Johannes Schmidt

Institute for Sustainable Economic Development
Department of Economics and Social Sciences
University of Natural Resources and Life Sciences, Vienna

Bio

I am currently Assistant Professor at the Institute for Sustainable Economic Development, at the University of Natural Resources and Life Sciences, Vienna (BOKU). My current main research interests are linked to the integrated modelling of energy systems with high shares of renewables. I'm interested in developing a comprehensive approach to the assessment of renewable energies, including technical, economic, social, and ecological aspects into my research. Recently, I have won the ERC Starting Grant for the reFUEL project, which deals with a global, integrated analysis of trade with renewable energy carriers and associated impacts on land-use. In 2017, I have also finished my habilitation. The full text of the treatise can be found here.

Research Summary

Current Research Activities

Currently, I'm involved in the RE_EXTREME research project together with Elisabeth Wetterlund at Luleå Technical University and Sylvain Leduc at IIASA, looking into how extreme events in the weather system affect renewable power generation. For that purpose, we use the COPA model model, which I previously developed at a research stay at the Energy Planning Program, Federal University of Rio de Janeiro.

I am also supporting Sebastian Wehrle in developing the MEDEA dispatch model: a unit commit model for the German-Austrian market zone. A current working paper uses the model to estimate the pass-through of CO2-prices to electricity prices. Current master students are working on developing simulation and optimization models in the field of renewable energy generation.

Previous Research Activities

I was visiting researcher at the EU Comission's Joint Research Center, Institute for Energy & Transportation of the EU Commission in Petten, Netherlands. There, I looked into the effects of renewable support policy on the spatial distribution of wind generation plants (link to paper, gated). In a subsequent project on wind energy, we assessed, together with other institutes at BOKU university here in Vienna, the potentials of wind energy with a special focus on considering limitations due to social acceptance issues. (Link to project website - in German)

In my doctoral thesis, I co-developed the Austrian BeWhere model together with Sylvain Leduc at IIASA. I used it to assess costs and greenhouse gas emissions of alternative bioenergy applications. Later, the same model was also used in an interdisciplinary context to assess the consequences of energy autarky for a small Austrian region (link, gated) and, in a large integrated modelling compound, the impacts of agricultural policy and climate change on ecosystem services, economics, and bio-energy supply. (link, gated)

Publications

Current working papers

Nogueira Hallack, L., Schmidt, J., Szklo, A. S., Pereira Júnior, A. O. Curve-fitting variants to model Brazil's Crude Oil offshore post-salt production.

Wehrle, S., Schmidt, J. Optimal emission prices for a district heating system owner.

Publications

A full list of publications can be found at my Google Scholar Profile or at the BOKU research information system.

Teaching

I am teaching the following master level classes:

I am teaching the following bachelor level classes:

Ressourcennutzung und Ressourcnemärkte

Master thesis

If you are interested in writing a master thesis in our group, please have a look at this document.

COPA model

Climate based Optimization of renewable Power Allocation (COPA) is a renewable electricity system model based on the principle that long-term climate change mitigation goals need high levels of renewable electricity penetration. The model therefore aims at exploring the characteristics of such systems and focuses on the underlying meteorological processes that strongly drive the dynamics of renewable electricity generation. Currently, the model is able to depict solar PV, wind, and hydropower production.

The model takes as input timeseries of renewable power production from solar PV, wind, and hydro power and minimizes the amount of thermal backup power production by optimizing the choice of renewable production locations, i.e. by optimizing the portfolio of renewable power production options in a linear programming approach. Additionally, other flexibility mechanisms such as storage and demand side response can be implemented in the model. Thermal power production is not modelled in great detail, although this is possible, wherever sufficient data is available.

The model was first developed for Brazil, but aims at reaching global coverage. For that reason we are using globally available climatological datasets - either from temporally and spatially highly resolved reanalysis products such as the MERRA and ECMWF interim reanalysis or from future climate change scenarios. Those products have been validated for utilization in energy system models against ground measurements for different world regions and we aim at additionally validating locations around the globe.

Data is prepared in R, using R-Studio. The optimization runs in GAMS.

