Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets

2026-01-21

AI Summary

This study addresses the challenge of quantifying flexibility prediction uncertainty for aggregators in ancillary service markets, where data scarcity, external dependencies, and user heterogeneity hinder compliance with stringent reliability requirements such as P90. To this end, the authors propose a scalable uncertainty quantification framework that uniquely integrates Monte Carlo Dropout with multivariate conformal prediction. Leveraging large-scale synthetic data and a surrogate model based on an industrial-grade home energy management system, the framework generates calibrated, conservatively controlled finite-sample prediction intervals. The approach not only satisfies regulatory compliance but also substantially reduces risks of over-bidding and contract violations. When embedded into a bidding strategy, it achieves approximately 70% of the theoretical optimal profit under near-perfect information conditions.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationMultiagent Systems: Multiagent Systems under UncertaintyReasoning under Uncertainty: Stochastic Optimization

Application Category

Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAIUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsSecurity and Privacy: Large-scale security measurements

Abstract

Reliable forecasting of prosumer flexibility is critical for demand response aggregators participating in frequency controlled ancillary services market, where strict reliability requirements such as the P90 standard are enforced. Limited historical data, dependence on exogeneous factors, and heterogenous prosumer behaviour introduce significant epistemic uncertainty, making deterministic or poorly calibrated probabilistic models unsuitable for market bidding. This paper proposes the use of scalable uncertainty quantification framework that integrates Monte Carlo dropout (MCD) with conformal prediction (CP) to produce calibrated, finite sample prediction intervals for aggregated prosumer flexibility. The proposed framework is applied to a behind-the-meter aggregator participating in the Danish manual frequency restoration reserve capacity market. A large-scale synthetic dataset is generated using a modified industry-grade home energy management system, combined with publicly available load, solar, price, activation and device-level data. The resulting machine learning surrogate model captures aggregate prosumer price responsiveness and provides uncertainty-aware estimates suitable for market bidding. Multiple multivariate CP strategies are evaluated and benchmarked against conventional MCD-based methods. Results show that standalone MCD systematically overestimates available flexibility and violates P90 compliance, whereas the proposed MCD-CP framework achieves reliable coverage with controlled conservatism. When embedded in aggregator bidding model, conformalised methods substantially reduce overbidding risk and achieve upto 70% of perfect-information profit while satisfying regulatory reliability constraints, providing practical, computationally efficient, and market-compliant solution for aggregator flexibility forecasting under uncertainty.

Problem

• prosumer flexibility
• uncertainty quantification
• ancillary service markets
• P90 compliance
• demand response aggregation

Innovation

• conformal prediction
• Monte Carlo dropout
• uncertainty quantification
• prosumer flexibility
• ancillary service markets