Models & Tools

The “Dispatch and Investment Evaluation Tool with Endogenous Renewables” (DIETER) is an open-source electricity sector model. It minimizes the investment and operating costs of a wide range of technologies on both the supply and demand sides, as well as various types of energy storage technologies. Typically, every consecutive hour of the year is modeled to realistically capture the short- and long-term fluctuations of variable renewable energy sources. 

The model’s main input data includes time series on demand and the availability of renewable energy, as well as techno-economic parameters of various electricity generation, demand, and storage technologies. The model results include total costs as well as the optimal deployment and hourly utilization of various technologies. They can be interpreted as the outcome of a perfectly competitive market. 

DIETER has been developed and maintained at DIW Berlin for over ten years and has been used in numerous peer-reviewed scientific studies. In particular, it enables investigations into how the most cost-effective interaction of various power generation and flexibility technologies can be achieved in future renewable energy systems. Previous analyses have focused on the economics of energy storage and various electrification technologies. 

The model was originally developed in GAMS and later equipped with a Python wrapper for pre- and post-processing. Most recently, a pure Julia version was developed. All versions are available in a public GitLab repository. 

The Python tool emobpy can create battery electric vehicle time series. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. Four different time series can be created: vehicle mobility time series, driving electricity consumption time series, grid availability time series and grid electricity demand time series. 

The emobpy tool and a sample dataset were introduced in Gaete-Morales et al. (2021).

HydrOGEnMod is a partial equilibrium model of the future global market for green hydrogen and hydrogen derivatives, characterized by imperfect competition. The model depicts representative market actors and their interactions, specifically the availability of clean (renewable) electricity, the production of electricity-based hydrogen, the conversion into derivatives such as ammonia, as well as transportation and storage. The model has a long-term time horizon extending to 2050 and a multi-regional breakdown of the future global market. 

The model is programmed as an optimization model in Julia. The model code, including the dataset, is available as open source here. A detailed model description is published here. HydrOGEnMod is released under the MIT License, which can be viewed here. When using HydrOGEnMod, it must be cited with reference to the source (Source: https://github.com/LukasBarner/HydrOGEnMod.jl). The latest version of Julia is required to use the model, and a suitable solver may also be needed (see documentation). 

The Global Gas Model (GGM) was developed in collaboration with Ruud Egging, PhD from NTNU & SINTEF and is occasionally used jointly with him for studies. A joint model documentation was published in 2019. 

GGM is an equilibrium model of the global natural gas market (including conventional natural gas, shale gas, and LNG). All countries with significant natural gas production, consumption, and transportation are included in the dataset. The model distinguishes between the various types of actors (“players”) in the natural gas sector: producers, transporters (pipeline and LNG), and storage facilities. In the model’s equilibrium framework, their respective profit-maximization problems and specific costs are represented, linked, and solved simultaneously. 

The current version of the GGM model is programmed in Julia. The model code, along with the dataset, is available as open source here. GGM is released under the MIT License, which can be viewed here. When using the Global Gas Model, it must be cited with reference to the source (Source: https://github.com/Franziska-Holz/GGM_public). The latest version of Julia is required to use the model, and a suitable solver may also be needed. 

The CCTSMOD model was developed in collaboration with the Technical University of Berlin. CCTSMOD models the entire CCS value chain, including CO2 capture, CO2 transport, and CO2 storage (Carbon Capture, Transport & Storage). The model calculates the optimal expansion of infrastructure at all stages of the CCS value chain, including pipeline infrastructure.  

CCTSMOD is programmed in GAMS as a mixed-integer optimization problem in which system costs are minimized. Economies of scale are accounted for by considering different pipeline sizes, with larger pipeline diameters resulting in lower costs.  

COALMOD-World was developed at DIW Berlin and is used in collaboration with TU Berlin and the European University of Flensburg. COALMOD-World is a sectoral model of the international thermal coal market that is used specifically to analyze the impacts of climate policy measures. The model accounts for both domestically consumed coal and coal offered on the export market. 

It depicts the key players along the value chain: coal producers and exporters. They determine production volumes and infrastructure expansion. The model accounts for the progressive depletion of the lowest-cost coal reserves, which leads to rising extraction costs over time. Coal reserves are reduced based on the age of the coal mines. 

COALMOD-World 2.0 is programmed in GAMS. The model code, including the dataset, is available as open source here. COALMOD-World is published under the MIT License, which can be viewed here. A GAMS license with the PATH solver is required to use the model.