ABSTRACTThis study reviews recent developments in optimization techniques for hybrid solar photovoltaic and wind energy systems, particularly those using artificial intelligence (AI) and hybrid algorithms. Due to the global need for sustainable energy, the study compares both traditional and modern optimization techniques. It shows that hybrid algorithms, like, Gray Wolf–Cuckoo Search Optimization (GWCSO), can speed up convergence and reduce costs by up to 25% compared with other conventional methods, such as linear programming. The study groups optimization techniques into traditional, software‐based, AI‐driven, and hybrid approaches; assessing how well they improve system efficiency, reliability, and cost. It also outlines sizing methods and their economic, technical, and environmental effects, with results showing that AI‐driven methods can lower the levelized cost of energy by 10%–15% in complex microgrids (MGs). The study further provides a structured way to size MGs, addressing a gap in optimization methods for independent hybrid systems in remote locations. Greater flexibility of hybrid algorithms in handling complex optimization problems was emphasized. Ultimately, this study offers new insights into combining AI with traditional methods, suggesting future research directions for both smart grid and MG design.
Read full abstract- All Solutions
Editage
One platform for all researcher needs
Paperpal
AI-powered academic writing assistant
R Discovery
Your #1 AI companion for literature search
Mind the Graph
AI tool for graphics, illustrations, and artwork
Journal finder
AI-powered journal recommender
Unlock unlimited use of all AI tools with the Editage Plus membership.
Explore Editage Plus - Support
Overview
11580 Articles
Published in last 50 years
Articles published on Wind Loads
Authors
Select Authors
Journals
Select Journals
Duration
Select Duration
11292 Search results
Sort by Recency