Data-Driven Case-based Grid Segmentation for Local Flexibility Markets

Abstract

Local flexibility markets have emerged as a promising market-based approach to accommodate the increasing penetration of distributed energy resources in distribution grids in a cost-efficient manner. This paper presents a novel fast data-driven methodology for the segmentation of medium-voltage distribution networks into flexibility zones based on historical operation data. Within each flexibility zone, activating active power flexibility has a similar effect on network constraints regardless of the specific connection node. This allows the DSO to assess and procure flexibility needs at zonal level, while enabling aggregators to manage and optimize their flexibility portfolios by zone. A case study is conducted to validate the methodology and the performance of the proposed data-driven grid segmentation by comparing with the online zones computation approach. The results demonstrate the advantages of the proposed grid segmentation in terms of computational efficiency in real-time flexibility procurement.

Local flexibility markets have emerged as a promising market-based approach to accommodate the increasing penetration of distributed energy resources in distribution grids in a cost-efficient manner. This paper presents a novel fast data-driven methodology for the segmentation of medium-voltage distribution networks into flexibility zones based on historical operation data. Within each flexibility zone, activating active power flexibility has a similar effect on network constraints regardless of the specific connection node. This allows the DSO to assess and procure flexibility needs at zonal level, while enabling aggregators to manage and optimize their flexibility portfolios by zone. A case study is conducted to validate the methodology and the performance of the proposed data-driven grid segmentation by comparing with the online zones computation approach. The results demonstrate the advantages of the proposed grid segmentation in terms of computational efficiency in real-time flexibility procurement.