Reliable forecasting of prosumer flexibility is critical for demand response aggregators in frequency controlled ancillary service markets, where reliability requirements such as the P90 standard are enforced. Limited behavioural data and dependence on exogenous forecasts introduce uncertainty into flexibility estimates, which can cause reserve overestimation and financial penalties. This study systematically evaluates established uncertainty quantification approaches based on Monte Carlo dropout (MCD), conformal prediction (CP), and conformalised quantile regression (CQR) to construct prediction intervals for aggregated prosumer flexibility and support uncertainty-aware bidding. The approaches are evaluated for a behind-the-meter aggregator in the Danish manual frequency restoration reserve capacity market, using a large-scale synthetic dataset generated with an industry-grade home energy management system and publicly available load, solar, price, activation, and device-level data. Multiple multivariate CP strategies are applied to MCD predictions, alongside CQR using a separately trained quantile regression model. Under nominal test conditions, conformal methods substantially improve joint coverage over uncalibrated MCD baselines. More conservative methods satisfy the 90% lower-tail reliability requirement, while CQR and multivariate CP (MCP) yield narrower intervals and higher economic value, retaining approximately 69% and 68% of perfect-information profit, respectively, under the stricter penalty interpretation. A controlled robustness analysis shows that increasing perturbations of load, solar, price, and activation inputs increase overbidding and reduce coverage and economic performance. The results highlight the trade-off between reliability, economic value, and robustness to input uncertainty in aggregator bidding.

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