Efficiency Assessment of Wind Power Companies Through Multi-Dimensional DEA Performance Comparison

This study conducts a comprehensive evaluation of operational efficiency in Peruvian wind power generation facilities using Data Envelopment Analysis (DEA). Daily generation data from October 2024 to April 2025 were analyzed, reflecting the rapid growth of the national wind sector, which now exceeds 400 MW of installed capacity and supplies nearly 5% of Peru's electricity. The framework incorporates 212 daily operational records from eight major enterprises connected to the national grid. An output-oriented DEA model was applied to maximize energy production efficiency while maintaining infrastructure constraints. The methodology considers three outputs: mean generation capacity, maximum generation potential, and operational consistency measured through the inverse coefficient of variation. This non-parametric approach enables multi-dimensional optimization without restrictive assumptions regarding production technology. Results reveal significant heterogeneity in efficiency levels. ENGIE is the only technically efficient enterprise, achieving a unity score and serving as the benchmark frontier. The other seven enterprises recorded efficiency scores between 0.849 and 0.226, indicating substantial room for improvement. Efficiency gap analysis highlights that the best-performing inefficient enterprise requires 17.8% improvement, while the weakest demands 342.5%. Maximum generation capacity is identified as the main optimization target across most firms, followed by mean generation. These findings provide strategic insights for resource allocation, enterprise-specific optimization, and policy interventions aimed at advancing overall sector performance and supporting Peru's renewable energy transition.