Skip to main content

Advertisement

Springer Nature Link
Log in

The robustness of inventory levels and power support in business and energy grid administration

Robustheit des Lagermanagements und der Energieversorgung

  • Originalarbeiten
  • Published:
e & i Elektrotechnik und Informationstechnik Aims and scope Submit manuscript

Abstract

This paper is addressed to problems arising in the dynamics of business administration and power systems which are characterized by dead-time delay and which make the problems more difficult, especially if power generation is highly variable. Overshoot and undershoot reactions should be avoided as far as possible. The classical problem is outlined and the methods of predictors and model predictive control are carried out. Special interest is put on accessing the robustness with sensitivity analysis methods, on keeping an eye on maintaining optimal transients including the application of selected gradients.

Zusammenfassung

Der Aufsatz behandelt Probleme des Bestandsmanagements im Bereich der Geschäftsabwicklung und bei Energiesystemen. Durch die totzeitbedingten Verzögerungen werden die Aufgaben komplexer, insbesondere bei volatiler Energieumsetzung. Über- und Unterschwingen sollten soweit wie möglich unterbunden werden. Die klassischen Probleme und die Methoden der Vorhersage werden abgehandelt, ebenso die Regelung mit modellbasierter Vorhersage. Besondere Aufmerksamkeit wird auf die Robustheits- und Sensitivitätsmethoden gelegt, um mit ausgewählten Gradienten optimale Transienten zu erhalten.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Adamy, J. (2009): Nichtlineare Regelungen. Berlin: Springer.

    Book  Google Scholar 

  2. Alessandri, A., Gaggero, M., Tonelli, F. (2011): Min-max and predictive control for the management of distribution in supply chains. IEEE Trans. Control Syst. Technol., 19, 1075–1089.

    Article  Google Scholar 

  3. Appleby, R. C. (1994): Modern business administration. Financal Times Management.

  4. Balance Sheet sample of a typical trading company: Wal-Mart in Jan 31, 2006: Source. http://www.edgar-online.com.

  5. Bordeneuve-Guibé, J., Vaucoret, C. (2001): Robust multivariable predictive control. IEEE Control Syst. Mag., 21(2), 54–65.

    Article  Google Scholar 

  6. Brauner, G. (2011): Nachhaltige Energieversorgung - Herausforderungen und Lösungsmöglichkeiten. E&I, Elektrotech. Inf.tech., 128, 302–303.

    Article  Google Scholar 

  7. Camacho, E. F., Bordons, C. (1999): Model predictive control. London: Springer. (2007)

    Book  Google Scholar 

  8. Cao, C. T. (1989): Entwurf eines robusten prädiktiven Reglers für totzeitbehaftete Regelsysteme. Automatisierungstechnik, 37, 104–110.

    MATH  Google Scholar 

  9. Carlberg, C. (2007): Business analysis with Microsoft excel. 3rd ed. Indianapolis: Que (Pearson).

    Google Scholar 

  10. Clarke, D. W., Mohtadi, C., Tuffs, P. S. (1987): Generalized predictive control. Automatica, 23, 137–160.

    Article  MATH  Google Scholar 

  11. Demircioglu, H., Karasu, E. (2000): Generalized predictive control. IEEE Control Syst. Mag., 20(5), 36–47.

    Article  Google Scholar 

  12. Gehrer, W. (2011): Powervision 2040. E&I, Elektrotech. Inf.tech., 128, 306–308.

    Article  Google Scholar 

  13. Halvgaard, R., Poulsen, N. K., Madsen, H., Jorgensen, J. B. (2012): Economic model predictive control for building climate control in a smart grid. In IEEE PES innovative smart grid technologies (pp. 1–6).

    Google Scholar 

  14. Halvgaard, R., Poulsen, N. K., Madsen, H., Jorgensen, J. B., Marra, F., Bondy, D. E. M. (2012): Electric vehicle charge planning using economic model predictive control. In Electric vehicle conference (pp. 1–6).

    Google Scholar 

  15. Hanau, A. (1928): Die Prognose der Schweinepreise. Berlin: Reimar Hobbing.

    Google Scholar 

  16. Harlow, A. A. (1960): The hog cycle and the cobweb theorem. J. Farm Econ., Nov., 842–853.

    Google Scholar 

  17. Hug-Glanzmann, G. (2011): A hybrid approach to balance the variability and intermittency of renewable generation. In IEEE power tech Trondheim (pp. 1–8).

