By Mag. Gerald Schweiger MA, researcher at AEE INTEC in Gleisdorf/Austria and lector at Technical University Graz in Graz/Austria. PhD Stéphane Velut, PhD, Modelon AB
To enable the transition towards a low carbon energy system, we need to increase the efficiency of existing systems and to assess and design completely new energy systems that may differ fundamentally from those of today. Both require computational tools and methods that allow detailed investigations, depicting and mapping real systems as precisely as possible. This article presents a novel framework that allows for fully dynamic simulations and optimizations of 4th Generation District Heating (4GDH) Systems. Two cases show the applicability of the framework. An existing district heating network is adopted to test the simulation requirements for 4GDH systems. The second case presents the dynamic optimization of a district heating system in a planned city district based on physical models of the entire system.
NOVEL FRAMEWORK HELPS TO UNDERSTAND ON-GRID ENERGY SYSTEM
language Modelica and a high-level, large-scale continuous dynamic optimization method implemented in JModelica.org and Optimica Compiler Toolkit (OCT). Out-of-the box models for simulation and optimization are available in Modelon’s Thermal Power Library. THE FRAMEWORK The framework consists of a library for fully dynamic investigations of district heating systems and an automated workflow which is shown in Figure 2. We developed a comprehensive library with out-of-the box models for simulation and optimization. Engineers are used to such models for simulation; out-of-the box models for optimization are rare but offer novel and intuitive possibilities for engineers. The district heating library consists of models for production, distribution, storage and consumption as well as different control schemes. Models can be connected intuitively while no programming skills are required. Advanced users can further utilize the advantages of Modelica and adopt existing models or develop new ones.
The overall goal was to develop numerical methods and tools for the simulation and optimization of (future) district heating and cooling systems. As fluctuating energy sources have an increased share in the overall energy mix, other parts of the energy systems must become more flexible to match the available renewable supply with the demand in terms of location, time, quantity and quality. Previous research has underpinned the potential of district heating to provide flexibility for the overall energy system. District heating provides multiple options for the integration of renewable energies, short & long- term storage technologies, Power-to-Heat (P2H), (industrial) waste heat utilization and smart integration of other urban infrastructures such as wastewater treatment plants. To address the challenges of this emerging paradigm in district heating design and operation, new requirements arise for simulation and optimization tools such as dynamic and multi-domain analysis. The author was lucky to get intense support from university and industry in the field of modelling, and applied math as well as from applied research and industry to tackle real world problems and to look two steps ahead, where we find various ideas, concepts and even more uncertainties.
Unified network representation
Here, we present a novel framework to represent and simplify on-grid energy systems as well as to perform fully dynamic, thermo-hydraulic simulation and optimization of district heating and cooling systems. This article is partially based on our scientific publications, where more details including a precise mathematical formulation of the proposed methods can be found. This framework combines the engineering need for high-level description format including readily available and validated component libraries and sophisticated numerical optimization methods which are often cumbersome to use. The framework is based on the modelling
Fully dynamic investigations Automated workflow Real-time applicable
Solving the problem using Jmodelica.org / OCT
Dynamic optimization problem
Figure 1: The framework: from the network plan to dynamic optimization results
E N E R G Y A N D E N V I R O N M E N T
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