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Modelling and statistical model checking of a microgrid

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Abstract

This paper reports on the modelling and analysis of a microgrid with wind, microturbines, and the main grid as generation resources. The microgrid is modelled as a parallel composition of various stochastic hybrid automata. Extensive simulation runs of the behaviour of the main individual microgrid components give insight into the complex dynamics of the system and provide useful information to determine adequate parameter settings. The analysis of the microgrid focuses on determining the probability of linear temporal logic properties expressed in the logic LTL, using the statistical model checker Uppaal-SMC.

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Notes

  1. Frequency deviation stands for a deviation of the actual frequency from the nominal frequency and can be modelled by df \(= K_\mathrm{f} \cdot \varDelta P\) where \(K_\mathrm{f}\) is a multiplicative constant.

  2. \(Q_\mathrm{cool}(t) = Q_{\mathrm{cool},1}(t) + Q_{\mathrm{cool},2}(t)\).

  3. A load profile captures the typical pattern of the load which can be expressed in a form of mean values for given time instances (e.g. daily load pattern).

  4. Stochastic process based on Uhlenbeck–Ornstein model.

References

  1. Abate, A., Katoen, J.-P., Lygeros, J., Prandini, M.: Approximate model checking of stochastic hybrid systems. Eur. J. Contr. 16(6), 1–18 (2010)

    Article  MathSciNet  Google Scholar 

  2. Abate, A., Katoen, J.-P., Mereacre, A.: Quantitative automata model checking of autonomous stochastic hybrid systems. In: Hybrid Systems: Computation and Control (HSCC). ACM, New York, pp. 83–92 (2011)

  3. Anderson, G.: Dynamics and Control of Electric Power Systems. Lecture Notes, ETH Zurich (2012)

  4. Barringer, H., Falcone, Y., Finkbeiner, B., Havelund, K., Lee, I., Pace, G.J., Rosu, G., Sokolsky, O., Tillmann, N. (ed.) Runtime verification—first international conference, RV 2010, St. Julians, Malta, November 1–4, 2010. In: Proceedings. Lecture Notes in Computer Science, vol. 6418. Springer, Berlin (2010)

  5. Carrillo, C., Obando Montao, J.C., Daz-Dorado, E.: Review of power curve modelling for wind turbines. Renew. Sustain. Energy Rev. 21, 572–581 (2013)

  6. Cassandras, C., Lygeros, J.: Stoch. Hybrid Syst. Taylor & Francis, UK (2007)

    Google Scholar 

  7. David, A., Du, D., Larsen, K.G., Legay, A., Mikucionis, M., Poulsen, D., Sedwards, S.: Statistical model checking for stochastic hybrid systems. In: Workshop on Hybrid Systems and Biology (HSB), EPTCS 92, pp. 122–136 (2012)

  8. Gensollen, N., Gauthier, V., Marot, M., Becker, M.: Modeling and Optimizing a Distributed Power Network: A Complex System Approach of the Prosumer Management in the Smart GRID. CoRR abs/1305.4096 (2013)

  9. Gordon, J.M., Ng, K.C.: Predictive and diagnostic aspects of a universal thermodynamic model for chillers. Int. J. Heat Mass Transf. 38(5), 807–818 (1995)

    Article  Google Scholar 

  10. Hahn, E.M., Hartmanns, A., Hermanns, H., Katoen, J.P.: A compositional modelling and analysis framework for stochastic hybrid systems. Formal Methods Syst. Des. 43(2), 191–232 (2013)

    Article  MATH  Google Scholar 

  11. Hartmanns, A., Hermanns, H.: Modelling and Decentralised Runtime Control of Self-stabilising Power Micro Grids. In: ISoLA (1), LNCS, vol. 7609, pp. 420–439 (2012)

  12. Hartmanns, A., Hermanns, H., Berrang, P.: A comparative analysis of decentralized power grid stabilization strategies. In: Winter Simulation Conference (WSC), WSC, pp. 1–13 (2012)

