An Improved Statistical Approach to Generation Shift Keys: Lessons Learned from an Analysis of the Austrian Control Zone

Schönheit, David

doi:10.1007/s12398-019-00261-w

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To obtain our desired GSKs regularisations, we use the JPW [2] variant of the Sensitivity Coefficients Regression (SCR) model, which was proposed to define the GSKs in Austria [2] and Germany [1]. For each zone z and time τ , the model solves the linear regression problem denoted as R(z, p, τ ),…”

Section: A a Regularisation For The Gsksmentioning

confidence: 99%

“…One can now apply the procedure for all τ ∈ T to obtain the zonal GSKs which we denote as G 1 z (T ) and we denote this procedure as SRC(z, T ). It is suggested in [2] that better results are obtained when using the day-ahead (D-1) version of the regression coefficients. For us, this poses a problem for the left hand side of (12), since the D-1 version of φ z t is g 0 z,t , which is unknown.…”

Section: A a Regularisation For The Gsksmentioning

confidence: 99%

See 1 more Smart Citation

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

Puiu¹,

Hauser²

2021

Preprint

View full text Add to dashboard Cite

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 in-sample relative errors of 22.3% and 15.9% for the PTDFs and RAMs respectively, while the out-ofsample ones are 25.4% and 18.1% respectively. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure.

show abstract

Section: A a Regularisation For The Gsksmentioning

confidence: 99%

Section: A a Regularisation For The Gsksmentioning

confidence: 99%