DIW Berlin researchers

Applications of the DIETER model have been published in a growing number of peer-reviewed articles. Many of these applications were co-authored by DIW Berlin researchers, as the model was initially developed at DIW.

Two companion articles introduce the basic model version, which we now refer to as DIETERgms 1.0.2, and investigate optimal electrical storage capacity in long-run scenarios with very high shares of renewable energy sources:

  • Zerrahn & Schill (2017): Long-run power storage requirements for high shares of renewables: review and a new model, Renewable and Sustainable Energy Reviews, 79, 1518-1534. Paper

  • Schill & Zerrahn (2018): Long-run power storage requirements for high shares of renewables: Results and sensitivities, Renewable and Sustainable Energy Reviews, 83, 156-171. Paper

Another article describes how the GAMS-based core of the model was wrapped in a Python environment, giving rise to DIETERpy:

  • Gaete-Morales, Kittel, Roth, Schill (2021): DIETERpy: a Python framework for The Dispatch and Investment Evaluation Tool with Endogenous Renewables. SoftwareX, 15, 100784. Paper

Reduced model versions have been used for more general reflections on the economics of electrical storage, its changing role in settings with increasing renewable penetration, and the phenomenon of unintended storage cycling:

  • Zerrahn, Schill, & Kemfert (2018): On the economics of electrical storage for variable renewable energy sources. European Economic Review 108, 259-279. Paper; model code available on Zenodo.

  • Schill (2020): Electricity storage and the renewable energy transition. Joule 4, 1-6. Paper; model code available on Zenodo.

  • López Prol & Schill (2021): The Economics of Variable Renewables and Electricity Storage. Annual Review of Resource Economics, 13(1), 443-467. Paper; model code available on Zenodo.

  • Kittel & Schill (2022): Renewable Energy Targets and Unintended Storage Cycling: Implications for Energy Modeling. iScience, 25(4), 104002. Paper; model code available here.

Other model applications have explored power sector effects of solar prosumage in Germany and Western Australia:

  • Schill, Zerrahn & Kunz (2017): Prosumage of solar electricity: pros, cons, and the system perspective. Economics of Energy and Environmental Policy 6(1), 7-31. Paper; model version DIETERgms 1.2.0.

  • Say, Schill & John (2020): Degrees of displacement: The impact of household PV battery prosumage on utility generation and storage. Applied Energy 276, 115466. Paper; model code available on Zenodo.

To analyze the effects of tariff design on prosumage decisions, a model version with a mixed complementarity problem (MCP) instead of a linear optimization problem was developed and applied to Germany:

  • Günther, Schill, & Zerrahn (2021): Prosumage of solar electricity: tariff design, capacity investments, and power sector effects. Energy Policy 152, 112168. Paper; model code available on Zenodo.

Another model application explored the power sector impacts of electric vehicles in Germany, focussing on reserve provision and vehicle-to-grid:

  • Schill, Niemeyer, Zerrahn & Diekmann (2016): Bereitstellung von Regelleistung durch Elektrofahrzeuge: Modellrechnungen für Deutschland im Jahr 2035. Zeitschrift für Energiewirtschaft 40 (2), 73-87. Paper; model version DIETERgms 1.1.0.

Another paper introduced a module for residential power-to-heat options and an application focussing on the flexibilization of legacy night-time storage heaters, using model version DIETERgms 1.3.0:

  • Schill & Zerrahn (2020): Flexible electricity use for heating in markets with renewable energy. Applied Energy 266, 114571. Paper

A hydrogen module was introduced and applied to explore the trade-off between energy efficiency and temporal flexibility of various green hydrogen supply chains:

  • Stöckl, Schill & Zerrahn (2021): Optimal supply chains and power sector benefits of green hydrogen. Scientific Reports 11, 14191. Paper; model code available on Zenodo.

A reduced model version has also been used to generate data points for estimating the macroeconomic elasticity of substitution between “clean” and “dirty” electricity generation for high shares of renewables not yet observable in empirical data:

  • Stöckl & Zerrahn (2020): Substituting Clean for Dirty Energy: A Bottom-Up Analysis. DIW Discussion Paper. Paper; model code available on Zenodo.

The model has also been used in the FlexMex model comparison exercise, which has led to three publications:

  • Gils, Gardian, Kittel, Schill, Zerrahn, Murmann, Launer, Fehler, Gaumnitz, van Ouwerkerk, Bußar, Mikurda, Torralba-Díaz, Janßen, Krüger (2022): Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases. Renewable and Sustainable Energy Reviews. 158, 111995. Paper

  • van Ouwerkerk, Gils, Gardian, Kittel, Schill, Zerrahn, Murmann, Launer, Torralba-Díaz, Bußar (2022): Impacts of power sector model features on optimal capacity expansion: A comparative study. Renewable and Sustainable Energy Reviews, 157, 112004. Paper

  • Gils, Gardian, Kittel, Schill, Murmann, Launer, Gaumnitz, van Ouwerkerk, Mikurda, Torralba-Díaz (2022): Model-related outcome differences in power system models with sector coupling—Quantification and drivers. Renewable and Sustainable Energy Reviews, 159, 112177. Paper

Work in progress (selected):

  • A contribution to an open-source model comparison on the effects of cheaper stationary batteries, using the model version DIETERgms 1.3.1

  • An evaluation of the power sector implications of different types of low-carbon freight traffic

  • A detailed analysis of the trade-off between additional demand and additional flexibility potential related to battery-electric vehicles

  • An application to Europe to analyse how electricity storage needs are affected by geographical balancing