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Principled Data Completion of Network Constraints for Day Ahead Auctions in Power Markets

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Ioan Alexandru Puiu, Raphael Andreas Hauser

Systems and Control · Submitted 4 Jun 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our reconstruction achieves good in-sample and out-of-sample relative errors for the PTDFs and RAMs.

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arXiv ID2106.04310 (version 1)
Pith IDBNE4ZY2V, Pith's permanent record of this paper and its review
CitationIoan Alexandru Puiu, Raphael Andreas Hauser. "Principled Data Completion of Network Constraints for Day Ahead Auctions in Power Markets." arXiv:2106.04310 (2021). https://pith.science/paper/BNE4ZY2V
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,948 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.15 · human participants 0.02 · controlled comparison 0.79 · randomized assignment 0.01 · states quantitative result 0.12 · compares with prior results 0.93 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. Our reconstruction achieves good in-sample and out-of-sample relative errors for the PTDFs and RAMs. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. background 0.72 0.09 0.03 asserted 1.00
s1 We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. method 0.81 0.95 0.10 asserted 1.00
s2 This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. method 1.00 0.86 0.05 asserted 1.00
s3 The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. method 1.00 0.87 0.05 asserted 1.00
s4 Our reconstruction achieves good in-sample and out-of-sample relative errors for the PTDFs and RAMs. result 1.00 0.97 0.43 asserted 1.00
s5 We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure. result 1.00 0.96 0.40 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.75.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. Our reconstruction achieves good in-sample and out-of-sample relative errors for the PTDFs and RAMs. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure.

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