Content area
Abstract
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.
Details
Load shedding;
Particle swarm optimization;
Marine mammals;
Electrical loads;
System reliability;
Distributed generation;
Random variables;
Algorithms;
Wind;
Systems design;
Fines & penalties;
Probability density functions;
Energy resources;
Statistical analysis;
Uncertainty;
Radiation;
Photovoltaic cells;
Efficiency;
Design optimization;
Monte Carlo simulation;
Photovoltaics;
Cost reduction;
Renewable resources;
Objective function;
Cost analysis;
Hybrid systems;
Alternative energy sources;
Cost control;
Parameters;
Operating costs
; Shokouhandeh, Hassan 2
; Outbib, Rachid 3
; Colak, Ilhami 4
; El Manaa Barhoumi 5
1 Department of Electrical Engineering Jo.C. Islamic Azad University Jouybar Iran
2 Department of Electrical Engineering National University of Skills (NUS) Tehran Iran
3 LIS UMR CNRS Aix-Marseille University 7020 Marseille France
4 Faculty of Engineering and Natural Science Department of Electrical and Electronics Engineering Istinye University Istanbul Türkiye
5 Department of Electrical and Computer Engineering College of Engineering Dhofar University Salalah Oman