Techno‐Economic Design of a Hybrid Photovoltaic–Wind System for a Residential Microgrid Considering Uncertainties Using Dynamic Parameters Bald Eagle Algorithm

This paper presents a probabilistic cost‐based model for grid‐connected photovoltaic (PV)–wind hybrid system design, employing probability density functions (PDFs) and Monte Carlo simulation (MCS) to address renewable generation and load demand uncertainties. The proposed scenario‐based approach features an innovative objective function incorporating weighted scenario costs, allowing controlled load shedding through energy not supplied (ENS) penalties while enforcing system reliability via a loss of power supply probability (LPSP) constraint. For optimization, we develop a dynamic parameter bald eagle search (DP‐BES) algorithm, demonstrating through MATLAB simulations its superior performance over Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) methods, with the hybrid PV–wind configuration achieving maximum cost reduction (41%) compared to standalone PV (33%) or wind (25%) systems.