The growing share of renewable energy in shortterm European electricity markets has significantly increased congestion management costs and demands. Therefore, current market design is not optional to keep congestion costs low. A proper market would incentivize the integration of flexibilities to boost competition and lower costs, while mitigating risks of manipulation. However, assessing behavioral impacts is challenging due to increasingly interconnected market structures. Studies modeling more than two markets often overlook the strategic opportunities that emerge from these interactions, focusing instead on large-scale dynamics. To capture the detailed impact of bidding strategies, we use reinforcement learning to explore multi-market strategies. By progressively training a Deep Reinforcement Learning (DRL) agent as a market participant - from replicating established behaviors to mastering intricate multimarket interactions - we employ Domain-Informed Curriculum Learning (DomCL), a structured approach that systematically guides learning through staged complexity. We validate our approach against established two-market studies, then evaluate it in two progressively complex four-market case studies spanning a 6-bus network, including historical data. Results show that our DRL-based method improves performance while uncovering challenges that arise as strategic opportunities expand, offering a structured approach for multi-market design analysis.

Background: The transition to a renewable energy based and decentralized energy system poses growing challenges for grid stability, particularly regarding redispatch measures. Traditionally carried out by large-scale conventional power plants at the transmission grid level, redispatch operations must adapt to changing conditions as conventional capacities decline and the need for system flexibility increases. Industrial facilities—especially those connected at the distribution grid level—offer untapped potential for participation in redispatch provision. The project Industry4Redispatch, embedded in Austria’s NEFI model region, explored the technical, regulatory, and economic conditions for integrating industrial assets into the redispatch process, including coordination between transmission system operators (TSOs) and distribution system operators (DSOs), as well as the development of standardized redispatch processes. Approach: This paper summarizes the congestion and redispatch analysis conducted within the project, focusing on Austria in 2030. Considering uncertainties in the spatial distribution of future generation, demand, and flexibility, the model results show consistency with real redispatch outcomes when compared to publicly available data on costs and activation volumes. Besides these uncertainties, the modeling is based on the National Trends+ scenario of the TYNDP 2024 and does not include variations or sensitivity analyses. Conclusions: The results show that integrating industrial flexibility leads to moderate but consistent system benefits, including a reduction in total redispatch costs by up to 1.5% and a decrease in curtailment of renewable generation by about 7.5% in Austria. The findings indicate that industrial flexibility potentials represent a valuable complement to existing redispatch resources and can enhance the efficiency of congestion management. Particularly in the area of negative redispatch, they make an important contribution by reducing the need to curtail renewable generation. Although the overall impact remains limited due to local and temporal constraints, the targeted use of industrial facilities in redispatch activations supports and improves the integration of renewable energy. The results underline the central importance of an adequate system design and targeted regulatory adjustments to enable the reliable and cost-efficient activation of industrial flexibility for redispatch in the future.