Luka Herc

Working on holistic approaches that aim to capture a wide range of knowledge, researchers are usually faced with phenomena characterized by different time and geographical scales. This is the case of energy systems and Integrated Assessment Models (IAMs). More specifically, the nature of the variable renewable energy supply (VRES) has traditionally posed a barrier to accurately capturing the effects inflicted by VRES in the energy system. This research provides a soft link between an energy system model running with an hourly time step, on the one hand, and a yearly-based IAM, on the other hand, by the implementation of an emulator. The proposal here presented is a bridge, based on different types of knowledge, which successfully allows the flow of information between time scales. Results achieve a 100% renewable energy system on a case of Bulgaria. After a brief literature review on the topic, the method is explained in detail, including some results between EnergyPLAN (energy syst...

Dear colleagues, This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible. <strong>Python scripts </strong>("create_inputs.txt" and "run_simulations.txt") creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations (which are saved in the "EU_Iterate_case.xlsx" file). Hourly distributions of demands and supply technologies are contained in the RAR file ("EUdist.rar") and "EU_start_v2_noFlex.txt" initialize the starting configuration of the European energy system. Those files are required to run EnergyPLAN. The <strong>PowerPoint file</strong> ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the runs of combinations in Python/Excel. In case you couldn't properly do the combinations, the<strong> Excel file</strong> ("EU.xlsx") saves this information, so the steps of the approach could be followed from this point with the Excel file. We have used Power Query (Excel) to prepare the data for the next step of building the regression models. The <strong>Matlab file (</strong>"CreateRegressionModels.m"<strong>)</strong> automatically generates the regression models for the European region of WILIAM (official model of the Locomotion project). Best regards, Gonzalo.

Dear colleagues, This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible. <strong>Python scripts </strong>("create_inputs.txt" and "run_simulations.txt") creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations (which are saved in the "EU_Iterate_case.xlsx" file). Hourly distributions of demands and supply technologies are contained in the RAR file ("EUdist.rar") and "EU_start_v2_noFlex.txt" initialize the starting configuration of the European energy system. Those files are required to run EnergyPLAN. The <strong>PowerPoint file</strong> ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the run...

This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible. <strong>Text files </strong>("create_inputs.txt" and "run_simulations.txt") --> The first Python script creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations. Hourly distributions required to run the energy model are contained in the RAR file ("EUdist.rar"). The <strong>PowerPoint file</strong> ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the runs of combinations in Python/Excel. In case you couldn't properly do the combinations, the<strong> Excel file</strong> ("EU.xlsx") saves this information, so the steps of the approa...

This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data and comment about our work by referencing the main authors of it. <strong>"create_inputs.txt"</strong> and <strong>"run_simulations.txt"</strong> --> The first Python script creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of permutations. The<strong> Excel files</strong> saves the information of how data has been prepared to generate the regression models. The <strong>Matlab files</strong> automatically generates the regression models by region of WILIAM (official model of the Locomotion project).

This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data and comment about our work, referencing the main authors of it. Parrado-Hernando, Gonzalo Department of System Engineering and Automation School of Industrial Engineering, University of Valladolid, Spain e-mail: gonzalo.parrado@uva.es Pfeifer, Antun Department of Energy, Power Engineering and Environment Faculty of Mechanical Engineering and Naval Architecture University of Zagreb, Zagreb, Croatia e-mail: Antun.Pfeifer@fsb.hr Herc, Luka Department of Energy, Power Engineering and Environment Faculty of Mechanical Engineering and Naval Architecture University of Zagreb, Zagreb, Croatia e-mail: luka.herc@frodo.fsb.hr Gjorgievski, Vladimir Faculty of Electrical Engineering and Information Technologies, University Ss Cyril and Methodius, Skopje, North Macedonia e-mail: vladimir.gjorgievski@feit.ukim.edu.mk Batas, Ilija School of Electrical Engineering University of Belgrade, Serbia e-m...

Geothermal energy is reliable renewable energy source. It has historically been limited to usage only on geologically active locations, but with the advancements in ORC technology, it has become viable to use relatively low temperature heat sources. New highly energy efficient buildings do not require high water temperature in their heating systems. Therefore, it is possible to use lower temperature heat sources. The most common case when designing district heating network is combination of buildings with differing thermal properties. In that case, additional heat source may be required to provide desired temperature of heating water. This can be geothermal heat pump standalone or in combination with electric heater, natural gas boiler or biomass boiler. In this thesis, two possible locations (Recica and Laniste) were analyzed for usage of geothermal energy in electricity and heat generation. Few technical solutions were made for each of them. This thesis describes the procedure of ...