Releases

How to cite

In the academic spirit of collaboration, the source code should be appropriately acknowledged in the resulting scientific disseminations. You may cite it as follows:

  • [1], for general reference to the EnergyScope project and the EnergyScope modeling framework 2

  • [2], for reference to the origins of the EnergyScope project or to the first online version of the calculator energyscope.ch 3

  • [3], for reference to the energyscope MILP modeling framework 4

  • [4], for reference to the current code 1

[1] G. Limpens, S . Moret, H. Jeanmart, F. Maréchal (2019). EnergyScope TD: a novel open-source model for regional energy systems and its application to the case of Switzerland. https://doi.org/10.1016/j.apenergy.2019.113729

[2] V. Codina Gironès, S. Moret, F. Maréchal, D. Favrat (2015). Strategic energy planning for large-scale energy systems: A modelling framework to aid decision-making. Energy, 90(PA1), 173–186. https://doi.org/10.1016/j.energy.2015.06.008

[3] S. Moret, M. Bierlaire, F. Maréchal (2016). Strategic Energy Planning under Uncertainty: a Mixed-Integer Linear Programming Modeling Framework for Large-Scale Energy Systems. https://doi.org/10.1016/B978-0-444-63428-3.50321-0

[4] G. Limpens (2021). Generating energy transition pathways: application to Belgium. PhD thesis Université Catholique de Louvain. http://hdl.handle.net/2078.1/249196

You are welcome to report any bugs related to the code to the following: moret.stefano@gmail.com or gauthierLimpens@gmail.com

Or by submitting an issue on the github repository.

License

Copyright (C) <2018-2021> <Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland and Université catholique de Louvain (UCLouvain), Belgium>

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License

Codes versions

Authors:

  • Stefano Moret, Ecole Polytechnique Fédérale de Lausanne (Switzerland), <moret.stefano@gmail.com>

  • Gauthier Limpens, Université catholique de Louvain (Belgium), <gauthierLimpens@gmail.com>

  • Paolo Thiran, Université catholique de Louvain (Belgium)

  • Xavier Rixhon, Université catholique de Louvain (Belgium)

Model extensions

The EnergyScope TD models account for three published model extension:

  • Pathway optimisation: EnergyScope Pathway enables to optimise the energy system from an existing year (usually 2015) to a future target year. The model has been extended to a big linear model which represents 8 representative years from 2015 to 2050. The overall transition is optimised at once with a perfect foresight on technology performances (prices, efficiency,…), resources prices, energy demand …

    Main contributors: Gauthier Limpens, see 1

  • Multi-regional; EnergyScope Multi-Cells allows the representation of several region at once. The regions are resolved simultaneously with the exchanges of several energy carriers (usually electricity, molecules and wood). The new model has been first developped by Thiran et al.7 on a fictive case, then extended to the Western Europe region, see Cornet et al.8. It has also been applied in other studies on different regions 9,10. A European version is currently under development.

    Main contributors: Paolo Thiran, see 7,8,9,10 Other contributors: Aurélia Hernandez, Noé Cornet, Pauline Eloy, Jeroen Dommisse, Jean-Louis Tychon.

Applications

The model has been used for:

  • Uncertainty quantification:

    • Robust optimisation design: Moret developed a framework to integrate uncertainties in energy models. The framework accounts for uncertainty characterisation, sensitivity analysis and robust optimisation.

    Main contributors: Stefano Moret, see 5.

    • Global sensitivity analysis (GSA): this allow to identify the critical parameters for the energy transition. As an example, Moret5

      quantifies how the price of fossil ressources drive the uncertainty. This result was verified for the case of Belgium and compared to other decision, such as phasing out Nuclear (see studies of Limpens1 and Rixhon et al.11).

    Main contributors: Gauthier Limpens, Xavier Rixhon and Diederik Coppiters, see 1,5,11,12.

  • Scenario analysis of the transition: the model has been applied to study different scenarios of transition for the Swiss (see 2,13) and the Belgian case (see 1,6). The analysis enable to quantify the role of storage technologies (i.e. electricity, heat and molecule storage), identify the key technologies of the transition or even estimate the cost for each transition option.

    Main contributors: Gauthier Limpens and Stefano Moret, see 1,2,6,13.

  • Coupling with a dispatch model; EnergyScope-DispaSET soft couples the two models EnergyScope and Dispa-SET. EnergyScope is a Energy System Optimisation (ESO) model while Dispa-SET is a Unit Commitment and Economic Dispatch (UCED) model. Pavičević et al.14 proposed a soft linking methods based on the preliminary works of Coates et al.15 The ESO model optimises the design (with limited representation of the operation) while the UCED model optimises the dispatch (i.e. operation). An iterative loop has been implemented in python. In a first step, the ESO model feeds the design to the UCED model. In a second step, the UCED model changes constraints in the ESO model by modifying the values of some parameters. Pavičević et al.14 presents the additional constraints needed to couple the ESO and UCED models.

    Main contributors: Pavicevic Matija, Thiran Paolo and Gauthier Limpens, see 14,15

  • Coupling with an economic model; EnergyScope-GEMMES couples the models EnergyScope and GEMMES. (This work is undergoing and no peer-reviewed publication is available yet.) GEMMES (General Monetary and Multisectoral Macrodynamics for the Ecological Shift) is a macro-economic tool that estimates the impact of public decisions on the real and financial spheres of an open emerging economy with an open capital account and a flexible exchange rate. Coupling the two models will allow us to anticipate the macro-economic consequences of the energy transition and to revise the transition plans accordingly. Namely, the energy transition will have a major impact on the balance of payments of the country, which will be positive or negative depending on whether this country is currently a net importer or exporter of fossil fuels. EnergyScope-GEMMES will be applied to both types of countries.

    Main contributors: Pierre Jacques, see 16

Case studies

The model has been applied to the following countries:

  • Switzerland:

    • Uncertainty: Moret5

    • Scenario analysis and storage needs: see for the main study Limpens et al.2 and Limpens et al.13 for a specific study on the storage.

  • Belgium:

    • Scenarios analysis: see Limpens et al.6 who analysed different scenarios to reduce greenhouse gases emissions.

    • Uncertainty: see Limpens et al.12 for the elaboration of the methodology to the Belgium case (using a novel methodology), see Rixhon et al.11 for a specific study on electro-fuels and see Limpens1 for an updated study on the Belgian case.

    • Pathways analysis: see Limpens1 who investigate several pathways including an uncertainty study of critical parameters.

  • Italy:

    • Scenarios analysis: see Borasio and Moret17 for an exhaustive analysis (per regions and with uncertainty) to reduce the energy system at the horizon of 2050.

    • Multi-region analysis: see Thiran et al.9 for an application of the Multi-cell model to a three region case.

  • Spain:

    • Scenario analysis: see Rosello Martinez18 for different scenarios of transition in Spain.

  • Other countries:

    • European Union countries see Dommisse et al.19 for a data collection and results for 26 european countries.

    • Ugandan growth see Limpens et al.20 who illustrates the energy system of Uganda in 2019 and investigate different growth based on fossil or renewable energies.

Current developments:

  • Pathway: Myopic optimisation

  • Multi-cells: work on the selection of typical days and application to a larger region.

  • Multi-criteria: Use of additional criteria (Global warming potential, energy embodied, …), see Muyldermans et al.21

  • Coupling with other models: dispatch model (Dispa-SET) and economic model (GEMMES).

  • (And also works from Stefano and EPFL)