Joint planning and operations of wind power under decision-dependent uncertainty

We study a joint wind farm planning and operational scheduling problem under decision-dependent uncertainty. Geographic heterogeneity in wind power resources induces stochastic fluctuations that can partially offset one another—a phenomenon known as the smoothing effect. Capturing this effect requires strategic capacity allocation, which introduces decision-dependent uncertainty. At the same time, joint planning and operations must address long-term complexity, where traditional stochastic optimization faces a trade-off between statistical robustness and computational burden. To address these challenges, we propose a two-stage distributionally robust optimization model with a decision-dependent Wasserstein ambiguity set, where both the distribution and the radius adapt to planning decisions. The resulting decision-dependent radius contracts at the O ( N − 1 / 2 ) rate and avoids the dimensionality dependence. The model is reformulated as a mixed-integer second-order cone programming, incorporating an empirical approximation and a regularization term that includes variance-covariance estimates for the wind power resource of each farm. We further establish finite-sample guarantees on both cost and power stability under the theoretically calibrated radius. These theoretical insights motivate an asymmetric sampling strategy that leverages large datasets for variance-covariance estimation while using smaller datasets for optimization. To improve computational efficiency, we develop a constraint generation based solution framework that accelerates the solution procedure by hundreds of times. Numerical experiments using different datasets validate the effectiveness of the solution framework and demonstrate the superior performance of the proposed model in risk management. Our results offer clear managerial implications for renewable energy planning under uncertainty: (i) information quality outweighs quantity—covariance information should be incorporated only when it is sufficiently reliable; otherwise, it may compromise power stability; and (ii) effective data utilization is more valuable than simply increasing data quantity—the asymmetric sampling strategy can achieve comparable performance with substantially lower computational effort.