スペインの日前電力市場における太陽光発電の市場価値侵食（2016–2025年）の再現データとコード

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:PV市場価値侵食の計量手法と再現可能なコード・データをそのまま流用できる。

🏢実務担当者:再エネ電源の市場価格リスクとキャプチャ比率の評価枠組みを投資判断に応用できる。

🏛政策担当者:再エネ大量導入下での市場価値低下と制度設計（FIP・容量市場等）の論点を把握できる。

This record contains the replication dataset, Python scripts, figures, tables, and supporting diagnostics associated with the manuscript entitled "Photovoltaic market-value erosion in Spain’s day-ahead electricity market: Evidence from captured prices and penetration-based operating states". The study examines photovoltaic (PV) market-value erosion in the Spanish peninsular day-ahead electricity market over 2016–2025. The analysis focuses on technology-specific captured prices, capture ratios, price spreads relative to the all-hours pool benchmark, and operating states defined by PV penetration. The replication package includes: A consolidated hourly dataset constructed from electricity market and power-system series obtained from Red Eléctrica de España's ESIOS platform. Seven Python scripts (00–06) for data preparation, descriptive figures, captured-price calculations, PV operating-state analysis, regression tables, and independent regression diagnostics. The numerical outputs underlying Tables 1–3 and Figures 2–5 of the main manuscript, together with Tables S1–S2 and Figures S1–S7 of the Supplementary Material. Documentation describing the input data, processing conventions, computational environment, and execution sequence. The analysis defines 20 global PV-generation-weighted penetration bins, grouped into four operating states: Low, Medium, High, and Very High. Daily regression specifications include renewable penetration measures, electricity demand, calendar controls, and event indicators, with HAC/Newey–West standard errors. A robustness exercise excludes the months with the highest daily-price volatility. The main README provides instructions for reproducing the results using Python 3.11.9. Figure 1 of the manuscript is an editable methodological diagram prepared separately and is not generated by the Python scripts. Data source: Red Eléctrica de España (REE), ESIOS platform. The original data provider should be acknowledged when reusing the underlying information. Access: The files are subject to a temporary embargo associated with the manuscript's editorial review and publication process. Record metadata remain publicly accessible during the embargo.

• Zenodo https://zenodo.org/records/24032765first seen 2026-11-15 04:15:26