A Multi-Objective ESTO Framework for Efficient PV Siting and Sizing in Radial Distribution Systems

Photovoltaic (PV) systems have become one of the most prominent renewable energy technologies for reducing losses and improving efficiency in power distribution networks. Achieving these benefits requires proper decisions on where PV units should be installed and how large they should be. This paper presents an Enhanced Search and Tuning Optimization (ESTO) algorithm developed to address this allocation problem. The algorithm introduces an adaptive mechanism that balances wide-ranging exploration with local fine-tuning, which enhances solution precision while preventing premature convergence. The proposed method is applied to the IEEE 33-bus and 69-bus distribution test systems using a backward/forward sweep power flow. Results indicate a significant improvement in bus voltage levels—where the lowest voltage in the 33-bus system increased from 0.931 p.u. to 0.968 p.u.—together with a 64.7% reduction in real power losses. Comparisons with existing approaches such as Particle swarm optimization (PSO), Genetic Algorithm (GA), and Ant Lion Optimizer (ALO) confirm that ESTO provides faster convergence, improved reliability, and higher-quality solutions.