Behrang Shirizadeh, Philippe Quirion
The EOLES family of models optimizes the investment and operation of an energy system in order to minimize the total cost while satisfying energy demand. EOLES_elec is the electricity version of this family of models. It minimizes the annualized power generation and storage costs, including the cost of connection to the grid. It includes eight power generation technologies: offshore and onshore wind power, solar photovoltaics (PV), runof-river and lake-generated hydro-electricity, nuclear power (EPR, i.e. third generation European pressurized water reactors), open-cycle gas turbines and combined-cycle gas turbines equipped with post-combustion carbon capture and storage. The latter two generation technologies burn methane which can come from three sources: fossil natural gas, biogas from anaerobic digestion and renewable gas from power-to-gas technology (methanation). EOLES_elec also includes four energy storage technologies: pumpedhydro storage (PHS), Li-Ion batteries and two types of methanation.
The main simplification assumptions in the EOLES_elec model are as follows; it considers continental France as a single node, demand is inelastic, and the optimization is based on full information about the weather and electricity demand. This model uses only linear optimization: non-linear constraints might improve accuracy, especially when studying unit commitment, however they entail significant increase in computation time. Palmintier (2014) has shown that linear programming provides an interesting trade-off, with little impact on cost, CO2 emissions and investment estimations, but speeds up processing by up to 1,500 times. The model is written in GAMS and solved using the CPLEX solver. The code and data are available on Github.
Shirizadeh B., Quirion P., EOLES_elec model description, CIRED Working Papers, 2020-79