The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

This paper presents an integrated framework designed for capacity planning of grid-connected nanogrid, a small solar and energy storage system that can provide kilowatt-level services to individual buildings. This framework comprehensively evaluates nanogrid cost-effectiveness, sustainability, and reliability, employing a multi-faceted techno-enviro-economic assessment approach. Traditional nanogrid capacity planning often prioritizes peak load requirements, which may lack optimality owing to occasional peak load occurrences. Conversely, optimizing solely for base load requirements might also fall short of effectiveness, compromising reliability and sustainability objectives. The proposed framework employs a three-step, integrated process for nanogrid (NG) capacity planning. Firstly, the Planner module identifies optimal asset sizing considering a two-day look-ahead logic. Then, the Operator module serves as a digital twin for the system, conducting hourly calculations over a short-term horizon. Lastly, the Evaluator module evaluates technical, environmental, and economic metrics for each solution, assessing the effectiveness of asset-sizing decisions. A simulated case study has demonstrated the effectiveness of the proposed framework. The technical assessment revealed that a PV size of 24 kW and a storage capacity of 91 kWh led to the most reliable solution, with a probability of local sufficiency of 95 percent. Furthermore, the environmental assessment showcased a renewable fraction of 94% with a PV size of 26 kW and a storage of 85 kWh. Economically, the analysis identified that a PV size of 12 kW and a storage size of 24 kWh led to the minimum total cost. In contrast, a PV size of 26 kW and a storage size of 85 kWh yielded a total operating savings of $4,801.