Independent aggregators securing end user Wasserstein distributionally robust flexibility through bilevel incentives

The imperative for increased power system flexibility, driven by the energy transition, positions Independent Aggregators (IAs) as central to integrating Distributed Energy Resources (DERs). However, the inherent uncertainty of DERs limits their participation in reserve markets and complicates the design of economic incentives through bi-level optimization methods. To address this challenge, this paper proposes a bi-level optimization framework that employs a novel reformulation of Wasserstein distributionally robust joint chance constraints. The approach enables IAs to mobilize stochastic DER flexibility through robust incentives while securing reserve provision. The problem is reformulated as a single-level mixed-integer linear program using Karush-Kuhn-Tucker conditions and a Faster Inner Convex Approximation (FICA) technique. This provides computationally fast and accurate probability guarantees for reserve delivery. Empirical validation using Spanish market data demonstrates that the proposed FICA-enabled framework for DER aggregation substantially enhances economic efficiency and ex-post risk compliance over benchmarks. FICA increases the aggregator profits under stringent robustness while determining optimal incentive combinations that unlock higher flexibility volumes, with less computational burden as single-level approaches. This research offers IAs a practical, robust tool for effective reserve market participation, facilitating DER integration in reserve markets.