Co-optimizing of grouping and aggregated bidding for multiple wind farms under uncertainties

Market-based day-ahead bidding is increasingly used to support renewable integration, but wind producers remain exposed to imbalance costs and revenue volatility due to forecast errors. While many risk-mitigation approaches rely on external flexibility resources, systematic methods for internal coordination among multiple wind farms that jointly address grouping, bidding, and benefit allocation remain limited. This paper proposes an uncertainty-driven framework for cooperative wind-farm grouping and aggregated day-ahead bidding under exogenous day-ahead prices and wind-power uncertainty. Complementarity among wind farms is quantified and verified using scenario-based over- and under-generation patterns together with tail dependence, while the grouping and bidding decisions favor complementary combinations through a risk-aware objective on aggregated imbalance losses. Beyond the grouping and bidding co-optimization layer, the main contribution lies in a tail-consistent and budget-balanced settlement mechanism that translates the uncertainty-reduction benefits created by cooperative aggregation into implementable member-level payments. Energy revenues are allocated through a stable capacity-proportional rule, and the risk-savings pool is redistributed using scenario-level imbalance attribution and tail-responsibility weights. Numerical experiments based on exogenous day-ahead price profiles and simulated wind data show that the proposed framework reduces imbalance risk, improves risk-adjusted revenues, and outperforms benchmark allocation rules. The case study also indicates that the resulting settlement remains transparent and practically attractive at the member level, while sensitivity analyses confirm robustness to key risk parameters.