Entropy-Based Uncertainty in Onshore and Offshore Wind Power: Implications for Economic Reliability

Fernando M. Camilo, Paulo J. Santos and Armando J. Pires ()
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Fernando M. Camilo: Escola Superior de Tecnologia de Setúbal, Instituto Politécnico de Setúbal, Campus do IPS, Estefanilha, 2910-761 Setúbal, Portugal
Paulo J. Santos: Escola Superior de Tecnologia de Setúbal, Instituto Politécnico de Setúbal, Campus do IPS, Estefanilha, 2910-761 Setúbal, Portugal
Armando J. Pires: Escola Superior de Tecnologia de Setúbal, Instituto Politécnico de Setúbal, Campus do IPS, Estefanilha, 2910-761 Setúbal, Portugal

Energies, 2025, vol. 18, issue 10, 1-16

Abstract: The increasing penetration of wind power—driven by the expansion of offshore projects and the repowering of existing onshore installations—poses novel challenges for power system operators. While wind energy is currently integrated without curtailment and considered fully dispatchable, its inherent variability introduces growing concerns due to its rising share in installed capacity relative to conventional sources. In Portugal, wind energy already accounts for approximately 30% of the total installed capacity, with projections reaching 38% by 2030, making it the country’s second largest energy source. In the context of the 2050 carbon neutrality targets, quantifying and managing wind power uncertainty has become increasingly important. This study proposes an integrated methodology to analyze and compare the uncertainty of onshore and offshore wind generation using real-world high-resolution data (15 min intervals over a three-year period) from three onshore and one offshore wind turbine. The framework combines statistical characterization, probabilistic modeling with zero-inflated distributions, entropy-based uncertainty quantification (using Shannon, Rényi, Tsallis, and permutation entropy), and an uncertainty-adjusted Levelized Cost of Energy (LCOE). The results show that although offshore wind energy involves higher initial investment, its lower temporal variability and entropy levels contribute to superior economic reliability. These findings highlight the relevance of incorporating uncertainty into economic assessments, particularly in electricity markets where producers are exposed to penalties for deviations from scheduled generation. The proposed approach supports more informed planning, investment, and market strategies in the transition to a renewable-based energy system.

Keywords: entropy; wind generation; uncertainty quantification; economic reliability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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