Modeling and simulation of local flexibilities and their effect to the entire power system


The transformation of the energy system requires new methods for its modeling and simulation, as established methods are facing problems in representing the increasing complexity and flexibility. We present the modeling and simulation framework MOCES that is based on the modeling language Modelica. The novelty of MOCES is the explicit modeling of the processes that ensure the balancing of production and consumption and which link the physical system with the energy markets. In this contribution, we show how MOCES can be used to model and simulate the German power system with detailed spatial representation by adapting and extending the ELMOD-DE model. The adapted model is modified to investigate the effect of local storage systems that aim at minimizing the exchange of electricity with the upstream grid. We provide findings on their effect to the grid usage, the grid stability, and the economic performance of the storage systems. In addition, we examine the influence of an increasing number of local storage systems on their economic performance.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12


  1. 1.

  2. 2.

  3. 3.

  4. 4.

    In this context, domain addresses different modeling domains, such as the hydraulic, thermal or electric power domain.

  5. 5.

    These actual feed-in values are itself calculated by means of models that use the measurement values of a subset of all plants. Unfortunately, no detailed information about the models are publicly available.

  6. 6.

    The ELMOD-DE adaption has some obvious limitations, such as ignoring must-run restrictions of Combined Heat and Power (CHP) plants caused by local heat demand. In addition, several model parts could not be validated due to missing data, e.g. the grid model. Therefore, further steps are needed to give reliable statements.


  1. 1.

    Andresen L, Dubucq P, Garcia RP, Ackermann G, Kather A, Schmitz G (2015) Status of the TransiEnt library: transient simulation of coupled energy networks with high share of renewable energy. In: Proceedings of the 11th international modelica conference, no 118, September 21–23, 2015, Versailles, France. Linköping University Electronic Press, pp 695–705

  2. 2.

    Berkelaar M, Eikland K, Notebaert P (2004) Open source (mixed-integer) linear programming system.

  3. 3.

    BMWi (2014) Act on the development of renewable energy sources (renewable energy sources act—RES Act 2014).

  4. 4.

    Bundesnetzagentur (2012) Modell zur berechnung des regelzonenübergreifenden einheitlichen bilanz-ausgleichsenergiepreises (reBAP) unter beachtung des beschlusses BK6-12-024 der bundesnetzagentur vom 25.10.2012.

  5. 5.

    Casella F (2015) Simulation of large-scale models in modelica: state of the art and future perspectives. In: Proceedings of the 11th international modelica conference, no 118, September 21–23, 2015, Versailles, France. Linköping University Electronic Press, pp 459–468

  6. 6.

    Chassin DP, Fuller JC, Djilali N (2014) GridLAB-D: an agent-based simulation framework for smart grids. J Appl Math. doi:10.1155/2014/492320

    Google Scholar 

  7. 7.

    Choi J, Tran T, El-Keib AA, Thomas R, Oh H, Billinton R (2005) A method for transmission system expansion planning considering probabilistic reliability criteria. IEEE Trans Power Syst 20(3):1606–1615. doi:10.1109/TPWRS.2005.852142

    Article  Google Scholar 

  8. 8.

    Conzelmann G, Boyd G, Koritarov V, Veselka T (2005) Multi-agent power market simulation using EMCAS. In: Power engineering society general meeting, 2005. IEEE, pp 2829–2834. doi:10.1109/PES.2005.1489271

  9. 9.

    Doms A, Marinzek Z, Pedersen T (2013) MIRABEL—efficiently managing more renewable energy using explicit demand and supply flexibilities. In: Poster session presented at World Smart Grid Forum 2013, Berlin, Germany

  10. 10.

    Dorfner J (2016) Open source modelling and optimisation of energy infrastructure at urban scale. PhD thesis, Technische Universität München

  11. 11.

    Egerer J (2016) Open source electricity model for Germany (ELMOD-DE).

  12. 12.

    entso-e (European Network of Transmission System Operators for Electricity) (2012) The entso-e scheduling system (ESS) implementation guide v4r1.

  13. 13.

    Exel L, Felgner F, Frey G (2015) Multi-domain modeling of distributed energy systems—the MOCES approach. In: 2015 IEEE international conference on smart grid communications (SmartGridComm), pp 774–779. doi:10.1109/SmartGridComm.2015.7436395

  14. 14.

