At the Modelica Conference in Paris, I had the opportunity to present to the audience a work that I, together with colleagues from Modelon, Vattenfall R&D and SICS Swedish ICT, carried out over 2014 – employing JModelica.org for production planning in distributed district heating networks.
Now I’m glad to offer you a glimpse into this topic that lately has increased in interest. The models and applied tool have high relevance for operators of both thermal power plants and district heating networks.
Using our approach, the quality of the production plans can be substantially increased as the supply temperature and flow are optimized together with the unit loads. The production plan accounts also for the typical operational constraints in the network: pump capacity or minimum temperature at the customer station.
The improved plan quality is a direct result of the chosen technology:
- to optimize the physical models without simplifications
- and to set constraint on any temperature, flow or pressure in the system.
Our JModelica.org-based workflow proposes to separate the production planning problem into two optimization problems in series:
By our proposed method and optimization solutions, engineers in the field can for example:
We applied our methods on a representation of the district heat network in Uppsala, including three production units.
Our study included four cases with incremental complexity:
With this study we could demonstrate that separation into two optimization studies works well and that limitations of real plants can conveniently be incorporated. The optimization model for the network showed among others that:
For the ones of you who have attended our Tutorial at the Modelica Conference 2015 you can recognize the tool used in the training.
I’ll be happy to hear more about how this work is relevant to yours, and to answer questions on how to implement such an approach and what you can expect as results.
Per-Ola Larsson holds a Ph.D. in Automatic Control and an M.Sc. in Electrical Engineering, both from Lund University and with focus on optimization, process control and signal processing. At Modelon he is a simulation consultant and project manager in several consultancy and research projects within the field of thermodynamics, containing both steady-state and dynamic models as well as several types of optimizations.