Publications

Journal articles

Schmidt, J., Cancella, R., Pereira Jr., A.O., 2016. An optimal mix of solar PV, wind and hydro power for a low-carbon electricity supply in Brazil. Renewable Energy 85, 137-147.
link (gated) early working paper (free)

Schmidt, J., Cancella, R., Pereira Jr., A.O., 2016. The effect of windpower on long-term variability of combined hydro-wind resources: The case of Brazil. Renewable and Sustainable Energy Reviews 55, 131;141.
link (gated) early working paper (free)

Schmidt, J., Cancella, R., Pereira Jr., A.O., n.d. The role of wind power and solar PV in reducing risks in the Brazilian hydro-thermal power system. Energy.
link (gated)

Code

The current version of the R and GAMS-code of COPA, tailored to the case of Sweden, can be found on github: COPA on github

Datasets

6-hourly wind power production for four Brazilian states for the period 1979-2014 simulated from ECMWF-interim

This data set contains a 6-hourly production timeseries for the period 1979-2014 for wind power for one turbine (Time zone UTC, in kWh) for the Bahia (BA), Ceará (CE), Rio Grande do Norte (RN), and Rio Grande do Sul (RS). We used the ECMWF-interim reanalysis dataset to derive windspeeds and calibrated windspeed data against ground measurements. Reanalysis data points are chosen so that the simulated wind production is similar to the wind regime of wind turbines in operation in 2014/2015. We simulated power output from an Enercon E82 turbine. The exact procedure applied can be found in our paper: link (gated) early working paper (free).

Download data set

When using, please cite

Schmidt, J., Cancella, R., Pereira Jr., A.O., 2016. The effect of windpower on long-term variability of combined hydro-wind resources: The case of Brazil. Renewable and Sustainable Energy Reviews 55, 131;141.

Daily inflows into Brazilian hydropower plants for the period 1979-2014 for the four Brazilian subsystems.

I used Natural Inflow Data from ONS and the deck of PDE 2022 to generate the data. Please observe that the production data was not calibrated against real production values in the system.

Download data set

When using, please cite

Schmidt, J., Cancella, R., Pereira Jr., A.O., 2016. An optimal mix of solar PV, wind and hydro power for a low-carbon electricity supply in Brazil. Renewable Energy 85, 137-147.

Research Projects

Project Title	Duration	Institution	Partners
A renewable electricity system with high shares of intermittent production: the case of Brazil	1/2014-6/2015	Energy Planning Program, Federal University of Rio de Janeiro	-
Integrating renewable electricity systems with the biomass conversion sector: a focus on extreme meteorological events	9/2016-8/2018	Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna	Energy Engineering, Luleå University of Technology

Main model developer

Johannes Schmidt at BOKU University

Stefan Höltinger at BOKU University

Elisabeth Wetterlund at LTU

Rafael Cancella at IIASA and PPE/UFRJ

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Data & Code for Download

Master Theses, Software Tools and Data Sets

This page offers code and data sets for download. The respective tools were developed in master theses under my (co-)supervision and the corresponding thesis can also be downloaded here.

Treatment of reanalysis data for simulation of renewable power generation

R-Code for deriving wind power production from MERRA-2 wind speeds for single wind parks

The zip-file contains a list of functions useful to derive wind power production from MERRA-2 wind speeds. In particular, we provide an extensive list of functions for vertical and horizontal interpolation of wind speeds, and for bias correction. The respective background is described in two master theses (by Sebastian Mooshammer and Johann Baumgartner, who also wrote the code), listed below. There is an example script included for a wind farm in New Zealand, including real production data (for validation and bias correction) and the necessary MERRA-2 files.

Download scripts and datasets

Code Documentation (Master thesis)

Sebastian Mosshammer: Validation of MERRA-data with a wind power simulation model in comparison to real production quantities.

Johann Baumgartner: Modelling the aggregated wind power generation of windparks in comparison to measured output for two case studies in Austria and New Zealand (in German).

Download daily wind power production for Brazil from ONS

Download R-Script. Hint: the script can easily be updated to also download other daily data provided by ONS, such as hydropower or thermal power production. Instead of running the script yourself, you can also download a weekly update of the datasets below:

Daily wind power production per Region for Brazil

Download Data (Weekly Update)

Daily Wind Power Production vs. Forecast per Wind Farm for Brazil

Download Data (Weekly Update)

Optimization of placement of wind turbines in wind parks

R-Code for optimal placement of wind turbines in wind parks

Sebastian Gatscha has developed a genetic algorithm to optimize the placement of wind turbines within a wind-farm, considering wake effects. He published the R-code on Github. He additionally developed a Shiny-App which can be accessed here.

Code Documentation (Master thesis)

Sebastian Gatscha: Generic Optimization of a Wind Farm Layout using a Genetic Algorithm.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Contact

Please find my contact details here