The EOLES family of models

The EOLES family of models


The EOLES (Energy Optimization for Low Emission Systems) family of models optimizes the investment and operation of the energy system in order to minimize its total cost (possibly including a cost of CO2 emissions) while satisfying energy demand. The first model was developed at CIRED by Behrang Shirizadeh, Quentin Perrier and Philippe Quirion.

Here are a few questions these models can address:

  • What is the optimal energy mix in the long run, taking into account CO2 emissions?
  • If a particular technology turns out to be more expensive than expected, or if it cannot be used for whatever reason, how does it impact the energy mix, the energy system cost and CO2 emissions?
  • To what extent are the energy system cost, the electricity price and the optimal energy mix sensitive to the electricity or hydrogen demand?
  • How does sector coupling, and more generally the coupling  between different energy carriers (electricity, hydrogen, methane, heat networks…) change the optimal energy mix?
  • What are the conditions needed to reach carbon-neutrality in the energy system?

Various EOLES models exist within this family, which differ mostly by the technologies included, the considered energy commodities, the geographical scope, the energy demand and whether investment and dispatch are both optimized, or the investment is fixed and only dispatch is optimized.

  1. EOLES_elecRES is a dispatch and investment optimization model for a fully renewable power sector, applied to France for 2050. It is developed in GAMS and used in the following journal articles:
    – Shirizadeh, B., Perrier, Q., & Quirion, P. (2022). How sensitive are optimal fully renewable power systems to technology cost uncertainty? The Energy Journal, 43(1).
    – de Guibert, P., Shirizadeh, B., & Quirion, P. (2020). Variable time-step: A method for improving computational tractability for energy system models with long-term storage. Energy, 213, 119024. 
  2. EOLES_elec builds on the previous model to which it adds nuclear and fossil gas with or without CO2 capture and storage, as well as biogas with CO2 capture enabling negative emissions. It is developed in GAMS and has been used in the following journal article:
    – Shirizadeh, B., & Quirion, P. (2021). Low-carbon options for the French power sector: What role for renewables, nuclear energy and carbon capture and storage?. Energy Economics, 95, 105004. 
  3. EOLES_elec_pro is an evolution of the previous model, with an updated representation of storage options, and new parameters based on the same assumptions as in RTE’s Futurs énergétiques 2050 study. It has been developed in GAMS and translated into Python.
  4. EOLES_mv is a dispatch and investment optimisation model of the whole energy system with endogenous representation of energy sources, carriers, storage options and end-use substitution among the main energy carriers. It has been used in the following journal articles:
    – Shirizadeh, B., & Quirion, P. (2022). The importance of renewable gas in achieving carbon-neutrality: Insights from an energy system optimization model. Energy, 255, 124503. 
    – Shirizadeh, B., & Quirion, P. (2022). Do multi-sector energy system optimization models need hourly temporal resolution? A case study with an investment and dispatch model applied to France. Applied Energy, 305, 117951.
  5. EOLES_elec_H2 is a dispatch and investment optimization model of the electricity and hydrogen sectors, under carbon neutrality, based on EOLES_elec_pro  with a detailed representation of hydrogen production, featuring more hydrogen production technologies. It has been used in:
    – Shirizadeh, B., & Quirion, P. (2023). Long-term optimization of the hydrogen-electricity nexus in France: Green, blue, or pink hydrogen?. Energy Policy, 181, 113702.
  6. EOLES-Dispatch is a dispatch optimization model applied to France & neighbouring countries, developed in Python. It has been used in:
    – Leblanc, C., & Lamy, L. (2021). Pitfalls of insuring renewable energy production: a case study on some wind power auctions in France. In Energy, COVID, and Climate Change, 1st IAEE Online Conference, June 7-9, 2021. International Association for Energy Economics.
Model nameGeneration technologiesStorage technologiesGeographical scopeCapacitiesYearEnergy demandCodeProgramming language
EOLES_elec_RESRenewablesPHS, batteries, methanationFranceEndogenous2050Electricityhttps://github.com/BehrangShirizadeh/EOLES_elecRESGAMS
EOLES_elec_RES_compact*RenewablesPHS, batteries, methanationFranceEndogenous2050Electricityhttps://github.com/BehrangShirizadeh/EOLES_elecRESGAMS
EOLES_elecRenewables, nuclear, fossil gasPHS, batteries, methanationFranceEndogenous2050Electricityhttps://github.com/BehrangShirizadeh/EOLES_elecGAMS
EOLES_elecPRORenewables, nuclear, fossil gasPHS, batteries, hydrogenFranceEndogenous2050Electricity, hydrogenhttps://github.com/BehrangShirizadeh/EOLES_elec_pro
GAMS & Python
EOLES_mvRenewables, nuclear, fossil gasPHS, batteries, methanationFranceEndogenous2050Electricity, heat, transportationhttps://github.com/BehrangShirizadeh/EOLES_mvGAMS
EOLES_mv_temp*Renewables, nuclear, fossil gasPHS, batteries, methanationFranceEndogenous2050Electricity, heat, transportationhttps://github.com/BehrangShirizadeh/EOLES_mv_tempGAMS
EOLES_elec_H2Renewables, nuclear, fossil gas, blue hydrogenPHS, batteries, hydrogenFranceEndogenous2050Electricity, hydrogenhttps://github.com/BehrangShirizadeh/EOLES_elec_H2GAMS
EOLES-DispatchRenewables, nuclear, fossil gas, coal, oilPHS, batteriesFrance & neighbouring countriesExogenous2015-2019ElectricityPython


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