Optimizing Local Flexibility Market Tenders: A Probabilistic Approach to Spatial and Temporal Aggregation for Efficient Grid Management

The growing dependence on electricity strains distribution systems, especially during peak demand. Local Flexibility Markets offer a market-based solution, enabling Distribution System Operators (DSOs) to procure flexibility services for congestion management. However, this implies a transition for DSOs from the role of grid operators to market actors; however, setting up market tenders capable of ensuring the needed flexibility while minimizing costs is complex. This study adopts a probabilistic approach to account for grid load uncertainties, considering three cost components: capacity availability, activation, and unmet flexibility demand. While previous research optimized tenders for specific grid locations and time, broader coverage is essential for efficiency. However, aggregating flexibility needs into a few products, across areas and periods, risks cost inefficiencies and market failures due to supply-demand disparities. This paper proposes an optimization strategy for spatial and temporal aggregation, minimizing costs while enabling DSOs to manage tenders, required quantities, and market scope efficiently.