A Distributionally Robust Framework for Improving the Provision of Passive Balancing Services

Abstract

Passive balancing refers to the intentional deviations of market actors from their position in energy markets to support the real-time system balancing. Such a service is typically incentivized in a single imbalance pricing mechanism, where all imbalances are settled at a unique price. In this context, this paper presents a novel distributionally robust decision support tool, based on a bi-level model, for a market actor providing passive balancing services. The uncertainty related to the imbalance price is characterized by a Wasserstein metric-based ambiguity set, collecting all distributions in the neighborhoud of a central empirical one. In addition, the bi-level structure of the model allows capturing the interaction between the intentional imbalance position of the actor and the imbalance pricing mechanism. The proposed approach provides optimal passive balancing services in expectation over a worst-case imbalance price distribution, ensuring performance guarantees even if the probability distribution is not perfectly known. With a realistic case study using real-world market data from the Belgian power system, we validate and compare the proposed approach w.r.t. the performance of stochastic programming and robust optimization models.