The Benefit of Coordinating Congestion Management in Germany

Abstract

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. The management of congestion within the German electricity transmission network has become more important during the last years. This emerging relevance is caused by the increase of renewable generation and the partial phaseout of nuclear power plants. Both developments yield a change in the transmission flow pattern and thus the need for congestion management. Currently, four German transmission system operators (TSOs) are in charge of managing congestion using curative methods, particularly re-dispatch of power plants. However, the existence of four TSOs within Germany induces the question whether coordination between them in managing national congestion would be beneficial. To address this issue, we apply a generalized Nash equilibrium model to analyze different degrees of coordination, covering the German electricity market with a detailed representation of the generation and network structure. Our results indicate that the costs of congestion management decrease in a rising degree of coordination as TSOs take into account congestion in other operators' zones. Total costs are highest in case each TSO is solely responsible for its own zone, and lowest if one integrated entity is in charge of mitigating congestion. We conclude that, in a setup with multiple TSOs, inducing coordination, for instance through a common market, has the potential of lowering the overall costs of congestion management. Terms of use: Documents inJEL Codes: C61, L94, Q40

A multi-attribute congestion-driven approach for evaluation of power generation plans

“…In this paper, we assume that transmission network constraints are not taken into consideration in market‐clearing procedure and any infeasibility will be relieved in TCM. This procedure is similar to TCM procedure in Germany . Performing TCM for the normal operating case. If the results of market‐clearing procedure cause transmission constraint violation, TCM needs to be performed.…”

“…If the results of market‐clearing procedure cause transmission constraint violation, TCM needs to be performed. The procedure is similar to classical TCM in which is briefly described in the APPENDIX A. Calculating the following TCM attributes: It is noted that the list is open for the decision makers to add or replace any attributes that might be a matter of importance for them. TCM cost [ TCMC ]…”

“…This attribute denotes the cost of rescheduling generators and loads for mitigation of congestion and is equal to the optimum value of TCM procedure [17,19]. TCM cost should be paid by the energy market players and might decrease the social welfare [19].…”

“…We assume that market clearing is tackled by ISO and TCM is the task of TSO. This is similar to TCM procedure performed in Germany and some other European countries [17]. Generators' cost functions are considered linear, i.e.…”

An Integrated Approach to Model Redispatch and to Assess Potential Benefits from Market Splitting in Germany

“…Burstedde 2012 p. 6). With focus on Germany, Kunz and Zerrahn (2013) analyse the impacts of different degrees of coordination between TSOs within a generalized Nash game on redispatch costs and volumes. Although the hourly resolution of the model, the nodal representation of the German transmission network and the dispatch optimization on block level are suitable for the modelling of redispatch, the authors do not take into account limitation of units through unit commitment restrictions like ramping constraints, minimum generation and online/offline times.…”