  13. Jégourel, C., Legay, A., Sedwards, S.: Cross-entropy optimisation of importance sampling parameters for statistical model checking. In: International Conference on Computer Aided Verification (CAV), LNCS, vol. 7358, pp. 327–342 (2012)

  14. Kim, Y.J., Kim, M., Kim, T.H.: Statistical model checking for safety critical hybrid systems: an empirical evaluation. In: Haifa Verification Conference (HVC), pp. 162–177 (2012)

  15. Lasseter, R.: Smart distribution: coupled microgrids. Proc. IEEE 99(6), 1074–1082 (2011)

    Article  Google Scholar 

  16. Machowski, J., Bialek, J.W., Bumby, J.R.: Power System Dynamics—Stability and Control. Wiley, New York (2008)

    Google Scholar 

  17. Mathisen, K.W., Morari, M., Skogestad, S.: Dynamic models for heat exchangers and heat exchanger networks. In: European Symposium on Computer Aided Process Engineering3 25th European Symposium of the Working Party on Computer Aided Process Engineering and 494th Event of the European Federation of Chemical Engineering (EFChE). Computers & Chemical Engineering, vol. 18, Supplement 1(0), pp. S459–S463 (1994)

  18. Mur-Amada, J., Bayod-Rujula, A.A.: Wind power variability model—part II—probabilistic power flow. In: 9th Int. Conf. on Electrical Power Quality and Utilisation (EPQU 2007), pp. 1–6 (2007)

  19. Parisio, A., Glielmo, L.: Energy efficient microgrid management using model predictive control. In: 50th IEEE Conf. on Decision and Control and European Control Conference (CDC-ECC 2011), pp. 5449–5454 (2011)

  20. Philippopoulos, K., Deligiorgi, D.: Statistical simulation of wind speed in Athens, Greece, based on Weibull and ARMA models. J. Energy 3, 151–158 (2009)

    Google Scholar 

  21. Ramponi, F., Chatterjee, D., Summers, S., Lygeros, J.: On the connections between PCTL and dynamic programming. In: Hybrid Systems: Computation and Control (HSCC). ACM, New York, pp. 253–262 (2010)

  22. Strelec, M., Macek, K., Abate, A.: Modeling and simulation of a microgrid as a stochastic hybrid system. In: IEEE Conf. on Innovative Smart Grid Technologies (ISGT). IEEE, New York, pp. 1–9 (2012)

  23. Tigges, M.: Modellbasierte Analyse zur Verbesserung der elektrischen Energiebereitstellung zu künftiger Offshore–Windparks mittels Biogastechnologie. Ph.D. thesis, Universitaet Paderborn (2010)

  24. Tzanos, J., Margellos, K., Lygeros, J.: Optimal wind turbine placement via randomized optimization techniques. In: Power Syst. Comput. Conf. (PSCC), pp. 1–8 (2011)

  25. Younes, H., Simmons, R.G.: Statistical probabilistic model checking with a focus on time-bounded properties. Inf. Comput. 204(9), 1368–1409 (2006)

  26. Younes, S., Clarke, E.M., Gordon, G.J., Schneider, J.G.: Verification and Planning for Stochastic Processes with Asynchronous Events. Technical Report (2005)

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Souymodip Chakraborty, Joost-Pieter Katoen & Falak Sher

  2. University of West Bohemia, NTIS, Pilsen, Czech Republic

    Martin Strelec

Authors
  1. Souymodip Chakraborty
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  2. Joost-Pieter Katoen
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  3. Falak Sher
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  4. Martin Strelec
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Corresponding author

Correspondence to Souymodip Chakraborty.

Additional information

This work has been financially supported by the MoVeS (Modelling, verification and control of complex systems: from foundations to power network applications) EU FP7 project SENSATION and the EU FP7 IRSES project MEALS.

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Chakraborty, S., Katoen, JP., Sher, F. et al. Modelling and statistical model checking of a microgrid. Int J Softw Tools Technol Transfer 17, 537–554 (2015). https://doi.org/10.1007/s10009-014-0345-y

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