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

Abstract

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 highfidelity, 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 bypass 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. fy flexible optimization problems including constraints on arbitrary process variables such as heat load of the production units, supply temperature and flow rate, pressures.

Optimization-friendly thermodynamic properties of water and steam

“…(Velut, et al, 2014) and (Runvik, 2014) mention the use of "smooth media model functions" but don't go into any detail. (Casella, Donida, & Åkesson, 2011) use simplifications such as incompressible fluids with constant heat capacity for non-saturated liquid and steam.…”

“…to improve efficiency and economical aspects in energy applications. Examples where Modelica models have been used include start-up of power plants (Casella, Donida, & Åkesson, 2011), (Runvik, 2014), (Parini, 2015), production planning of district heating networks (Velut, et al, 2014) and power plant load scheduling (Kumar & Mathur, 2014). Modelica is well suited to describe the behavior of dynamical models and thereby also suitable to be used in the context of optimization.…”

Framework for dynamic optimization of district heating systems using Optimica Compiler Toolkit

“…Therefore standard approaches are restricted and thus unsuitable for investigating many issues. The presented framework is based on the previous work of some authors (Velut et al, 2014;Runvik et al, 2015;Schweiger et al, 2017a).…”