Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets

TL;DR

This paper introduces a network-aware optimization method for local flexibility markets that considers physical grid constraints and uncertainty, improving computational efficiency while maintaining social welfare.

Contribution

It presents a chance-constrained optimization approach using LinDistFlow that avoids sharing sensitive data and outperforms stochastic benchmarks in speed with comparable results.

Findings

Outperforms stochastic market-clearing in computation time

Achieves similar social welfare and costs

Scales effectively to real-sized distribution grids

Abstract

This paper proposes a method to design network-aware flexibility requests for local flexibility markets. These markets are becoming increasingly important for distribution system operators (DSOs) to ensure grid safety while minimizing costs and public opposition to new network investments. Despite extended recent literature on local flexibility markets, little attention has been paid to quantifying the flexibility required at each location, considering physical network constraints (e.g. line and voltage limits). The method introduced uses a chance-constrained optimization model and a LinDistFlow approximation to consider both physical network constraints and uncertainty caused by renewable production or demand fluctuations. Unlike other methods, it avoids sharing sensitive grid data with the market operator. We compare our approach against a stochastic market-clearing mechanism which serves as a benchmark, and we derive analytical conditions for the performance of our method to determine flexibility requests. We show on two case studies that our method outperforms the stochastic market-clearing benchmark in terms of computation time while achieving comparable social welfare and costs for the DSOs. One of the case studies is conducted on an actual German distribution grid, showing that the proposed method can scale well to real-sized networks.