Abstract
This comprehensive review examines the state-ofthe-art methodologies for optimal Distributed Generation (DG) placement in modern distribution networks, addressing the critical challenges of power quality enhancement and system reliability. The study systematically analyses various approaches including analytical methods, optimization techniques, artificial intelligence algorithms, graph theory applications, simulation-based methods, and hybrid solutions. Recent research indicates that optimized DG placement can achieve up to 60% reduction in system losses and 15% improvement in voltage profiles. The review highlights that while traditional analytical methods provide mathematically robust solutions, they often face scalability challenges in large networks. Artificial intelligence methods, particularly hybrid approaches combining neural networks with conventional techniques, have demonstrated superior performance with 25% better solution quality and 35% reduced computational time. The paper also explores emerging trends, including real-time optimization and blockchain integration, while addressing critical challenges such as renewable energy intermittency and regulatory frameworks. This review contributes to the field by providing a comprehensive evaluation framework for DG placement methods and identifying promising directions for future research in the context of evolving smart grid technologies.
Published Version
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