Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter (O) November 27, 2020

Mixed-integer nonlinear optimization for district heating network expansion

Gemischt-ganzzahlige nichtlineare Optimierung für den Ausbau von Fernwärmenetzen
  • Marius Roland and Martin Schmidt EMAIL logo

Abstract

We present a mixed-integer nonlinear optimization model for computing the optimal expansion of an existing tree-shaped district heating network given a number of potential new consumers. To this end, we state a stationary and nonlinear model of all hydraulic and thermal effects in the pipeline network as well as nonlinear models for consumers and the network’s depot. For the former, we consider the Euler momentum and the thermal energy equation. The thermal aspects are especially challenging. Here, we develop a novel polynomial approximation that we use in the optimization model. The expansion decisions are modeled by binary variables for which we derive additional valid inequalities that greatly help to solve the highly challenging problem. Finally, we present a case study in which we identify three major aspects that strongly influence investment decisions: the estimated average power demand of potentially new consumers, the distance between the existing network and the new consumers, and thermal losses in the network.

Zusammenfassung

Wir präsentieren ein gemischt-ganzzahliges und nichtlineares Optimierungsmodell zur Berechnung des optimalen Ausbaus eines bestehenden und baumförmigen Fernwärmenetzes für eine Menge an gegebenen, potenziellen neuen Verbrauchern. Zu diesem Zweck stellen wir ein stationäres und nichtlineares Modell aller hydraulischen und thermischen Effekte im Leitungsnetz sowie nichtlineare Modelle für die Verbraucher und den Betriebshof vor. Hierfür betrachten wir zunächst die Eulergleichungen und die Energiegleichung. Die thermischen Aspekte stellen eine besondere Herausforderung dar, für die wir eine neuartige polynomielle Approximation entwickeln, die wir im Optimierungsmodell verwenden. Die Ausbauentscheidungen werden durch binäre Variablen modelliert, für die wir zusätzliche gültige Ungleichungen herleiten, die die Lösung der äußerst anspruchsvollen Probleme beschleunigen. Schließlich analysieren wir eine Fallstudie und identifizieren drei Hauptaspekte, die die Investitionsentscheidungen stark beeinflussen: der geschätzte durchschnittliche Leistungsbedarf neuer Verbraucher, die Entfernung zwischen dem bestehenden Netz und den neuen Verbrauchern und die thermischen Verluste im Netz.

Award Identifier / Grant number: 154

Award Identifier / Grant number: EiFer

Funding statement: The second author thanks the Deutsche Forschungsgemeinschaft for their support within projects A05 and B08 in the Sonderforschungsbereich/Transregio 154 “Mathematical Modelling, Simulation and Optimization using the Example of Gas Networks”. Both authors acknowledge the support by the German Bundesministerium für Bildung und Forschung within the project “EiFer”.

Acknowledgment

We are very grateful to all the colleagues within the EiFer consortium for many fruitful discussions on the topics of this paper and for providing the data.

Appendix A Proof of Lemma 1

Two cases are considered for solving the ODE. First, we consider va=0. Then, (5) reduces to

4UacpρDa(Ta(x)Tsoil)=0,x[0,La].

This obviously leads to the first case in (6).

Next, we consider the case va>0. In this case, (5) can be rewritten as

dTadx(x)+4UacpρDavaTa(x)=4UacpρDavaTsoil,x[0,La].

We know from classical ODE theory, see, e. g., [13], that the first-order ODE of the form

dy(x)dx+ay(x)=b,

with constants a and b has the solution

y(x)=Ceax+ba,

with C being another constant. This then allows to derive the second case of (6).

To prove continuity and since both cases in (6) are composed of continuous functions it suffices to see that

limva0Ta(x;va)=Tsoil=Ta(x;0)

for all x[0,La]. This concludes the proof.

References

1. M. Ameri and Z. Besharati. Optimal design and operation of district heating and cooling networks with CCHP systems in a residential complex. Energy and Buildings, 110:135–148, 2016.10.1016/j.enbuild.2015.10.050Search in Google Scholar

2. A. Benonysson, B. Bøhm and H. F. Ravn. Operational optimization in a district heating system. Energy Conversion and Management, 36(5):297–314, 1995.10.1016/0196-8904(95)98895-TSearch in Google Scholar

3. M. Blommaert, R. Salenbien and M. Baelmans. An adjoint approach to thermal network topology optimization, 2018.10.1615/IHTC16.cms.024074Search in Google Scholar

4. C. Bordin, A. Gordini and D. Vigo. An optimization approach for district heating strategic network design. European Journal of Operational Research, 252(1):296–307, 2016.10.1016/j.ejor.2015.12.049Search in Google Scholar

