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Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets

Eléa Prat, Irena Dukovska, Lars Herre, Rahul Nellikkath, Malte Thoma, Spyros Chatzivasileiadis

TL;DR

It is shown that the proposed method to design network-aware flexibility requests for local flexibility markets outperforms the stochastic market-clearing benchmark in terms of computation time while achieving comparable social welfare and costs for the DSOs.

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.

Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets

TL;DR

It is shown that the proposed method to design network-aware flexibility requests for local flexibility markets outperforms the stochastic market-clearing benchmark in terms of computation time while achieving comparable social welfare and costs for the DSOs.

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.

Paper Structure

This paper contains 21 sections, 16 equations, 7 figures, 2 tables.

Figures (7)

  • Figure 1: Conceptual representation of the proposed market design. Top: Benchmark option with a stochastic market clearing where the FMO requires network data. Bottom: Proposed privacy-preserving FlexRequest market design where the FMO only requires knowledge of the zone (DSO area).
  • Figure 2: Social welfare (€) for three levels of liquidity and comparing stochastic market clearing to deterministic market clearing with different ways to define clearing zones, for the 15-bus test case
  • Figure 3: Submitted offers and requests per bus in the case of medium liquidity. The upper part of the graph corresponds to bids up and the lower part to bids down. The filling of the offers bar shows the quantity accepted after the corresponding market clearing.
  • Figure 4: DSO costs (€) for three levels of liquidity and comparing stochastic market clearing to deterministic market clearing with different ways to define clearing zones. The last graph illustrates the real-time costs without a flexibility market, for the 15-bus test case
  • Figure 5: DSO costs (€) for three levels of liquidity and comparing stochastic market clearing to deterministic market clearing with different ways to define clearing zones, for the bnNETZE 81-bus test case
  • ...and 2 more figures