Complexity Reduction for TSO-DSO Coordination: Flexibility Aggregation vs. Distributed Optimization

The increasing number of flexible devices and distributed energy resources in power grids renders the coordination of transmission and distribution systems increasingly complex. In this paper, we discuss and compare two different approaches to optimization-based complexity reduction: Flexibility aggregation via Approximate Dynamic Programming (ADP) and distributed optimization via the Alternating Direction Method of Multipliers (ADMM). Flexibility aggregation achieves near-optimal solutions with minimal communication. However, its performance depends on the quality of the approximation used. In contrast, ADMM attains results closer to the centralized solution but requires significantly more communication steps. We draw upon a case study combining different matpower benchmarks to compare both methods.

Authors

Institutions

Publication Details

Published
2025-06-29
DOI
https://doi.org/10.1109/powertech59965.2025.11180375
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Complexity Reduction for TSO-DSO Coordination: Flexibility Aggregation vs. Distributed Optimization

Maísa Beraldo Bandeira, Timm Faulwasser, Alexander Engelmann
Modular Robots and Swarm Intelligence
article

Complexity Reduction for TSO-DSO Coordination: Flexibility Aggregation vs. Distributed Optimization

Maísa Beraldo Bandeira, Timm Faulwasser, Alexander Engelmann
article en

Abstract

The increasing number of flexible devices and distributed energy resources in power grids renders the coordination of transmission and distribution systems increasingly complex. In this paper, we discuss and compare two different approaches to optimization-based complexity reduction: Flexibility aggregation via Approximate Dynamic Programming (ADP) and distributed optimization via the Alternating Direction Method of Multipliers (ADMM). Flexibility aggregation achieves near-optimal solutions with minimal communication. However, its performance depends on the quality of the approximation used. In contrast, ADMM attains results closer to the centralized solution but requires significantly more communication steps. We draw upon a case study combining different matpower benchmarks to compare both methods.

Universität Hamburg (DE), TU Dortmund University (DE), X-Fab (Germany) (DE), Hamburg University of Technology (DE)
Openalex Percentile: Top 71%
Modular Robots and Swarm Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.