Abstract
Current energy reports confirm the steadily dilating gap between available conventional energy resources and future energy demand. This gap results in increasing energy costs and has become a determining factor in economies. Hence, politics, industry, and research focus either on regenerative energy resources or on energy-efficient concepts, methods, and technologies for energy-consuming devices. A remaining challenge is energy optimization of complex systems during their operation time. In addition to optimization measures that can be applied in development and engineering, the generation of optimization measures that are customized to the specific dynamic operational situation, promise high-cost saving potentials. During operation time, the systems are located in unique situations and environments and are operated according to individual requirements of their users. Hence, in addition to complexity of the systems, individuality and dynamic variability of their surroundings during operation time complicate identification of goal-oriented optimization measures. This contribution introduces a model-based approach for user-centric energy cost analysis of industrial automation systems. The approach allows automated generation and appliance of individual optimization proposals. Focus of this paper is on a basic variant for a single industrial automation system and its operational parameters.
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Bandidni, S., & Sartori, F. (2005). Improving the effectiveness of monitoring and control systems exploiting knowledge-based approaches. Personal and Ubiquitous Computing, 9(5), 301–311.
Barroso, L. A., & Hölzle, U. (2007). The case for energy-proportional computing. IEEE Transactions on Computers, 40(12), 33–37.
Beck, A., Göhner, P. (2010). Generation of optimization proposals for electrical energy analysis of industrial automation systems. IEEE International Energy Conference and Exhibition.
Beck, A., Jazdi, N. (2010). Model-based electrical energy analysis of industrial automation systems. IEEE-TTTC International conference on Automation, Quality, and Testing, Robotics.
Beer, S., Rütinger, H., Bischofs, L., & Appelrath, H.-J. (2010). Towards a reference architecture for regional electricity markets. Information Technology (IT), 52(2), 58–64. Munich: Oldenbourg Industrie Verlag.
Benini, L., & de Micheli, G. (2000). System-level power optimization: Techniques and tools. ACM Transactions on Design Automation of Electronic Systems, 5(2), 115–192.
Booy, D., Liu, K., Qiao, B., Guy, C. (2008). A semiotic multi-agent system for intelligent building control. International Conference on Ambient Media and Systems.
BP p.l.c. (2011). BP statistical review of world energy. http://www.bp.com/assets/bp_internet/globalbp/globalbp_uk_english/reports_and_publications/statistical_energy_review_2011/STAGING/local_assets/pdf/statistical_review_of_world_energy_full_report_2011.pdf. Accessed 29 July 2011.
British Standards Institution—BSI. (1997). Electrically propelled road vehicles—Measurement of energy performances—Part 1: Pure electric vehicles. European Standard. BS EN 1986-1:1997, November 1997.
Chaoui, M., Ghariani, H., Lahiani, M., Perdriau, R., Ramdani, M., Sellami, F. (2005). Electrical modeling of inductive links for high-efficiency energy transmission. IEEE International Conference on Electronics, Circuits and Systems.
Cho, Y., Chang, N., Chakrabarti, C., Vrudhula, S. (2006). High-level power management of embedded systems with application-specific energy cost functions. ACM/IEEE Design Automation Conference.
Contreras, J., Losi, A., Russo, M., & Wu, F. F. (2001). Simulation and evaluation of optimization problem solutions in distributed energy management systems. IEEE Transactions on Power Systems, 17(1), 57–62.
Crawley, D. B., et al. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Elsevier Energy & Buildings, 33(4), 443–457.
Felgner, F., Augustina, S., Cladera Bohigas, R., Merz, R., Litz, L. (2002). Simulation of thermal building behaviour in Modelica. International Modelica Conference.
Ge, M., Xu, Y., & Du, R. (2008). An intelligent online monitoring and diagnostic system for manufacturing automation. IEEE Transactions on Automation Science and Engineering, 5(1), 127–139.
Gibson, G. (2001). Intelligent software agents for control and scheduling of distributed generation. California Energy Commission.
