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. Marc Hanfeld, Stephan Schlüter marc.hanfeld@wintershall.com, stephan.schlueter@hs-ulm.de abstract We investigate, if it pays off for a company to invest into complex swing option algorithms. We first introduce least squares Monte Carlo as a complex valuation algorithm and explain in detail how it works. Using a simulation study and two backtest scenarios we compare the output of this method with a simple myopic approach, and evaluate the results also from a business point of view. We find that myopic operation performs fairly well, but given a certain contract size and a certain contract flexibility, LSMC clearly prevails.
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Global warming and CO2 emission reduction targets mandate a closer look to energy system planning on different levels. In this work we model a typical residential area that will be built and has to be equipped with a cost optimized, decentral energy system with a high degree of energy autarky and integration of renewable sources. We sketch out the optimization problem and show that the optimization can be done using open data and an open source tool, the Helmholtz Framework for Integrated Energy System Assessment tool, FINE, exclusively. The energy system model chooses from a predetermined set of technologies, takes into account a temporal discretization approach for energy demands as well as for energy production capacities and considers decentralized sector coupling options. As a result, we get a cost-optimized energy system structure as a base for energy system design.
Motivated by the increasing share of renewable energy in the markets for energy commodities, this study has evaluated the potential for optimizing production planning by taking into account disposable options for procuring energy, in this case electricity. For this purpose, a material flow simulation study extended by an electricity price simulation has been executed to examine possible cost scenarios. Our findings support the notion of a potential for further research in new optimization models involving energy procurement as well as energy trading options.
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. In this paper, we develop a market screening model to detect inconstancies in price changes. Although there is a long history of industrial organization research of collusion, price setting behavior, and conduct -a robust model to detect structural changes in market structure was missing so far. Our non-parametric approach closes this gap and can be used as a tentative warning system for emerging collusions. Based on the theoretical and empirical results from previous research, we describe requirements of screenings, develop a model, and illustrate our approach with a short market simulation. Finally, we apply the model to the German electricity market. According to our results, between 2001 and 2011 energy suppliers appear to be successful in controlling the market price for several phases.
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