    Fishbone LG, Abilock H (1981) Markal, a linear programming model for energy systems analysis: technical description of the bnl version. Int J Energy Res 5(4):353–375. doi:10.1002/er.4440050406

  15. 15.

    Franke R, Wiesmann H (2014) Flexible modeling of electrical power systems—the modelica powersystems library. In: Proceedings of the 10th international modelica conference, no 096, March 10–12, 2014, Lund, Sweden. Linköping University Electronic Press, pp 515–522

  16. 16.

    Hirth L (2013) The market value of variable renewables: the effect of solar wind power variability on their relative price. Energy Econ 38:218–236. doi:10.1016/j.eneco.2013.02.004

    Article  Google Scholar 

  17. 17.

    Macana CA, Quijano N, Mojica-Nava E (2011) A survey on cyber physical energy systems and their applications on smart grids. In: 2011 IEEE PES conference on innovative smart grid technologies Latin America (ISGT LA), pp 1–7. doi:10.1109/ISGT-LA.2011.6083194

  18. 18.

    Modelica Association (2017) Modelica—a unified object-oriented language for systems modeling.

  19. 19.

    Mueller SC, Georg H, Nutaro JJ, Widl E, Deng Y, Palensky P, Awais MU, Chenine M, Kuch M, Stifter M, Lin H, Shukla SK, Wietfeld C, Rehtanz C, Dufour C, Wang X, Dinavahi V, Faruque MO, Meng W, Liu S, Monti A, Ni M, Davoudi A, Mehrizi-Sani A (2016) Interfacing power system and ict simulators: challenges, state-of-the-art, and case studies. IEEE Trans Smart Grid PP(99):1–1. doi:10.1109/TSG.2016.2542824

    Article  Google Scholar 

  20. 20.

    Palensky P, Widl E, Elsheikh A (2014) Simulating cyber–physical energy systems: challenges, tools and methods. IEEE Trans Syst Man Cybern Syst 44(3):318–326. doi:10.1109/TSMCC.2013.2265739

    Article  Google Scholar 

  21. 21.

    Pfeiffer A (2012) Optimization library for interactive multi-criteria optimization tasks. In: Proceedings of the 9th international modelica conference, September 3–5, 2012, Munich, Germany. Linköping University Electronic Press, pp 669–679

  22. 22.

    Pfenninger S, Hawkes A, Keirstead J (2014) Energy systems modeling for twenty-first century energy challenges. Renew Sustain Energy Rev 33:74–86. doi:10.1016/j.rser.2014.02.003

    Article  Google Scholar 

  23. 23.

    Schütte S (2014) Simulation model composition for the large-scale analysis of smart grid control mechanisms. PhD thesis, Carl von Ossietzky University Oldenburg

  24. 24.

    Sensfuß F, Genoese M (2006) Agent-based simulation for the German electricity markets—an analysis of the German spot market prices in the year 2001. In: Proceedings of the 9 symposium energieinnovationen, Grazm, Austria.

  25. 25.

    Weißbach T (2009) Verbesserung des kraftwerks- und netzregelverhaltens bezüglich handelsseitiger fahrplanänderungen. PhD thesis, Universität Stuttgart. doi:10.18419/opus-1823

  26. 26.

    Xia X, Elaiw A (2010) Optimal dynamic economic dispatch of generation: a review. Electr Power Syst Res 80(8):975–986. doi:10.1016/j.epsr.2009.12.012

    Article  Google Scholar 

  27. 27.

    Zimmer D (2009) Module-preserving compilation of modelica models. In: Proceedings of the 7th international modelica conference, no 043, 20–22 September, 2009, Como, Italy. Linköping University Electronic Press, pp 880–889

Download references


The work was partially supported by the SINTEG project DESIGNETZ funded by the German Federal Ministry of Economic Affairs and Energy (BMWi) under grant 03SIN224. We thank Jonas Egerer from DIW for providing validation data for the grid model of ELMOD-DE.

Author information



Corresponding author

Correspondence to Lukas Exel.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Exel, L., Frey, G. Modeling and simulation of local flexibilities and their effect to the entire power system. Comput Sci Res Dev 33, 49–60 (2018).

Download citation


  • Energy system modeling
  • Flexible energy systems
  • Modelica
  • Energy markets