Short-term production planning for district heating networks with JModelica.org

Stéphane Velut
Modelon AB, Ideon Science Park, Lund, Sweden

Per-Ola Larsson
Modelon AB, Ideon Science Park, Lund, Sweden

Johan Windahl
Modelon AB, Ideon Science Park, Lund, Sweden

Linn Saarinen
Vattenfall R&D, Älvkarleby, Sweden

Katarina Boman
Vattenfall R&D, Älvkarleby, Sweden

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp14096959

Ingår i: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:100, s. 959-968

Visa mer +

Publicerad: 2014-03-10

ISBN: 978-91-7519-380-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


The short term thermal production planning problem is solved in two steps by integrating physical plant models into the standard approach. The first step aims at solving the discrete variables from the unit commitment sub-problem (UCP) using standard mixed integer linear models and optimization techniques. The second step focuses on the economic dispatch sub-problem (EDP) described by high-fidelity; continuous time; physics-based Modelica models together with nonlinear optimization techniques from the JModelica.org platform. The output of the second step includes optimized power flows but also highly relevant variables such as supply temperature; supply flow rate; turbine by-pass valve in the cogeneration plant. The optimization is formulated as a maximization of the benefit from heat and electricity sell over a finite time-horizon.

The proposed method is validated in several test cases using experimental data from a plant in Nyköping. The optimizations demonstrate the feasibility and the high economic potential of the proposed approach when comparing with measurement data and the standard optimization techniques. The optimized planning schedules result in a balance between produced and consumed heat; priority to low-cost boilers and maximization plant revenue. Compared to measurement data; the optimizations result in a significantly lower supply temperature; a more extensive usage of the external cooler for higher efficiency and higher electricity production; fewer starts of units as well as an appropriate use of the accumulator tank.

The high-level description of optimization problems using JModelica.org provides useful means to specify flexible optimization problems including con-straints on arbitrary process variables such as heat load of the production units; supply temperature and flow rate; pressures.


Production planning; nonlinear optimization; district heating; physical modeling; unit commitment


[1] C. Cervantes and L. T. Biegler,"Optimization strategies for dynamic systems," in C. Floudas, P. Pardalos (Eds), Encyclopedia of Optimization, 2000.

[2] J. Åkesson, C. Laird, G. Lavedan, K. Prölss, H. Tummesheit, S. Velut and Y. Zhu,"Nonlinear Model Predictive Control of a CO2 post-combustion unit," Chemical Engineerging Technology, 2011.

[3] Ipopt homepage, coinor, http://projects.coinor.org/Ipopt/wiki/IpoptPapers.

[4] S. Mitchell, A. Mason, M. O’Sullivan and A. Phillips, "PuLP: a linear programming toolkit for python," http://www.coin-or.org/PuLP/.

[5] CBC Team, "CBC home page," 2013. [Online]. Available: https://projects.coinor.org/Cbc. [Accessed 12 August 2013].

[6] L. Saarinen, "Model-based control of district heating supply temperature," Värmeforsk P08-819, 2010.

[7] L. Saarinen and K. Boman, "Optimized district heating supply temperature for large networks," Värmeforsk P08-830, 2012.

[8] L. Saarinen, "Modeling and control of a district heating system," Master thesis, Uppsala University, 2008.

[9] Dassault Systemes, "Dassault Systemes Home Page," 2013. [Online]. Available: http://www.3ds.com/products/catia/portfolio/dymola. [Accessed 6 August 2013].

[10] The Modelica Association, "The Modelica Association Home Page," 2013. [Online]. Available: http://www.modelica.org. [Accessed 6 August 2013].

[11] J. Arroyo and A. Conejo, "Modeling of startup and shut-down power trajectories of thermal units," IEEE Transactions on power systems, vol. 19, no. 3, 2004.

[12] A. Wächter and L. T. Biegler, "On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming," Mathematical Programming, vol. 196, no. 1, pp. 25-68, 2006.

[13] HSL, "A collection of Fortran codes for large scale scientific computation," 2013. [Online]. Available: http://www.hsl.rl.ac.uk. [Accessed 13 August 2013].

[14] P.-O. Larsson, PhD thesis: Optimization of Low-Level Controllers and High-Level Polymer Grade Changes, Lund, 2011.

[15] E. Dotzauer, "Algorithms for Short-Term Production planning of Cogeneration Plant," Lic. Thesis, Linköping University, 1997.

[16] "www.jmodelica.org," Modelon AB, 2013. [Online]. Available: www.jmodelica.org. [Accessed 2013].

[17] Bauer, O. Modelling of Two-Phase Flows with Modelica, Master’s Thesis, Lund University, Department of Automatic Control, 1999.

[18] S. Velut, P.O. Larsson, J. Windahl, L. Saarinen, K. Boman, Non-linear and Dynamic Optimization for Short-term Production Planning. Värmeforsk report, 2013.

Citeringar i Crossref