5. R. Borsche, M. Eimer and N. Siedow. A local time stepping method for district heating networks, 2018.Search in Google Scholar

6. S. Bracco, G. Dentici and S. Siri. Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area. Energy, 55:1014–1024, 2013.10.1016/j.energy.2013.04.004Search in Google Scholar

7. European Commission. Communication from the cimmission to the European parliament, the European council, the council, the European economic and social committe and the committe of the regions: The European green deal, 2019. Accessed 2020-04-02.Search in Google Scholar

8. T. Falke, S. Krengel, A.-K. Meinerzhagen and A. Schnettler. Multi-objective optimization and simulation model for the design of distributed energy systems. Applied Energy, 184:1508–1516, 2016.10.1016/j.apenergy.2016.03.044Search in Google Scholar

9. A. Fügenschuh, B. Geißler, R. Gollmer, A. Morsi, M. E. Pfetsch, J. Rövekamp, M. Schmidt, K. Spreckelsen and M. C. Steinbach. Physical and technical fundamentals of gas networks. In T. Koch, B. Hiller, M. E. Pfetsch and L. Schewe, editors, Evaluating Gas Network Capacities, SIAM-MOS series on Optimization, chapter 2, pages 17–44. SIAM, 2015.10.1137/1.9781611973693.ch2Search in Google Scholar

10. B. Geißler, A. Morsi, L. Schewe and M. Schmidt. Solving highly detailed gas transport MINLPs: Block separability and penalty alternating direction methods. INFORMS Journal on Computing, 30(2):309–323, 2018.10.1287/ijoc.2017.0780Search in Google Scholar

11. E. Guelpa, G. Mutani, V. Todeschi and V. Verda. Reduction of CO2 emissions in urban areas through optimal expansion of existing district heating networks. Journal of Cleaner Production, 204:117–129, 2018.10.1016/j.jclepro.2018.08.272Search in Google Scholar

12. C. Haikarainen, F. Pettersson and H. Saxén. A decomposition procedure for solving two-dimensional distributed energy system design problems. Applied Thermal Engineering, 100:30–38, 2016.10.1016/j.applthermaleng.2016.02.012Search in Google Scholar

13. E. Hairer, S. P. Nørsett and G. Wanner. Solving ordinary differential equations I. Nonstiff problems. Springer Series in Computational Mathematics, 1993.Search in Google Scholar

14. F. M. Hante and M. Schmidt. Complementarity-based nonlinear programming techniques for optimal mixing in gas networks. EURO Journal on Computational Optimization, 7(3):299–323, 2019.10.1007/s13675-019-00112-wSearch in Google Scholar

15. W. E. Hart, C. D. Laird, J.-P. Watson, D. L. Woodruff, G. A. Hackebeil, B. L. Nicholson and J. D. Siirola. Pyomo-optimization modeling in Python. Springer, 2017.10.1007/978-3-319-58821-6Search in Google Scholar

16. W. E. Hart, J.-P. Watson and D. L. Woodruff. Pyomo: modeling and solving mathematical programs in Python. Mathematical Programming Computation, 3(3):219–260, 2011.10.1007/s12532-011-0026-8Search in Google Scholar

17. S.-A. Hauschild, N. Marheineke, V. Mehrmann, J. Mohring, A. M. Badlyan, M. Rein and M. Schmidt. Port-Hamiltonian modeling of district heating networks. In Progress in Differential Algebraic Equations II, Differential-Albergaic Equations Forum. Springer, 2020.10.1007/978-3-030-53905-4_11Search in Google Scholar

18. R. Krug, V. Mehrmann and M. Schmidt. Nonlinear optimization of district heating networks. Optimization and Engineering, 2020.10.1007/s11081-020-09549-0Search in Google Scholar

19. R. Köcher. Beitrag zur Berechnung und Auslegung von Fernwärmenetzen, 2000.Search in Google Scholar

20. X.-l. Li, L. Duanmu and H.-w. Shu. Optimal design of district heating and cooling pipe network of seawater-source heat pump. Energy and Buildings, 42(1):100–104, 2010. International Conference on Building Energy and Environment (COBEE 2008).10.1016/j.enbuild.2009.07.016Search in Google Scholar

21. J. E. Marsden and A. J. Chorin. A mathematical introduction to fluid mechanics. Springer-Verlag, 1993.10.1007/978-1-4612-0883-9Search in Google Scholar

22. D. Meha, T. Novosel and N. Duić. Bottom-up and top-down heat demand mapping methods for small municipalities, case Gllogoc. Energy, 199:117429, 2020.10.1016/j.energy.2020.117429Search in Google Scholar

