Bedarf und Verteilung elektrischer Tagesspeicher im zukünftigen deutschen Energiesystem

Mit Hilfe des Energiesystemmodells PERSEUS-NET-ESS wird der gesamtwirtschaftliche Bedarf an elektrischen Tagesspeichern in Deutschland bis 2040 ermittelt. Alternative Technologien, wie Gasturbinen oder Lastverschiebepotentiale, werden ebenso endogen berücksichtigt wie die Restriktionen des deutschen Übertragungsnetzes. Ausgehend von einer für jeden Netzknoten gegebenen Elektrizitätsnachfrage wird die Kraftwerkseinsatz- und -ausbauplanung von thermischen Kraftwerken und Tagesspeichern bestimmt

Reducing computing time of energy system models by a myopic approach

In this paper, the performance of the existing energy system model PERSEUS-NET is improved in terms of computing time. Therefore, the possibility of switching from a perfect foresight to a myopic approach has been implemented. PERSEUS-NET is a linear optimization model generating scenarios of the future German electricity generation system until 2030, whilst considering exogenous regional characteristics such as electricity demand and existing power plants as well as electricity transmission network restrictions. Up to now, the model has been based on a perfect foresight approach, optimizing all variables over the whole time frame in a single run, thus determining the global optimum. However, this approach results in long computing times due to the high complexity of the problem. The new myopic approach splits the optimization intomultiple, individually smaller, optimization problems each representing a 5 year period. The change within the generation system in each period is determined by optimizing the subproblem, whilst taking into account only the restrictions of that particular period. It was found that the optimization over the whole time frame with the myopic approach takes less than one tenth of the computing time of the perfect foresight approach. Therefore, we analyse in this paper the advantages and draw-backs of a change in the foresight as a way of reducing the complexity of energy system models. For PERSEUS-NET it is found that the myopic approach with stable input parameters is as suitable as the perfect foresight approach to generate consistent scenarios, with the advantage of significantly less computing time

Reducing computing time of energy system models by a myopic approach

In this paper, the performance of the existing energy system model PERSEUS-NET is improved in terms of computing time. Therefore, the possibility of switching from a perfect foresight to a myopic approach has been implemented. PERSEUS-NET is a linear optimization model generating scenarios of the future German electricity generation system until 2030, whilst considering exogenous regional characteristics such as electricity demand and existing power plants as well as electricity transmission network restrictions. Up to now, the model has been based on a perfect foresight approach, optimizing all variables over the whole time frame in a single run, thus determining the global optimum. However, this approach results in long computing times due to the high complexity of the problem. The new myopic approach splits the optimization intomultiple, individually smaller, optimization problems each representing a 5 year period. The change within the generation system in each period is determined by optimizing the subproblem, whilst taking into account only the restrictions of that particular period. It was found that the optimization over the whole time frame with the myopic approach takes less than one tenth of the computing time of the perfect foresight approach. Therefore, we analyse in this paper the advantages and draw-backs of a change in the foresight as a way of reducing the complexity of energy system models. For PERSEUS-NET it is found that the myopic approach with stable input parameters is as suitable as the perfect foresight approach to generate consistent scenarios, with the advantage of significantly less computing time

On the road to an electric mobility mass market - How can early adopters be characterized?

Different field trials and corresponding acceptance studies with new technologies have been carried out between 2010 and 2013 at the Chair of Energy Economics at the Karlsruhe Institute of Technology (KIT). Those involved Elec-tric Vehicle (EV) users, Liquefied Petroleum Gas (LPG) and Compressed Natural Gas (CNG) vehicle users as well as persons with strong interest in EV and smart home technologies. In order to characterize early adopters the same item-sets con-cerning attitudes regarding climate change, prices and innovations as well as cor-responding socio-demographic characteristics, were used throughout all these studies and have been joined now and analyzed together. Additionally, regression methods have been applied in order to characterize early EV adopters based on a subsample of EV company car users in the French-German context. A binary logit model explaining private EV purchase intention has been developed. According to this model, early private EV adopters are likely to have a higher level of income, to have a household equipped with two or more cars and to travel more than 50 kilometers a day, not necessarily by car. This model additionally shows that possi-bilities to experience EV (e.g. by test drives) are important leverages to support adoption of EV by private car buyers. Respondents who already decided to pri-vately purchase an EV show significantly lower general price sensitivities than the LPG and CNG vehicle users