European Electricity Grid Infrastructure Expansion in a 2050 Context

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. Terms of use: Documents in May 2013 AbstractThe European climate targets until 2050 require an adaptation of the generation portfolio in terms of renewable and fossil based generation. Assumptions on the timeline of the targets and the availability and costs of generation technologies are used in energy system models to optimize the cost minimal system transformation. The results include investments in generation technologies and their national allocation. Yet, the models are limited to the national aggregation and lack the spatial resolution required to represent individual network investments and related costs.In this paper, we analyze the impact the results of an energy system model have on demand for network expansion in the European power grid in a line-sharp representation. A cost minimizing mixed-integer problem (MIP) model calculates where in the European electricity grid expansion needs to take place for different time steps (2020/30/40/50) in order to obtain minimal total costs for power plant dispatch and grid expansion. Scenarios based on the generation infrastructure options from the PRIMES EU-wide energy model scenarios invoke different expansion needs and are compared. The model allows investments in the AC network and an overlay DC grid. Resulting investment costs are compared to the numbers of the European Energy Roadmap 2050.JEL Codes: C61, H54, L94

Optimal heterogeneity in a simplified highly renewable European electricity system

“…12. For all cases of heterogeneity the associated total European LCOE drops steadily for decreasing solar costs.…”

“…The implications for total system costs of different homogeneous renewable penentrations, wind-solar mixes and transmission levels were considered in [6], where the cost-optimal design was found to consist of a renewable energy penetration of 50% and a wind fraction of 94%. Other relevant research on the advantages of grid extensions for the integration of renew-ables, including reduced variability and smaller forecast errors, can be found in [7][8][9][10][11][12][13]. In this paper the consequences of moving from a homogeneous spatial distribution of VRES and a uniform wind-tosolar mixing factor to a cost-optimal placement of VRES capacities around Europe are explored.…”

Optimising the European transmission system for 77% renewable electricity by 2030

“…The PTDFs are then iteratively updated as transmission capacity is increased in the optimisation. A similar methodology was also used in [22].…”

“…In principle, the optimisation problem should optimise the grid extensions for all hours of several years simultaneously, to capture as many possible load and weather situations as possible, but this is computationally infeasible given current technology. Some research groups have developed methodologies to select typical hours for the optimisation [7, 21, 22]; for this study a different approach was developed.…”

Influence of Renewable Energy Sources on Transmission Networks in Central Europe

“…For the transmission network analysis in this paper we use the most suitable state-of-the-art model ELMOD. Since its first publication in Leuthold et al (2008), this model has been applied most frequently to the analysis of market design (Neuhoff et al (2013); Egerer et al (2016b)), the influence of renewables on transmission networks (Egerer et al (2009); Schroeder et al (2013)) including grid and power plant investment decisions (Leuthold et al (2009); Weigt et al (2010); Dietrich et al (2010); Egerer et al (2016a)), uncertainty and stochastic effects (Abrell & Kunz (2012)) and congestion management issues (Kunz (2013); Kunz & Zerrahn (2015;).…”