23. T. Mertz, S. Serra, A. Henon and J. Reneaume. A MINLP optimization of the configuration and the design of a district heating network: study case on an existing site. Energy Procedia, 116:236–248, 2017. 15th International Symposium on District Heating and Cooling, DHC15-2016, 4–7 September 2016, Seoul, South Korea.10.1016/j.egypro.2017.05.071Search in Google Scholar

24. T. Mertz, S. Serra, A. Henon and J.-M. Reneaume. A MINLP optimization of the configuration and the design of a district heating network: Academic study cases. Energy, 117:450–464, 2016. The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems—ECOS 2015.10.1016/j.energy.2016.07.106Search in Google Scholar

25. R. Misener and C. A. Floudas. Antigone: Algorithms for continuous/integer global optimization of nonlinear equations. Journal of Global Optimization, 59(2):503–526, 2014.10.1007/s10898-014-0166-2Search in Google Scholar

26. B. Morvaj, R. Evins and J. Carmeliet. Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout. Energy, 116:619–636, 2016.10.1016/j.energy.2016.09.139Search in Google Scholar

27. T. Novosel, T. Pukšec, N. Duić and J. Domac. Heat demand mapping and district heating assessment in data-pour areas. Renewable and Sustainable Energy Reviews, 131:109987, 2020.10.1016/j.rser.2020.109987Search in Google Scholar

28. T. Nussbaumer and S. Thalmann. Influence of system design on heat distribution costs in district heating. Energy, 101:496–505, 2016.10.1016/j.energy.2016.02.062Search in Google Scholar

29. A. Omu, R. Choudhary and A. Boies. Distributed energy resource system optimisation using mixed integer linear programming. Energy Policy, 61:249–266, 2013.10.1016/j.enpol.2013.05.009Search in Google Scholar

30. M. Pagani, P. Maire, W. Korosec, N. Chokani and R. Abhari. District heat network extension to decarbonise building stock: A bottom-up agent-based approach. Applied Energy, 272:115177, 2020.10.1016/j.apenergy.2020.115177Search in Google Scholar

31. M. Rein, J. Mohring, T. Damm and A. Klar. Parametric model order reduction for district heating networks. PAMM, 18(1), 2018.10.1002/pamm.201800192Search in Google Scholar

32. G. Sandou, S. Font, S. Tebbani, A. Hiret, C. Mondon, S. Tebbani, A. Hiret and C. Mondon. Predictive control of a complex district heating network. In Proceedings of the 44th IEEE Conference on Decision and Control, pages 7372–7377, 2005.10.1109/CDC.2005.1583351Search in Google Scholar

33. M. Schmidt, M. C. Steinbach and B. M. Willert. High detail stationary optimization models for gas networks. Optimization and Engineering, 16(1):131–164, 2015.10.1007/s11081-014-9246-xSearch in Google Scholar

34. M. Schmidt, M. C. Steinbach and B. M. Willert. The precise NLP model. In T. Koch, B. Hiller, M. E. Pfetsch and L. Schewe, editors, Evaluating Gas Network Capacities, SIAM-MOS series on Optimization, chapter 10, pages 181–210. SIAM, 2015.10.1137/1.9781611973693.ch10Search in Google Scholar

35. M. Schmidt, M. C. Steinbach and B. M. Willert. High detail stationary optimization models for gas networks: validation and results. Optimization and Engineering, 17(2):437–472, 2016.10.1007/s11081-015-9300-3Search in Google Scholar

36. J. Söderman. Optimisation of structure and operation of district cooling networks in urban regions. Applied Thermal Engineering, 27(16):2665–2676, 2007. Selected Papers from the 9th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction—PRES2006.10.1016/j.applthermaleng.2007.05.004Search in Google Scholar

37. TWL. Preise für Fernwärme, 2020. Last accessed 2020-03-25.Search in Google Scholar

38. F. Verrilli, S. Srinivasan, G. Gambino, M. Canelli, M. Himanka, C. Del Vecchio, M. Sasso and L. Glielmo. Model predictive control-based optimal operations of district heating system with thermal energy storage and flexible loads. IEEE Transactions on Automation Science and Engineering, 14(2):547–557, 2017.10.1109/TASE.2016.2618948Search in Google Scholar

39. S. Werner. District heating and cooling in Sweden. Energy, 126:419–429, 2017.10.1016/j.energy.2017.03.052Search in Google Scholar

Received: 2020-04-23
Accepted: 2020-09-15
Published Online: 2020-11-27
Published in Print: 2020-11-18

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 27.4.2024 from https://www.degruyter.com/document/doi/10.1515/auto-2020-0063/html
Scroll to top button