Future availabilty and Prices of Biomass, Electricity, natural gas and others materials in AUstria and europe based on REMIND2.1

Copyright 2024 Safdar Abbas

Introduction about pyam

pyamis a Python package that is open source and offers a range of tools and methods for evaluating and visualizing input data (such as assumptions and parametrization) and output data (model results) from integrated-assessment models, macro-energy scenarios, energy systems analysis, and sectoral studies.

Overview and scope

The REMIND model can provide valuable insights into potential changes in energy and economies in the future. This model allows researchers to explore various scenarios that depict potential outcomes for the energy-economic and environmental systems.

The IPCC's sixth annual report relies heavily on analyses generated by different macroeconomic models, which are presented in metadata format. These analyses produce large results files containing extensive data, much of which may not be directly used for analysis or presentation purposes.

Various types of studies, including integrated assessment models, energy systems analyses, macro-energy scenarios, and sectoral studies, benefit from the utilization of the open-source Python package Pyam. This package enables the analysis and visualization of these diverse studies.

Below is an illustrating how the Pyam Package can be employed to examine potential future changes in the price of biomass and other key inputs, such as electricity within the REMIND 2.1 model.

Step 1

Installation

First install the pyam package. The process of installing Pyam typically involves utilizing Python package managers such as pip , conda or Mamba. Users can easily install Pyam by running a simple command in their terminal or command prompt. However, for users who require more detailed instructions or encounter specific installation challenges, a comprehensive guide is available. Please read this installation guide This guide offers step-by-step instructions, troubleshooting tips, and additional resources to ensure a smooth installation process for users of all levels of expertise.

Step 2

Download the Prospective Background Data Model

Integrated Assessment Modeling Consortium (IAMC) has developed a simple and consistent method for sharing data tables. These tables highlight how climate change and attempts to achieve the Sustainable Development Goals may affect energy systems, land usage, and economic considerations. The Intergovernmental Panel on Climate Change (IPCC) reports provide essential examples of how this data is utilized. The IPCC's sixth annual report relies heavily on analyses generated by different macroeconomic models, which are presented in metadata and CSV format. These analyses produce large results files containing extensive data. To download this please visit this IPCC AR6 Scenario Explorer and Database hosted by IIASA The furthur details of these Data model can be found in read the documents link

Step 3

Scenario Visualization

In the next step, we import the data downloaded in step 2 into the Pyam package. Specifically, we downloaded REMIND 2.1 scenario data for the European region in our study. We thoroughly examine this data using the Pyam package, focusing on future prices of biomass, electricity, and other input variables relevant to our research. Additionally, we provide a Python-based notebook located in the "docs" folder above to visualize these scenario data and generate other visualizations using the provided source code. We include snapshots of these timeseries data and provide plots as well. The scenario terminology used here follows the pattern "R2p1," indicating the REMIND 2.1 Model, along with "SSP2-PkBudg 1100," which corresponds to what we've consistently mentioned in our study's research article. Notably, "SSP2-PkBudg 900" is equivalent to "SSP2-PkBudg 500," with the variation in terminology stemming from a prospective LCA background database scenario developed by premise. They used "REMIND-SSP2-PkBudg1150" and "REMIND-SSP2-PkBudg500," while the REMIND 2.1 model provided in the AR6 database employs "R2p1-SSP2-PkBudg1100" and "R2p1-SSP2-PkBudg900." It is important to ensure that the data file is placed in the same folder where the notebook is running. For further details on scenario visualization and step-by-step guidance on using this package, refer to the Tutorial section.

Future Biomass availabilty in AUSTRIA

we can consider LHV of wheat straw biomass which is 18 MJ per kg of wheat straw.

Future Biomass Prices in AUSTRIA

Future electricity Prices in AUSTRIA

Future Natural gas Prices in AUSTRIA

Future coal Prices in AUSTRIA

Future liquid fuel Prices in AUSTRIA

Future Hydrogen Prices in AUSTRIA

Future carbon tax in AUSTRIA

Future Biomass Prices in Europe

we consider here the future price of biomass for the year of 2030, 2040 and 2050. The snapshots of these prices are given below

Graphical representation of Prospective Biomass Prices

The following is the graphical representation of this Biomass prices.

Electricity mix in the prospective background system under various scenarios

Graphical representation of Prospective Electricity mix under REMIND-SSP2-Base for the year 2030, 2040 and 2050

Graphical representation of Prospective Electricity mix under REMIND-SSP2-Pkbudg1150 for the year 2030, 2040 and 2050

Graphical representation of Prospective Electricity mix under REMIND-SSP2-Pkbudg500 for the year 2030, 2040 and 2050

Future Electricity Prices in Europe

Graphical representation of Prospective Electricity Prices

# Future Prices of District or Industrial Heating using Natural Gas in EU

Graphical representation of Future Prices of District or Industrial Heating using Natural Gas

# Future Prices of District or Industrial Heating other than Natural Gas in EU

Graphical representation of Future Prices of District or Industrial Heating other than Natural Gas

Future Prices of lignin sold as Coal for primary energy in EU

Future Prices of lignin sold as liquid fuel for secondary energy in EU

Graphical representation of Future Prices of lignin sold as liquid fuel for secondary energy

Scientific Publications

Huppmann D, Gidden MJ, Nicholls Z et al. pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios [version 2; peer review: 3 approved]. Open Res Europe 2021, 1:74 (https://doi.org/10.12688/openreseurope.13633.2)