Abstract
ABSTRACT Efficient participation by renewable‐based hybrid energy storage systems (HESSs) in modern electricity markets is challenged by multilayered uncertainties, including wind variability, volatile market prices, and stochastic reserve activation. This paper presents an ambiguity‐aware method that combines Type‐3 fuzzy logic with a receding‐horizon (T3‐RH) decision architecture for coordinated multimarket bidding and dispatch of wind–HESSs. A distribution‐free uncertainty‐modeling approach captures ambiguity in key market variables. The resulting representations are embedded directly within the proposed optimization framework via an ambiguity‐risk‐aware objective that maximizes economic returns while limiting degradation costs and penalizing exposure to uncertainty. A receding‐horizon structure dynamically updates decisions as system conditions and market signals evolve. The effectiveness of the proposed framework is demonstrated through a comprehensive case study involving participation in the day‐ahead and multiple ancillary‐service markets. The proposed T3‐RH method increases net annual profit by 20% relative to deterministic multimarket optimization while reducing ambiguity‐related risk by more than 50%. These gains arise primarily from improved use of balancing‐market opportunities and more reliable reserve commitments under uncertainty. Despite generating higher revenue, the proposed method also reduces degradation costs, indicating more efficient and less aggressive battery operation. These findings demonstrate that explicitly modeling higher‐order ambiguity can improve decision quality in multimarket participation under the conditions studied. The proposed framework provides a structured basis for enhancing profitability and robustness in renewable‐based hybrid energy systems, while its computational and economic scalability must be established on the target hardware and market data.