A Statistical Approach to Generation Shift Keys

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractBy interlinking power systems, significant welfare gains can be achieved. However, different approaches for coupling electricity markets exist. A recent development in this field is the implementation of flow-based market coupling (FBMC) in Central Western Europe (CWE). Indeed, FBMC has proven to be advantageous compared to the former situation in CWE Amprion et al. 2014 and to be operationally manageable under current market conditions. However, analyses of possible future adjustments of market areas ("bidding zones") have shown problems in assessing and even understanding FBMC Entso-E 2018. Therefore, Part I of this two-part paper contributes to resolving these problems. First, it presents key issues and effects of all essential FBMC elements. This is done drawing the feasible regions of the FBMC constraints -a method that is well-known from optimization theory. However, in the way it is applied to FBMC it is novel and offers significant insights. These insights are presented on the basis of a stylized yet fully transparent and reproducible example. Thereby, we improve the understanding of benefits and shortcomings of FBMC. Second, we introduce a large-scale model framework capable of assessing FBMC as implemented in CWE. This enables us to assess real-world power systemsalso when undergoing structural changes. This lays the foundation for the case study presented in Part II.

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“…3 Note that statistical procedures were proposed recently (cf. [Schönheit and Sikora 2018]). However, they have yet to be validated and tested.…”

Section: Tso Procedures For D-2 Calculationsmentioning

confidence: 99%

Flow-Based Market Coupling Revised - Part I: Analyses of Small- and Large-Scale Systems

2019

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“…However, due to the sparsity of the data set D LST , even for fixed (b, A), multiple solutions exist for the GSKs and PAs. To simplify the analysis and increase data density, we assume that the GSK and base case are scenario independent, which is in agreement with literature proposals of defining the GSKs [1], [3], [5]. Thus we have that G 1 s,t = G 1 t and g 0 s,t = g 0 t , which immediately implies that the s dependence of the {P T DF s,t } (s,t)∈ST and {F ref s,t } (s,t)∈ST data comes exclusively from A s and b s .…”

Section: Mathematical Optimisation Modelmentioning

confidence: 82%

“…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%

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

Puiu¹,

Hauser²

2021

Preprint

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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.

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“…Investigation of FBMC is not completely new. For instance, Van den Bergh and Delarue (2016), Dierstein (2017), Finck et al (2018), Schönheit and Sikora (2018), and Sebestyén et al (2018) assess the effect of GSKs, while Wyrwoll et al (2018) and Felling et al (2019) investigate different FBMC parametrizations of considered branches and security margins. The role of forecast errors is only examined in Felling et al (2019).…”

Section: What Are the Impacts Of Adjustments To The Capacity Allocatimentioning

confidence: 99%

“…2 Note that the geographical scope of the studies in Sebestyén et al (2018) and Finck et al (2018) are limited (i.e., using a detailed grid model for Belgium only or exclusively modeling Germany, Poland and Czech Republic, respectively). 3 For example, GSK improvements are suggested in Van den Bergh and Delarue (2016) or Schönheit and Sikora (2018). capacity to the market coupling process. To this end, the responsible TSO may decide to reduce the RAM of intra-zonal or cross-zonal transmission lines in order to restrict crossborder trade.…”

Section: Introductionmentioning

confidence: 99%

Flow-Based Market Coupling – What Drives Welfare in Europe’s Electricity Market Design?

2019

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A Statistical Approach to Generation Shift Keys

Cited by 9 publications

References 3 publications

Flow-Based Market Coupling Revised - Part I: Analyses of Small- and Large-Scale Systems

Flow-Based Market Coupling Revised - Part I: Analyses of Small- and Large-Scale Systems

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

Flow-Based Market Coupling – What Drives Welfare in Europe’s Electricity Market Design?

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