs compared to standard algorithms, while maintaining competitive runtime of 4.2 min. Furthermore, a dynamic energy trading strategy is integrated to optimize real-time interactions with the main grid. The findings validate the proposed framework as a robust solution for enhancing the economic and environmental performance of modern power systems.