Abstract
A plethora of energy management opportunities has emerged for electricity consumers and producers by way of the transition from the current grid infrastructure to a smart grid. The aim of this chapter is to present a new dynamic data-driven applications systems (DDDAS) methodology for partitioning the smart distribution grid based on dynamically varying data. In particular, the proposed methodology uses the k-means algorithm for performing partitioning and a fuzzy decision making method for increasing power efficiency and reliability. The network is divided into a set of “similar” subnetworks; where the subnetworks are comprised of residential customers (i.e., residencies) who share the same characteristics pertaining to the energy needs but not necessarily the same geographic vicinity or belong to the same grid node. A fuzzy logic method is used to make decisions on which partitions could be offered energy at lower prices available from Renewable Energy Sources (RES). Various scenarios based on the GridLAB-D simulation platform exhibits how the operation of the smart grid is affected from the partition of the distribution grid. The illustrative example utilizes the IEEE-13, IEEE-37 and IEEE-123 bus test feeders in the experiments from a distribution grid composing 3004 residencies and both conventional and distributed generators.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
J. Aghaei, M. Alizadeh, Demand response in smart electricity grids equipped with renewable energy sources: A review. Renew. Sust. Energ. Rev. 18, 64–72 (2015)
F. Darema, Dynamic data driven applications systems: A new paradigm for application simulations and a new paradigm for measurement systems, NSF Workshop, 2000
D.E. Moore, N. Celik, DDDAS-based communication in distributed Smartgrid networks, in Proceedings of the Annual Industrial and Systems Engineering Research Conference 2014, Montreal, 2014
X. Shi, H. Damgacioglu, N.A. Celik, Dynamic data-driven approach for operation planning of microgrids. Int. Conf. Comput. Sci. 51, 2543–2552 (2015)
A. Thanos, M. Bastani, N. Celik, C.H. Chen, Dynamic data driven adaptive simulation framework for automated control in microgrids. IEEE Trans. Smart Grid 51, 2503–2517 (2015)
N. Celik, A.E. Thanos, J.P. Saenz, DDDAMS-based dispatch control in power networks, in 13th Annual International Conference on Computational Science (2013) pp. 1899–1908
A.E. Thanos, X. Shi, J.P. Saenz, N.A. Celik, DDDAMS framework for real-time load dispatching in power networks, in Proceedings of the 2013 Winter Simulation Conference – Simulation: Making Decisions in a Complex World (2013) pp. 1893–1904
C. Park, J. Tang, Y. Ding, Aggressive data reduction for damage detection in structural health monitoring. Struct. Health Monit. 9, 59–74 (2010)
A.M. Khaleghi, D. Xu, Z. Wang, M. Li, A. Lobos, J. Liu, Y.A. Son, A DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs. Expert Syst. Appl. 40, 7168–7183 (2013)
E. Blasch, Y. Al-Nashif, S. Hariri, Static versus dynamic data information fusion analysis using DDDAS for cyber security trust. Procedia Comput. Sci. 29, 1299–1313 (2014)
E. Blasch, G. Seetharaman, F. Darema, Dynamic Data Driven Applications Systems (DDDAS) modeling for automatic target recognition, in Proceedings of SPIE 8744, Automatic Target Recognition XXIII, 8744 (2013)
F. Darema, Dynamic data driven applications systems: New capabilities for application simulations and measurements. Lect. Notes Comput. Sci 3515, 610–615 (2005)
R. Fainti, A. Nasiakou, E. Tsoukalas, M. Vavalis, Design and early simulations of next generation intelligent energy systems. Int. J. Monit. Surveill. Technol. Res. 2, 58–82 (2014)
GridLAB-D Market Module Documentation, http://gridlab-d.sourceforge.net/wiki/index.php/Spec:Market
L.H. Tsoukalas, R.E. Uhrig, Fuzzy and neural approaches in engineering (Wiley, New York, 1997)
J. Mendel, Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83, 345–377 (1995)
M. Alamaniotis, V. Agarwal, V. Jevremovic, Anticipatory monitoring and control of complex energy systems using a fuzzy based fusion of support vector regressors, in 5th International Conference on Information, Intelligence, Systems and Applications (2014) pp. 33–37
A. Nasiakou, M. Alamaniotis, L.H. Tsoukalas, Extending the k-meansclustering algorithm to improve the compactness of the clusters. J. Pattern Recognit. Res. 11(1), 61–73 (2016)
K. Zhou, S. Yang, C. Shen, A review of electric load classification in smart grid environment. Renew. Sust. Energ. Rev. 24, 103–110 (2013)
A. Singh, A. Yadav, A. Rana, K-means with three different distance metrics. Int. J. Comput. Appl. 67(10), 13–17 (2013)
A. Nasiakou, M. Vavalis, D. Bargiotas, Simulating active and reactive energy markets, in 6th International Conference on Information, Intelligence, Systems and Applications (2015) pp. 1–6
W.H. Kersting, Radial distribution feeders. Trans. Power Syst 6, 975–985 (1991)
J.C. Fuller, K. Schneider, P.D. Chassin, Analysis of residential demand response and double-auction markets, in IEEE Power and Energy Society General Meeting (2011)
Acknowledgements
This work is supported by the US National Science Foundation (NSF) in collaboration with the Air Force Office of Scientific Research (AFOSR) under the grant no 1462393.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nasiakou, A., Alamaniotis, M., Tsoukalas, L.H., Vavalis, M. (2018). Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-95504-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95503-2
Online ISBN: 978-3-319-95504-9
eBook Packages: Computer ScienceComputer Science (R0)