Advertisement

Globally Optimal Short-Term Unit Commitment and Dispatch for Combined Heat and Power Generation Units Connected to District Heating Grids

  • Lennart MerkertEmail author
  • Sören Hohmann
Conference paper
Part of the Operations Research Proceedings book series (ORP)

Abstract

With the raising share of renewable power generation, economic operation of combined heat and power plants (CHPs) is becoming more challenging. CHPs need to become more flexible to react to volatile renewable infeed and volatile energy prices. Such more flexible operation can be achieved by considering the heat storage capabilities of the connected district heating grid. But finding a good optimization model for heating grid dynamics is hard, as the underlying problem is a mixed integer nonlinear program (MINLP) with bilinear terms and time delays. There has not been much attention on global optimization of this problem yet. In this paper we present a new approach to find a global optimum for this MINLP using multiparametric disaggregation for bilinear terms and proposing “multiparametric delay modeling” for the modeling of time delays. It can be used to benchmark existing and future non-global optimization schemes.

Keywords

Global optimization Mixed integer nonlinear programming Integrated heat and power dispatch 

Notes

Acknowledgements

The authors gratefully acknowledge funding by the German Federal Ministry of Education and Research (BMBF) within the Kopernikus Project ENSURE ‘New ENergy grid StructURes for the German Energiewende’.

References

  1. 1.
    Lund, H., Østergaard, P.A., Connolly, D., Ridjan, I., Mathiesen, B.V., Hvelplund, F., Thellufsen, J.Z., Sorknæs, P.: Energy storage and smart energy systems. Int. J. Sustain. Energy Plann. Manag. 11, 3–14 (2016)Google Scholar
  2. 2.
    Li, P., Wang, H., Lv, Q., Li, W.: Combined heat and power dispatch considering heat storage of both buildings and pipelines in district heating system for wind power integration. Energies. 10, 893 (2017)CrossRefGoogle Scholar
  3. 3.
    Sandou, G., Font, S., Tebbani, S., Hiret, A., Mondon, C.: Predictive control of a complex district heating network. In: Proceedings of the 44th IEEE Conference on Decision and Control and the European Control Conference, Seville, Spain (2005)Google Scholar
  4. 4.
    Loukarakis, E., Mancarella, P.: A sequential programming method for multi-energy districts optimal power flow. In: 2017 IEEE Manchester PowerTech, Manchester, UK (2017)Google Scholar
  5. 5.
    Giraud, L.: Modélisation dynamique et gestion avancée de réseaux de chaleur. PhD, Université Grenoble Alpes (2016)Google Scholar
  6. 6.
    Groß, S.: Untersuchung der Speicherfähigkeit von Fernwärmenetzen und deren Auswirkungen auf die Einsatzplanung von Wärmeerzeugern. PhD, TU Dresden (2012)Google Scholar
  7. 7.
    Li, Z., Wu, W., Shahidehpour, M., Wang, J., Zhang, B.: Combined heat and power dispatch considering pipeline energy storage of district heating network. IEEE Trans. Sustain. Energy. 7, 12–22 (2016)CrossRefGoogle Scholar
  8. 8.
    Schweiger, G., Larsson, P.-O., Magnusson, F., Lauenburg, P., Velut, S.: District heating and cooling systems—framework for Modelica-based simulation and dynamic optimization. Energy. 137, 566–578 (2017)CrossRefGoogle Scholar
  9. 9.
    McCormick, G.P.: Computability of global solutions to factorable nonconvex programs: part I. Convex underestimating problems. Math. Program. 10, 147–175 (1976)CrossRefGoogle Scholar
  10. 10.
    Teles, J.P., Castro, P.M., Matos, H.A.: Global optimization of water networks design using multiparametric disaggregation. Comput. Chem. Eng. 40, 132–147 (2012)CrossRefGoogle Scholar
  11. 11.
    Castro, P.M., Teles, J.P.: Comparison of global optimization algorithms for the design of water-using networks. Comput. Chem. Eng. 52, 249–261 (2013)CrossRefGoogle Scholar
  12. 12.
    Gurobi Optimization.: Gurobi Optimizer Reference Manual. http://www.gurobi.com (2016)
  13. 13.
    Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Program. 106, 25–57 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ABB Corporate Research Center GermanyLadenburgGermany
  2. 2.Karlsruhe Institute of Technology, Institute for Control SystemsKarlsruheGermany

Personalised recommendations