Goehner, P. (2011). What is industrial automation? Lecture notes in industrial automation. http://www.ias.uni-stuttgart.de/ia/unterlagen/umdruck/chapter01.pdf. Accessed 29 July 2011.
Hu, C., Jiménez, D.A., Kremer, U. (2005). Toward an evaluation infrastructure for power and energy optimizations. IEEE International Parallel and Distributed Processing Symposium.
IAEA. (2008). Energy indicators for sustainable development: Guidelines and methodologies. Vienna: IAEA.
IEA. (2009). World energy outlook 2009. Paris: OECD/IEA.
Izumi, T., Zhou, H., & Li, Z. (2009). Optimal design of gear ratios and offset for energy conservation of an articulated manipulator. IEEE Transactions on Automation Science and Engineering, 6(3), 551–557.
Khan, M.R., Khan, M.F. (2009). Energy cost calculations for a solar PV Home System. International Conference on the Developments in Renewable Energy Technology.
Lapillone, B., Bosseboef, D., & Thomas, S. (2008). Top-down evaluation methods of energy savings. Draft summary report for consultation. Wuppertal: Wuppertal Institut.
Larouci, C., Boukhnifer, M., & Chaibet, A. (2010). Design of power converters by optimization under multiphysic constraints: application to a two-time-scale AC/DC-DC converter. IEEE Transactions on Industrial Electronics, 57(11), 3746–3753.
Maturana, F. P., et al. (2006). Energy management system. European Patent Application, EP 1 635 286 A1, March 2006.
New Energy and Industrial Technology Development Organization—NEDO. (2010). Energy efficiency technology knowledge base. http://62.160.8.20/eetkb/. Accessed 29 July 2011.
Secchi, C., Bonfe, M., & Fantuzzi, C. (2007). On the use of UML for modeling mechatronic systems. IEEE Transactions on Automation Science and Engineering, 4(1), 105–113.
Skogestad, S. (2000). Plantwide control: The search for the self-optimizing control structure. Elsevier Journal of Process Control, 10, 487–507.
Son, Y.-S., Pulkkinen, T., Moon, K.-D., & Kim, C. (2010). Home energy management system based on power line communication. IEEE Transactions on Consumer Electronics, 56(3), 1380–1386.
Väisänen, H., et al. (2010). Audit II. Guidebook for energy audit programme developers. SAVE-project Audit II. http://www.motiva.fi/files/1805/GB_Printversion.pdf. Accessed 29 July 2011.
van Gorp, J.C. (2005). Using key performance indicators to manage energy costs. Industrial Energy Technology Conference.
Velten, K. (2009). Mathematical modeling and simulation. Weinheim: WILEY-VCH.
Verein deutscher Ingenieure—VDI. (2000). Simulation of systems in materials handling, logistics and production—Fundamentals. VDI guideline, VDI 3633, March 2000. Berlin: Beuth.
Verein deutscher Ingenieure—VDI. (2003). Energetic characteristics. Definitions–terms–methodology. VDI guideline, VDI 4661, September 2003. Berlin: Beuth.
Vieritz, H., Yazdi, F., Jazdi, N., Schilberg, D., Jeschke, S., Goehner, P. (2011). Discussions on accessibility in industrial automation systems. IEEE International Symposium on Applied Machine Intelligence and Informatics.
Vojdani, A. (2008). Smart integration. IEEE Power and Energy Magazine, 6(6), 71–79.
Zhou, J., Cooper, K., Ma, H., & Yen, L. (2007). On the customization of components: A rule-based approach. IEEE Transactions on Knowledge and Data Engineering, 19(9), 1252–1275.
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Beck, A., Göhner, P. An approach for model-based energy cost analysis of industrial automation systems. Energy Efficiency 5, 303–319 (2012). https://doi.org/10.1007/s12053-012-9145-y
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DOI: https://doi.org/10.1007/s12053-012-9145-y