Wind power development in Brazil's Northeast region faces mounting economic pressure as the most productive areas fill with operating projects and grid-connection infrastructure becomes proportionally expensive. Traditional planning methods handle site selection and turbine positioning as independent steps, a practice that yields suboptimal outcomes when connection costs approach turbine capital costs.
Researchers have introduced Wind Farm Swarm Optimization (WFSO), a particle swarm-based algorithm that solves both problems simultaneously within a unified mathematical framework. The outer optimization loop repositions candidate farm locations across a continuous search space, while the inner loop refines turbine arrangement for each candidate site. Critically, both levels optimize levelized cost of energy (LCOE) that dynamically incorporates connection expenses, interference from existing wind farms, and available grid interconnection scenarios.
Validation on a 3,500 km² region in Rio Grande do Norte employed real-world resource maps, infrastructure networks, and regulatory constraints. Results showed convergence across four PSO configurations, with the lowest-cost solution achieving 249.64 R$/MWh. Computational efficiency improved as evaluation budgets increased—beyond roughly 11,400 function evaluations, independent algorithm runs consistently found the optimal basin. A practical configuration completed in 3.6 hours stayed within 0.27% of reference solutions requiring 38.6 hours.
Comparison against a sequential baseline—site selection followed by layout optimization using identical inner-loop logic—revealed significant economic advantage. The integrated approach delivered levelized costs 9.4% lower, directly translating to improved project economics. On reduced benchmark cases verified through exhaustive search, the algorithm matched theoretical optima in 10 of 10 independent runs.
This methodology addresses a practical pain point for developers scouting multiple candidate regions. The combination of endogenous connection-cost modeling, rapid convergence, and validated accuracy makes WFSO suitable for deployment in prospecting campaigns where time and computational resources are constrained.
