Risikomanagement in der Direktvermarktung erneuerbarer Energien

  • Risk management in direct marketing of renewable energies

Thie, Nicolas; Schnettler, Armin (Thesis advisor); Wagner, Ulrich (Thesis advisor)

1. Auflage. - Aachen : Verlagshaus Mainz GmbH (2020)
Book, Dissertation / PhD Thesis

In: Aachener Beitr├Ąge zur Hochspannungstechnik 70
Page(s)/Article-Nr.: 1 Online-Ressource (ii, 139 Seiten) : Illustrationen, Diagramme

Dissertation, RWTH Aachen University, 2020


In conjunction with the worldwide expansion of distributed energy resources(DER), the call for stronger market integration increases. In this process, aggregators, who pool DER and control them centrally, have established themselves as service providers for the direct marketing of distributed generation. In direct marketing, aggregators are exposed to risks due to forecast uncertainties, in particular due to intermittent generation. These risks can impair the profitability of direct marketing. Therefore, the quantification of risk exposure and the derivation and use of risk management measures are becoming increasingly important. However, existing market scheduling methods mostly take into account only individual uncertainties and do not permit a comprehensive assessment of risk management. The goal of this thesis is to develop a method which can determine an optimal market scheduling under uncertainty and at the same time apply and evaluate risk management measures in a targeted manner. From the analysis of related research fields, the following measures are identified: hedging with futures, the use of flexibilities as well as regional diversification of DER. The developed method is divided into two steps: scenario generation and market scheduling under uncertainty. Within the scenario generation, the uncertainties are modelled in the form of stochastic scenarios. They represent the statistical properties of the underlying uncertainties (probability distribution, autocorrelation and crosscorrelation).The market scheduling is implemented in the form of stochastic mixed-integer linear programming. On the basis of the scenarios, an optimal scheduling decision is determined with the objectives of expected revenue (or return on investment) and the risk measure Conditional-Value-at-Risk (CVaR).The entire scheduling process of an aggregator is considered as a whole in order to evaluate interdependencies between markets and risk management measures. The method is validated for an exemplary case and used to evaluate the risk management measures. The combined application of all investigated measures can significantly increase profitability. Thus, an increase of the expected return by 15%-points and of the CVaR by approx. 20%-points is achieved compared to the benchmark without risk management. As the main criterion for the effect of the measures can be identified as their influence on volume risks and thus the balancing energy demands. The more the measures can reduce the balancing energy demands, the more they improve the profitability in direct marketing.