Architecture of Computing Systems (Lecture Notes in Computer ...

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160 B. Becker et al. Power [W] 4500 4000 3500 3000 2500 2000 1500 1000 500 0 4500 4000 3500 3000 2500 2000 1500 1000 500 0 breadmaker deep freezer toaster stove dishwasher toaster 00:00 00:50 01:40 02:30 03:20 04:10 05:00 05:50 06:40 07:30 08:20 09:10 10:00 10:50 11:40 12:30 13:20 14:10 15:00 15:50 16:40 17:30 18:20 19:10 20:00 20:50 21:40 22:30 23:20 charging behavior request behavior request feed-back stove washing machine dishwasher 00:00 00:50 01:40 02:30 03:20 04:10 05:00 05:50 06:40 07:30 08:20 09:10 10:00 10:50 11:40 12:30 13:20 14:10 15:00 15:50 16:40 17:30 18:20 19:10 20:00 20:50 21:40 22:30 23:20 feed-back Fig. 5. Re-scheduling based on the behavior request be re-scheduled. In this case, the power demand of the stove can partially be balanced 2 by feeding-back from the mobile energy storage. This potential is highlighted by the shaded area in the lower diagram. The car’s battery has been discharged during the feed-back process. The charging process is scheduled to the early morning by the SHMD. This is a period of low demand. Thus, the demand of the house can, to a certain degree, comply with the given behavior request, and, accordingly, to the predicted load of the local grid. 7 Conclusion In this paper, a hierarchically structured observer/controller architecture is applied to control a smart-home, to increase the energy efficiency, and to avoid overloads of the low voltage grid. The integration of electric vehicles raises the potential of re-scheduling the energy demand of a smart-home. Thisisachieved 2 The power demand of the stove is higher than the max. feed-back power of the car’s battery.
Decentralized Energy-Management to Control Smart-Home Architectures 161 by controlling the charging process and feeding-back energy into the grid during times of high demand. The experimental results have shown the potential of automatic scheduling the smart-home’s appliances to comply with the external signals given from the grid operator. In this way, the challenge to balance demand and generation can be tackled by each smart-home. In future work, the currently applied static rules for determining the demand of control can be improved by extended learning algorithms to achieve a higher degree of autonomy and to increase the robustness of the management system. Consequently, less user interaction would be required, improving the systems’ usability and comfort. To get the capability of analyzing a more representative user behavior in a smart-home, it is planned to extend the environment in a long-running experiment in a real house with a significant number of intelligent appliances and distributed energy generators. References 1. Schiffer, H.W., Czakainski, M., Gerling, P., Hareiner, H.R., zu Schwabedissen, C.M., Rath, H., Schmid, C., Schupan, J., Seeligmüller, I., Wiemann, M.: Energie für Deutschland. Deutsches nationales Komitee des Weltenergierates, DNK (2006) 2. Schmeck, H.: Organic computing: A new vision for distributed embedded systems. In: Proceedings Eighth IEEE International Symposium on Object-Oriented Real- Time Distributed Computing (ISORC 2005), pp. 201–203. IEEE Computer Society, Los Alamitos (2005) 3. UCTE: Operation Handbook. European network of transmission system operators for electricity (2004) 4. Reiner, U., Leibfried, T., Allerding, F., Schmeck, H.: Potential of electrical vehicles with feed-back capabilities and controllable loads in electrical grids under the use of decentralized energy management systems. In: ETG-Kongress (2009) 5. Kamper, A., Eßer, A.: Strategies for decentralized balancing power. In: Biologicallyinspired optimization methods. Studies in Computational Intelligence, vol. 210, pp. 261–289. Springer, Heidelberg (2009) 6. Müller-Schloer, C.: Organic computing: On the feasibility of controlled emergence. In: Proceedings of the 2nd IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES + ISSS 2004), pp. 2–5. ACM Press, New York (2004) 7. Richter, U., Mnif, M., Branke, J., Müller-Schloer, C., Schmeck, H.: Towards a generic observer/controller architecture for organic computing. In: INFORMATIK 2006 – Informatik für Menschen! LNI, vol. P-93, pp. 112–119. Bonner Köllen Verlag (2006) 8. Nestle, D., Ringelstein, J.: Application of bidirectional energy management interfaces for distribution grid services. In: 20th Int. Conf. on Electricity Distribution, CIRED (2009)
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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4 J. Mische et al. The Real-time Vi
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76 M. Bonn and H. Schmeck 2.2 Node
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90 J.-P. Steghöfer et al. mechanis
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92 J.-P. Steghöfer et al. resource
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96 J.-P. Steghöfer et al. 1. Defin
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102 K. Kloch et al. large-scale sys
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104 K. Kloch et al. a�t� 1.0 0.
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106 K. Kloch et al. constant. This
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108 K. Kloch et al. Relative number
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Abdullah, Tariq 174 Alima, Luc Onan
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