Cost-effective and sustainable operation of microgrids using Improved Whale Optimization Algorithm
Sohayla M. El-Zaher, Aya M. Ahmed, Eman M. Ahmed, Yasmin T. Sedki, Hager K. Al-Muntaser, Ahmed N. Sheta
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.
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…
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Taxonomy
TopicsMicrogrid Control and Optimization · Smart Grid Energy Management · Electric Power System Optimization