Price-Based Demand Response Participation in Implicit Balancing Services: A Value-Oriented Inverse Optimization Framework

Indirect Demand Response (IDR) programs that incentivize consumer participation through unidirectional price-based mechanisms offer a promising way to mobilize small-scale flexibility. The main challenge in such IDR programs lies in modeling the uncertain price-response relationship of demand response resources (DRRs), which complicates DR pricing. Inverse Optimization (IO) provides an effective method for capturing historical price-response patterns with interpretability and seamless integration into retailers' decision-making frameworks. While traditional IO prioritizes forecast accuracy, recent research advocates for value-oriented forecasting, which prioritizes decision quality to mitigate the practical impacts of forecast errors. Despite their advantages, fully integrated value-oriented approaches can become computationally intensive for IO. To address this, we propose a novel value-oriented IO (VOIO) framework that facilitates a data-driven, value-oriented identification of DRRs' aggregate price-response parameters by shifting IO hyperparameter evaluation metrics to decision-making regret minimization. The proposed method was applied in a case study examining a Balance Responsible Party & sacute; (BRP) participation in the Belgian single-price imbalance market. The results demonstrated that the VOIO approach was able to reduce overall financial losses from forecast errors compared to a forecast-oriented IO benchmark, achieving overall profit gains in the validation and test sets through more conservative pricing.