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. Abstract Electricity market models, implemented as dynamic programming problems, have been applied widely to identify possible pathways towards a cost-optimal and low carbon electricity system. However, the joint optimization of generation and transmission remains challenging, mainly due to the fact that different characteristics and rules apply to commercial and physical exchanges of electricity in meshed networks. This paper presents a methodology that allows to optimize power generation and transmission infrastructures jointly through an iterative approach based on power transfer distribution factors (PTDFs). As PTDFs are linear representations of the physical load flow equations, they can be implemented in a linear programming environment suitable for large scale problems. The algorithm iteratively updates PTDFs when grid infrastructures are modified due to cost-optimal extension and thus yields an optimal solution with a consistent representation of physical load flows. The method is first demonstrated on a simplified three-node model where it is found to be robust and convergent. It is then applied to the European power system in order to find its cost-optimal development under the prescription of strongly decreasing CO 2 emissions until 2050. Terms of use: Documents in

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. Terms of use: Documents in AbstractAs an attempt to fight global warming, many countries try to reduce CO 2 emissions in the power sector by significantly increasing the proportion of renewable energies (RES-E). A highly intermeshed electricity transmission grid allows the achievement of this target cost-efficiently by enabling the usage of most favorable RES-E sites and by facilitating the integration of fluctuating RES-E infeed and regional electricity demands.However, construction of new lines is often proceeding very slowly in areas with a high population density.In this paper, we try to quantify the benefits of optimal transmission grid extensions for Europe until 2050 compared to moderate extensions when ambitious RES-E and CO 2 reduction targets are achieved. We iterate a large-scale dynamic investment and dispatch optimization model for Europe with a load-flow based transmission grid model, in order to determine the optimal deployment of electricity generation technologies and transmission grid extensions from a system integrated point of view. Main findings of our analysis include that large transmission grid extensions are needed to achieve the European targets cost-efficiently.When the electricity network is cost-optimally extended, 228,000 km are built until 2050, representing an increase of 76% compared to today. Further findings include substantial increases of average system costs for electricity until 2050, even if RES-E are deployed efficiently throughout Europe, the grid is extended optimally, and if significant cost reductions of RES-E are assumed.

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For the past couple of years, Plug-in Hybrid Electric Vehicles (PHEVs) demonstrated their ability to significantly reduce petroleum consumptions. However, more than any other vehicle powertrain, their benefits are dependent on the driving cycles from both an aggressiveness and distance point of view. In this paper, two powertrain configurations will be defined. A power split configuration will be used for low battery energy and a series configuration for high battery energy. For each vehicle we will evaluate several control strategies, including electrical dominant and blended, on real world drive cycles. A conventional vehicle will be defined to use as a baseline. The trade-off between fuel displacement and cost will be evaluated for each option.

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. Terms of use: Documents in AbstractWind power has seen strong growth over the last decade and increasingly affects electricity spot prices. Generation from wind energy is stochastic, and if there is lot of wind, prices tend to be lower. Therefore, for an investor, but also for the whole electricity system, it is important to assess the value of wind power at different locations. In this paper, we develop a stochastic simulation model that captures the full spatial dependence structure of wind power by using copulas, incorporated into a structural supply and demand based model for the electricity spot price. This model is calibrated with German data. We find that the specific location of a turbine -i.e., its spatial dependence with respect to the aggregated wind power in the system -is of high relevance for its value. Many of the locations analyzed show an upper tail dependence that adversely impacts the market value. Therefore, a model that assumes a linear dependence structure would systematically overestimate the market value of wind power in many cases. This effect becomes more important for increasing levels of wind power penetration and may render the large-scale integration into markets more difficult.

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Based upon probabilistic reliability metrics, we develop an optimization model to determine the efficient amount and location of firm generation capacity to achieve reliability targets in multi-regional electricity systems. A particular focus lies on the representation and contribution of transmission capacities as well as variable renewable resources. Calibrating our model with a comprehensive dataset for Europe, we find that there are substantial benefits from regional cooperation. The amount of firm generation capacity to meet a perfectly reliably system could be reduced by 36.2 GW (i.e., 6.4 %) compared to an isolated regional approach, which translates to savings of 14.5 bn Euro. Interconnectors contribute in both directions, with capacity values up to their technical maximum of close to 200 %, while wind power contributions are in the range of 3.8-29.5 %. Furthermore, we find that specific reliability targets heavily impact the efficient amount and distribution of reliable capacity as well as the contribution of individual technologies.