Optimization of size and siting of distributed generation in unbalanced distribution systems: A literature review

Abstract

Renewable energy sources (RES) are essential for meeting the rising global electricity demand while reducing greenhouse gas emissions from conventional generation. As traditional systems approach capacity saturation, the integration of RES into power grids becomes increasingly vital. However, the intermittent and variable nature of RES introduces significant technical, economic, and operational challenges. This review focuses on the optimal planning and integration of distributed generation in unbalanced distribution systems, which more accurately reflect real-world power network conditions. Emphasis is placed on siting and sizing strategies aimed at enhancing voltage stability, minimizing power losses, and reducing system costs and emissions. The review presents a comprehensive synthesis of advanced optimization methods, including metaheuristic algorithms and artificial intelligence based approaches, and evaluates multi-objective formulations that address technical, economic, and environmental performance criteria. Key research gaps are identified, notably the limited application of machine learning and reinforcement learning for adaptive control and the inadequate incorporation of policy, regulatory, and uncertainty factors into existing models. By addressing these challenges, this review consolidates the current state of research and outlines future directions for developing intelligent, policy-aware, and data-driven optimization frameworks to support the reliable and sustainable integration of RES in unbalanced distribution networks