Decomposition of power flow used for optimizing zonal configurations of energy market

Abstract

Abstract-Zonal configuration of energy market is often a consequence of political borders. However there are a few methods developed to help with zonal delimitation in respect to some measures. This paper presents the approach aiming at reduction of the loop flow effect -an element of unscheduled flows which introduces a loss of market efficiency. In order to undertake zonal partitioning, a detailed decomposition of power flow is performed. Next, we identify the zone which is a source of the problem and enhance delimitation by dividing it into two zones. The procedure is illustrated by a study of simple case.

Integrating Evs into Distribution Grids – Examining the Effects of Various Dso Intervention Strategies on Optimized Charging

“…The AC power ŕow is performed with each asset's active and reactive power time series input. The results, such as line loading and line ŕows, are then used as necessary inputs for matrix operations according to Schneider et al (2018) and Kłos et al (2015). By applying their presented methods, the contribution of single assets on the line loading can be obtained.…”

A clustering approach to the definition of robust, operational and market efficient delineations for European bidding zones

“…This is notably a justification for the use of LMPs as a partitioning criterion in the work of Felling et al in [11]. On the other hand, the use of PTDF-based clustering is also regularly featured in academic contributions, yet with alternative purposes like loop flow minimization [12] or the computation of generation shift keys [13]. Furthermore, most of the reviewed contributions rely on conventional bus clustering methods, among which Hierarchical, K-Means and Fuzzy C-Means clustering are particularly represented.…”

Consistent and robust delimitation of price zones under uncertainty with an application to Central Western Europe

“…In contrast to the LMP-method (Duthaler, 2012), (Kang et al, 2013), (Klos et al, 2014), (Kłos et al, 2015), (Sarfati et al, 2015) and (Bergh et al, 2016) apply cluster algorithms based on PTDF-values. (Kłos et al, 2015) aim to reduce loop effects by clustering PTDF values. Their methodology refers to the mentioned goal of CACM to minimize adverse effects of internal transactions on other price zones.…”