    Google Scholar 

  18. Kaldor, N. (1938): The cobweb theorem. Q. J. Econ., 52, 255–280.

    Article  Google Scholar 

  19. Kotler, P., et al. (2009): Marketing management, international edn. NewYork: Prentice Hall International.

    Google Scholar 

  20. Le, X., Ilic, M.D. (2009): Model predictive economic/environmental dispatch of power systems with intermittent resources. In IEEE power and energy society general meeting (pp. 1–6).

    Google Scholar 

  21. Le, X., Joo, J.-Y., Ilic, M.D. (2009): Integration of intermittent resources with price-responsive loads. In North American power symposium (pp. 1–6).

    Chapter  Google Scholar 

  22. Maciejowski, J. M. (2002): Predictive control with constraints. Upper Saddle River: Pearson.

    Google Scholar 

  23. Mattingley, J., Wang, Y., Boyd, S. (2011): Receding horizon control, automatic generation of high-speed solvers. IEEE Control Syst. Mag., 31(3), 52–65.

    Article  MathSciNet  Google Scholar 

  24. Mayhorn, E., Kalsi, K., Elizondo, M., Zhang, W., Lu, S., Samaan, N., Butler-Purry, K. (2012): Optimal control of distributed energy resources using model predictive control. In Power and energy society general meeting (pp. 1–8).

    Google Scholar 

  25. Monden, Y. (1998): Toyota production system: an integrated approach to just-in-time. 3rd edn. Edited by Inst. of Industrial Engineers.

    Google Scholar 

  26. Muller, M. (2003): Essentials of inventory management. New York: McGraw-Hill Professional.

    Google Scholar 

  27. Nandola, N. N., Rivera, D. E. (2013): An improved formulation of hybrid model predictive control with application to production-inventory systems. IEEE Trans. Control Syst. Technol., 21, 121–135.

    Article  Google Scholar 

  28. Özer, O. (2003): Replenishment strategies for distribution systems under advance demand information. Manag. Sci., 49(3), 255–272.

    Article  MATH  Google Scholar 

  29. Palmor, Z. J., Halevi, Y. (1990): Robustness properties of sampled-data systems with dead time compensators. Automatica, 26, 637–640.

    Article  MathSciNet  MATH  Google Scholar 

  30. Rawlings, J. B. (2000): Tutorial overview of model predictive control. IEEE Control Syst. Mag., 20(3), 38–52.

    Article  MathSciNet  Google Scholar 

  31. Smith, O. J. M. (1957): Closer control of loops with dead time. Chem. Eng. Prog., 53, 217–219.

    Google Scholar 

  32. Wang, L. (2009): Model predictive control system design and implementation in MATLAB. London: Springer.

    Google Scholar 

  33. Wang, T., Toktay, B. (2008): Inventory management with advance demand information and flexible delivery. Manag. Sci., 54(4), 716–732.

    Article  MATH  Google Scholar 

  34. Wei, Q., Liu, J., Christofides, P. D. (2013): Distributed supervisory predictive control of distributed wind and solar energy systems. IEEE Trans. Control Syst. Technol., 21, 504–512.

    Article  Google Scholar 

  35. Weinmann, A. (2003): Multi-Lagrange-multiplier problems: desensitized pole assignment with minimal controller matrix norm for networked control systems and constraints in high-order mechatronics. Math. Comput. Model. Dyn. Syst., 9, 345–362. Swets & Zeitlinger Publishers.

    Article  MATH  Google Scholar 

  36. Weinmann, A. (2010): Robustification of control systems under uncertainty proportion. E&I, Elektrotech. Inf.tech., 127, 269–273.

    Article  Google Scholar 

  37. www.economia48.com. Die große Enzyklopädie der Wirtschaft.

  38. www.economics.phil.uni-erlangen.de. Optimale Lagerwirtschaft.

  39. Wirtschaftslexikon.gabler.at. wikipedia.at ‘Lagerbestand’.

Download references

Author information

Authors and Affiliations

  1. Institute of Automatic Control, Vienna University of Technology, Gusshausstraße 27-29, 1040, Vienna, Austria

    Alexander Weinmann

  2. Vienna University of Technology, Vienna, Austria

    Gerhard Vöhr

  3. University of Applied Sciences Technikum Wien, Vienna, Austria

    Gerhard Vöhr

Authors
  1. Alexander Weinmann
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Gerhard Vöhr
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Alexander Weinmann.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Weinmann, A., Vöhr, G. The robustness of inventory levels and power support in business and energy grid administration. Elektrotech. Inftech. 130, 230–237 (2013). https://doi.org/10.1007/s00502-013-0159-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00502-013-0159-8

Keywords

Schlüsselwörter