# Sustainable sizing, dispatch, and resilience planning of hybrid microgrids using Arctic Puffin Optimization

## TL;DR

A new optimization method called Arctic Puffin Optimization is proposed to design efficient and eco-friendly hybrid microgrids in remote areas.

## Contribution

The novel Arctic Puffin Optimization framework improves hybrid microgrid planning by integrating techno-economic and environmental factors.

## Key findings

- APO achieves up to 8% lower Annual System Cost compared to other optimization algorithms.
- The method enables zero Loss of Power Supply Probability while increasing renewable energy use by 17%.
- Sensitivity analyses confirm the robustness of APO-optimized microgrid configurations under varying conditions.

## Abstract

Hybrid microgrids combining photovoltaic (PV), wind turbine (WT), diesel generator (DG), and battery energy storage systems (BESS) provide a practical pathway for delivering reliable and low-carbon energy to isolated regions. However, their optimal sizing and dispatch planning constitute a challenging multi-objective problem due to renewable intermittency, battery degradation, and competing economic–environmental trade-offs. This paper proposes a novel Arctic Puffin Optimization (APO)-based framework for the techno-economic planning of standalone hybrid microgrids. The model simultaneously minimizes the Annual System Cost (ASC), carbon dioxide (CO2) emissions, and Loss of Power Supply Probability (LPSP) through integrated component sizing, dispatch optimization, and adaptive constraint handling. Two real-world case studies from Ras Ghareb, Egypt, using hourly solar, wind, and load profiles validate the proposed approach. Comparative results demonstrate that APO consistently outperforms Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), and Starfish Optimization Algorithm (SFOA), achieving up to 8% lower ASC, 17% higher renewable penetration, and zero LPSP while maintaining stable convergence behavior. Sensitivity analyses across varying load demands, wind speeds, irradiance levels, and generator constraints confirm the robustness of the optimized configurations. By directly incorporating emission costs and battery degradation into the objective function, the framework ensures realistic, economically viable, and environmentally responsible system design suitable for off-grid hybrid energy applications.