Abstract
High voltage transmission networks play a crucial role in the ongoing transformation from centralized power generation in conventional power plants to decentralized generation from renewable energy sources (RES). The rapid expansion of RES requires a structural rearrangement of the entire power system to ensure the current level of supply security. Scientific approaches to the characterization and improvement of power transmission networks, however, often lack the availability of reliable and appropriate data on the networks’ structure. Using SciGRID, which was recently released open source, we generate a topological grid model for Germany using open data provided by OpenStreetMap. Starting from this particular grid model we characterize the structure of the German transmission grid by means of graph-theoretical decomposition approaches to complexity reduction. Our procedure aims to identify key features and characteristics complementing the grid’s electrotechnical properties; it is for example used to characterize the SciGRID approach and validate the resulting models against other (potentially not open source) transmission network models. In addition, it paves the way for networks with reduced complexity, which might be beneficial in optimization problems addressing system design and operation.
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References
Ahlhaus P, Stursberg P (2013) Transmission capacity expansion: An improved transport model. In: ISGT Europe, IEEE, pp 1–5, URL http://dblp.uni-trier.de/db/conf/isgteurope/isgteurope2013.html#AhlhausS13
Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Reviews of modern physics 74(1):47
Amaral LAN, Scala A, Barthelemy M, Stanley HE (2000) Classes of small-world networks. Proceedings of the national academy of sciences 97(21):11,149–11,152
Barabási AL, Albert R (1999) Emergence of scaling in random networks. science 286(5439):509–512
Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: Structure and dynamics. Physics reports 424(4):175–308
Brockmann D, David V, Gallardo AM (2009) Human mobility and spatial disease dynamics. Reviews of nonlinear dynamics and complexity 2:1–24
Butts CT (2001) The complexity of social networks: theoretical and empirical findings. Social Networks 23(1):31–72
Codes EEN (2015) Network code on operational security. http://networkcodes.entsoe.eu/category/requirements-within-the-code/?p=operational-security
ENTSO-E (2015) Entso-e continental europe operation handbook. https://www.entsoe.eu/publications/system-operations-reports/operation-handbook/Pages/default.aspx
Gonzalez MC, Hidalgo CA, Barabasi AL (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782
Hazy JK (2012) Leading large: emergent learning and adaptation in complex social networks. International Journal of Complexity in Leadership and Management 14 2(1–2):52–73
Johnson M, Paulusma D, van Leeuwen EJ (2015) Algorithms for diversity and clustering in social networks through dot product graphs. Social Networks 41:48–55
Kramer L (2013) Modeling price formation in a multi-commodity market - a graph-theoretical decomposition approach to complexity reduction. PhD thesis, University of Heidelberg, URL http://www.ub.uni-heidelberg.de/archiv/16013
Medjroubi W, Matke C, Kleinhans D (2015) SciGRID - An Open Source Reference Model for the European Transmission Network (v0.1). http://www.scigrid.de
Neis P, Zielstra D, Zipf A (2013) Comparison of volunteered geographic information data contributions and community development for selected world regions. Future Internet 5(2):282, DOI 10.3390/fi5020282, URL http://www.mdpi.com/1999-5903/5/2/282
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026,113, DOI 10.1103/PhysRevE.69.026113, URL http://link.aps.org/doi/10.1103/PhysRevE.69.026113
Sivanagaraju S (2008) Electric power transmission and distribution. Pearson Education India
Watts D, Strogatz S (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440–442
Acknowledgements
Funding for SciGRID by the German Federal Ministry of Education and Research (BMBF) through the funding initiative “Zukunftsfähige Stromnetze” (funding code 03SF0471) is acknowledged by C. Matke, W. Medjroubi, and D. Kleinhans. Sebastian Sager gratefully acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 647573) and from the German BMBF under grant 05M2013—GOSSIP.
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Matke, C., Medjroubi, W., Kleinhans, D., Sager, S. (2017). Structure Analysis of the German Transmission Network Using the Open Source Model SciGRID. In: Bertsch, V., Fichtner, W., Heuveline, V., Leibfried, T. (eds) Advances in Energy System Optimization. Trends in Mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-51795-7_11
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DOI: https://doi.org/10.1007/978-3-319-51795-7_11
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