iberian-day-ahead-market-simulator 0.1.0

Iberian day-ahead electricity market clearing simulator

iberian-day-ahead-market-simulator

A Python simulator for the MIBEL (Mercado Ibérico de Electricidad) Iberian day-ahead electricity market clearing process.

The package replicates the OMIE/MIBEL market-clearing algorithm, including:

• Simple and complex bid orders
• Paradoxical order inclusion and removal
• Spain–Portugal interconnection capacity constraints
• France exchange integration

Requirements

Solver support

The simulator uses Pyomo SolverFactory, so you can choose the solver with solver_factory_type.

• Recommended/default: Gurobi (solver_factory_type="gurobi")
• Also possible: HiGHS (solver_factory_type="highs"), or any solver plugin available in your Pyomo environment

Note: model performance and feasibility behavior can vary by solver. Gurobi is the most tested option in this project.

If you use Gurobi, install gurobipy and configure your licence:

pip install gurobipy

If you use HiGHS, install highspy:

pip install highspy

If you use other solvers, install the corresponding solver binaries in your system and use the appropriate solver_factory_type.

Installation

pip install iberian-day-ahead-market-simulator

Or install from source:

git clone https://github.com/EloyID/iberian-day-ahead-market-simulator.git
cd iberian-day-ahead-market-simulator
pip install -e .

Quick Start

from iberian_day_ahead_market_simulator.clearing_process import run_iberian_day_ahead_market_simulator
from iberian_day_ahead_market_simulator.plot_helpers import (
    plot_clearing_prices,
    plot_spain_portugal_transmissions,
)

# These files are available in OMIE
det_date = "path/to/det_file.1"
cab_date = "path/to/cab_file.1"
capacidad_inter_date = "path/to/capacidad_inter_file.1"

# Data available in ENTSO-E Transparency Platform
# The df must have date_sesion, int_periodo and 
# float_price_fr columns
price_france_date = "path/to/price_france_file.parquet"

results = run_iberian_day_ahead_market_simulator(
    det_date=det_date,
    cab_date=cab_date,
    capacidad_inter_date=capacidad_inter_date,
    price_france_date=price_france_date,
    n_jobs=10, # Number of parallel processes for scenario analysis
    solver_factory_type="gurobi", # "gurobi" or "highs"
)

# Now you can analyze the results, for example:
plot_clearing_prices(results["clearing_prices"])
plot_spain_portugal_transmissions(
    results["spain_portugal_transmissions"]
)

Usage examples

Some examples can be found in the examples/ directory:

• application_examples.ipynb: Example of how to run the simulator and analyze results
• paper_replication.ipynb: Replication of the results from the paper "iberian-day-ahead-market-simulator: A Python Package to Simulate the MIBEL Day-Ahead Market Clearing"
• download_omie_data.ipynb: Example of how to download OMIE data files

Data sources

OMIE files used by this project are:

• Cabecera de las ofertas (Header of bids for Day-ahead Market, CAB files). Contains one entry per bidding unit (e.g., unit identifier, buy/sell flag, and fixed-cost parameter)
• Detalle de las ofertas (Day-ahead market bid details, DET files). Contains the detailed bid curves and non-convex information, with one entry per bid element (period, price, quantity, block identifier, exclusive-group identifier, and additional SCO parameters when applicable).
• Capacidad y ocupación de las interconexiones tras la casación del mercado diario (Capacity and occupation of the interconnectors after Day-ahead matching process, capacidad_inter files). Contains the information about the interconnection flows and available capacities.

French day-ahead prices can be obtained from the ENTSO-E Transparency Platform.

Project Structure

src/iberian_day_ahead_market_simulator/
├── clearing_process.py        # Main iterative clearing loop
├── make_model.py              # Pyomo MILP model builder
├── run_model.py               # Pyomo solver wrapper (configurable solver)
├── data_preprocessor.py       # Input data processing
├── parse_omie_files.py        # OMIE flat-file parsers
├── paradoxal_orders_tools.py  # Paradoxical order utilities
├── residual_demand_curve.py   # RDC computation & interpolation
├── schemas/                   # Pandera data-validation schemas
└── data/                      # Bundled reference data

Data Documentation

For a complete data dictionary of inputs, transformed datasets, outputs, enums, and validation rules, see docs/data_book.md.

Licence

MIT