Optimization-based approaches for power loss reduction and voltage profile enhancement in radial distribution systems: A review of capacitor placement strategies

Radial distribution systems (RDSs), which dominate electricity delivery in many developing countries, are inherently prone to high technical losses and poor voltage regulation due to long feeder lengths and predominantly inductive loads. Optimal capacitor placement has therefore emerged as a cost-effective strategy for reducing power losses and enhancing voltage profiles. This paper presents a structured narrative review of optimization-based capacitor placement approaches applied to RDSs, synthesizing analytical, heuristic, and metaheuristic methods reported in the literature. The review systematically examines commonly used power-flow techniques and compares major optimization algorithms, including Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and hybrid frameworks, with respect to solution quality, convergence behavior, computational effort, and practical applicability. The synthesis reveals broad consensus that metaheuristic methods outperform deterministic approaches in complex, nonlinear networks, while also highlighting important trade-offs: algorithms offering marginally higher loss reduction often incur increased computational cost or parameter sensitivity. The review further identifies persistent gaps related to load variability modeling, economic integration, and real-world validation, particularly in data-constrained rural networks. From a practical perspective, the findings indicate that no single optimization technique is universally superior; rather, algorithm selection should be guided by network characteristics, data availability, and implementation constraints. The paper concludes by outlining context-specific research directions to support the deployment of optimization-based capacitor planning in developing power systems such as those in Uganda.