Mobility Scheme Description
In the frame of the SmILES project a mobility scheme was set up by the project partner EERA AISBL in the lead of Elena Dufour for realizing a researcher exchange. The content of this procedure is inspired on the successful mobility schemes of the ELECTRA IRP and INSHIP ECRIA projects. We specifically acknowledge, with thanks, the contributions from the Electra IRP and the INSHIP.
Reports of the SmILES researcher exchanges
Report to be completed
Within the SmiLES project, the cross-simulation activity is an important step towards developing structured ways of exchanging models between project partners. In the mobility scheme, I went to DTU to evaluate the possibility to replicate the DTU Nordhavn system configuration in our KIT toolchain. At KIT, we mainly use MATLAB and an optimization toolbox called CasADi for simulation and optimal control. This environment requires algebraic or differential equations in form of MATLAB functions. By acquiring domain knowledge of district heating and electricity grids, we were able to develop simple linear models of these two domains of Nordhavn and developed a first implementation in the KIT toolchain. Secondly, we developed first forms for structured evaluation and testing procedures for comparing simulation and optimization results of partners within SmiLES in a meaningful way.
A crucial step for the project SmILES is to derive methods to describe multi-energy systems in detail to enable others, project partners or third-parties, to assess the system with their respective tools and methods. Several forms and formats to describe relevant parts of the system (configuration, control, data, etc.) were already developed during the project. However, testing them on a specific system under study was not performed until now. Such a test is expected to identify shortcomings of the method and will highlight areas where improvements in the description forms are needed. This test is planned as part of SmILES WP4 (Task 4.4) using a simple system. During this researcher exchange the test system was described by AIT using the SmILES forms and formats. It was then implemented in the toolchains of DTU and AIT to assess the exchangability of multi-energy system descriptions using the SmILES description forms.
An important part of the SmILES project are the so-called cross-simulation activities planned. Cross-simulation partners thereby exchange data about their system under study to enable their counterpart to model and simulate this system with their respective toolchain. SmILES aims at providing the necessary methods to describe multi-energy systems and thus to allow for such a description and exchange of data. EDF and AIT agreed to provide data in order to enable such a cross-simulation of their systems under study. The main activity during this exchange was focused on the preparation of models and simulations by AIT to enable a cross-simulation of EDF’s system “Collectopia”. A major part of the work done at EDF during the exchange was to create a dynamic model of “Collectopia” in Modelica and to write a supervisory controller that is responsible for the operation of the booster heat pumps in Python. This model can be used a basis for further investigations of the system “Collectopia” with AIT’s toolchain.
The intention of developing a model of seasonal thermal energy storage in Dymola/Modelica as a part of WP4 is due to the fact that this language is widely used in the dynamic simulation of thermal systems, such as district heating and cooling networks, and, whilst offering from the one side a multitude of open-source libraries allowing the simulation of several system components, it lacks from the other side of valid models for seasonal storages (and in particular for underground systems). The model herein developed and (partly) validated is expected to be helpful in setting up simulations e.g. to evaluate energy networks integrating large-scale TES in a variety of scenarios (e.g. to pursue a large-scale integration of solar thermal or industrial waste heat) and taking the advantages of already existing Dymola/Modelica models as for the other system components. The underground TES model bases on assembling and adapting tools already available in the public Modelica libraries. The validation relies on data derived from detailed field measurements as well as from mathematical models of the thermo-hydraulic system.
One of the main objectives in the SmILES project is to up-scale and replicate the smart storage solutions in energy sector from a local to the national level. Scaling up is important topic for the large-scale rollout of smart storage technologies and infrastructures. The main objective in this report was to evaluate the costs and identify the main barriers of upscaling the energy storage systems such as battery storages in a rural area ”Eberstalzell und Littring (ELZ)” in Austria. To this aim we used the Balmorel tool which is an open source modelling tool for dispatch and investment planning in electricity and district heating sectors. In this research activity, first we implemented ELZ with the Austrian Balmorel model and then evaluated the costs of upscaling storage technologies in this region and in the country level. By comparing the scalability of storage facilities in these regions, it is important to consider that the potentials for storage technologies in the Austria and EZL are different. While hydro pump storages provide a considerable contribution of flexibility resources to the Austrian electricity sector, the potential for pump storage in EZL is zero and batteries provide the required flexibility in this region. In ELZ region operating as an energy island, increasing the electricity demand leads to an increase in the investment in battery storages and consequently the total system costs. By increasing the electricity exchange capacity between ELZ and the rest of Austria, the investment in battery storages decreases rapidly because of the lower costs of importing electricity from grid in comparison to investing in battery storages. Results show that the economically optimal share of storage technologies in the Austrian electricity sector raises from 3.12 GW in 2018 to 4.83 GW in 2030, while the optimal capacity of battery storages in 2030 is expected to be 130 MW.