Abstract
Virtual power plants are considered a promising concept for the integration of decentralized energy resources into the future electricity grid. But such a plant must not only optimize load schedules merely according to given economic objectives and technical constraints, if it is to be considered as a green technology. Hence, environmental issues have to be incorporated into optimization objectives, too. Here, we show the possibility of integrating respective performance indicators into search space descriptions in a way that enables direct incorporation into optimization. A meta-model for constrained search spaces based on one-class support vector machines is enriched with information on individual environmental impacts.
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
Preview
Unable to display preview. Download preview PDF.
References
Ben-Hur, A., Siegelmann, H.T., Horn, D., Vapnik, V.: Support vector clustering. Journal of Machine Learning Research 2, 125–137 (2001)
Bremer, J., Rapp, B., Sonnenschein, M.: Support vector based encoding of distributed energy resources feasible load spaces. In: IEEE PES Conference on Innovative Smart Grid Technologies Europe. Chalmers Lindholmen, Gothenburg, Sweden (2010)
Caldon, R., Patria, A., Turri, R.: Optimal control of a distribution system with a virtual power plant. Bulk Power System Dynamics and Control, Cortina. d’Ampezzo, Italy (2004)
Dada, A., Staake, T., Fleisch, E.: Reducing environmental impact in procurement by integrating material parameters in information systems: The example of apple sourcing. AMCIS 2010 Proceedings (2010)
Hrebíček, J., Misarová, P., Hyršlová, J.: Environmental Key Performance Indicators and Cor- porate Reporting. In: Environmental Accounting and Sustainable Development Indicators (EA-SDI 2007), pp. 978–980. Universita Jana Evangelisty Purkyně (2007)
Lukovic, S., Kaitovic, I., Mura, M., Bondi, U.: Virtual power plant as a bridge between distributed energy resources and smart grid. Hawaii International Conference on System Sciences 0, 1–8 (2010). DOI. http://doi.ieeecomputersociety.org/10.1109/HICSS.2010.437
Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller, K.R., Rätsch, G., Smola, A.: Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks 10(5), 1000–1017 (1999)
Stadler, M., Krause, W., Sonnenschein, M., Vogel, U.: Modelling and evaluation of control schemes for enhancing load shift of electricity demand for cooling devices. Environmental Modelling & Software 24(2), 285–295 (2009)
Teuteberg, F., Gómez, J.M.: Green computing & sustainability - Status Quo und Herausforderungen für BUIS dernächsten Generation. Praxis der Wirtschaftsinformatik 47(274), 6–17 (2010)
Thomas, B.: Mini-Blockheizkraftwerke: Grundlagen, Gera¨tetechnik, Betriebsdaten. Vogel Buchverlag (2007)
Acknowledgements
This work was partially funded by the European research project OEPI (Solutions for Managing Organizations Environmental Performance Indicators, 748735).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Bremer, J., Rapp, B., Sonnenschein, M. (2011). Including Environmental Performance Indicators into Kernel based Search Space Representations. In: Golinska, P., Fertsch, M., Marx-Gómez, J. (eds) Information Technologies in Environmental Engineering. Environmental Science and Engineering(), vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19536-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-19536-5_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19535-8
Online ISBN: 978-3-642-19536-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)