Local flexibility markets based on grid segmentation

Local flexibility markets are a promising solution to aid system operators in managing the network as it faces the growth of distributed resources and the resulting impacts on voltage control, among other factors. This paper presents and simulates a proposal for an intra-day local flexibility market based on grid segmentation. The design provides a market-based solution for distribution system operators (DSOs) to address near-real-time grid issues. The grid segmentation computes the virtual buses that represent each zone and the sensitivity indices that approximate the impact of activating active power flexibility in the buses within the zone. This approach allows DSOs to manage and publish their flexibility needs per zone and enables aggregators to offer flexibility by optimizing their resource portfolios per zone. The simulation outcomes allow for the assessment of market performance according to the number of zones computed and show that addressing overloading and voltage control through zonal approaches can be cost-effective and counterbalance minor errors compared to node-based approaches. • This study demonstrates how grid segmentation enhances the effectiveness and cost-efficiency of local flexibility markets based on zones. The key findings are: • Cost-effective grid constraint resolution – Flexibility zones efficiently mitigate voltage and overloading issues at a cost nearly identical to the theoretical minimum, even with a limited number of zones. • Enhanced market operations through virtual buses – By using virtual buses, DSOs can provide simplified grid information to LMOs while still ensuring grid constraints are considered in market clearing, beyond a simple merit-order list. • Optimized grid segmentation for accuracy – The effectiveness of grid segmentation increases with the number of zones, with the virtual bus error arround 10 % when more than three zones are used. This supports optimal market performance. The outcomes of the Local Flexibility Market (LFM) are also evaluated using nonlinear power flow equations, demonstrating the feasibility of the proposed approach.