Ant Colony Optimization for Optimized Operation Scheduling of Combined Heat and Power Plants

  • Johannes MastEmail author
  • Stefan Rädle
  • Joachim Gerlach
  • Oliver Bringmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11454)


In the worldwide expansion of renewable energies, there is not only a need for weather-dependent plants, but also for plants with flexible power generation that have the potential to reduce storage requirements by working against fluctuations. A highly promising technology is provided by Combined heat and power (CHP) plants, which achieve high efficiencies through the simultaneous generation of electricity and heat. This is why they are also being promoted by the European Union. Also, the construction of biogas plants is usually linked to the construction of CHP plants in order to generate energy from the emission-free produced biogas. However, until now CHP plants have mostly been operated by heat demand (just like boilers), causing the generated electricity often to put additional stress on the power grid. The planning of a CHP plant, whose generated heat always finds a consumer and the generated electricity is simultaneously optimized with regard to an optimization objective, requires nonlinear optimization approaches due to the physical effects in the heat storage. This paper presents a methodology for optimized planning of CHP plants using Ant Colony Optimization. The selected optimization objectives are the power exchange, the tenant electricity and CO\(_2\). It could be shown that all optimizations are at least 10% better than the heat-led operation. The best results were achieved with the electricity exchange optimization that can be up to 24% more profitable than a CHP in a heat-led mode.


Ant Colony Optimization Combined heat and power Operation scheduling Electricity exchange Tenant electricity 


  1. 1.
    Directive 2004/08/EC of the European parliament and of the council. Off. J. Eur. Union 47(52), 50–60 (2004)Google Scholar
  2. 2.
    Chen, H., Yu, Y., Jiang, X.: Optimal scheduling of combined heat and power units with heat storage for the improvement of wind power integration. In: 8th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1508–1512. IEEE (2016).
  3. 3.
    Mongibello, L., Graditi, G., Bianco, N., Musto, M., Caliano, M.: Optimal operation of residential micro-CHP systems with thermal storage losses modelling. In: 2014 International Symposium on Power Electronics, Electrical Drives, pp. 1027–1033. IEEE (2014).
  4. 4.
    Majic, L., Krzelj, I., Delimar, M.: Optimal scheduling of a CHP system with energy storage. In: 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1253–1257. IEEE (2013)Google Scholar
  5. 5.
    Sauter, A., Gerlach, J., Bringmann, O.: Simulationsbasierte analyse energietechnischer systemszenarien. In: 19th Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV), pp. 139–150. Freiburg (2016)Google Scholar
  6. 6.
    Blum, C.: Ant Colony Optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005). Scholar
  7. 7.
    Katiyar, S., Nasiruddin, I., Ansari, A.Q.: Ant Colony Optimization: a tutorial review. In: National Conference on Advances in Power and Control, pp. 99–110. Manav Rachna International University, Faridabad (2016)Google Scholar
  8. 8.
    Stephen, A.A., Misra, S.: A comprehensive study on the Ant Colony Optimization algorithms. In: 11th International Conference on Electronics, Computer and Computation (ICECCO), pp. 1–4. IEEE (2014).
  9. 9.
    Nowak, W., Arthkamp, J.: BHKW-Grundlagen, 1st edn. ASUE Arbeitsgemeinschaft, Berlin (2010)Google Scholar
  10. 10.
    Günther, M.: Energieeffizienz durch Erneuerbare Energien. Springer, Wiesbaden (2015). Scholar
  11. 11.
    Senertec: Dachs G/F und HR - Technical data sheet. Accessed 4 Nov 2018
  12. 12.
    Mast, J., Rädle, S., Gerlach, J.: Multikriterien-Optimierung energietechnischer Komponenten unter Anwendung von Methoden der Künstlichen Intelligenz. In: 59th MPC-Workshop, pp. 59–66. IEEE German Section Solid-State Circuit Society (2018)Google Scholar
  13. 13.
    Andrychowicz, M., Przybylski, J.: Evaluation of the flexibility approach in construction of Scenario Outlook & Adequacy Forecast 2015 by ENTSO-E. In: 13th International Conference on the European Energy Market (EEM), pp. 1253–1257. IEEE (2016).
  14. 14.
    Landlord-to-tenant electricity supply. Accessed 5 Nov 2018

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Albstadt-Sigmaringen UniversityAlbstadtGermany
  2. 2.University of TübingenTübingenGermany

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