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Decreasing greenhouse gas emissions plays a crucial role in the Europe energy transition and the production of hydrogen using electricity from renewable sources (green H 2) contributes significantly to this objective. As the renewable energy production capacity grows, the impact of green H 2 is likely to increase. In the case of the Iberian Electricity Market (MIBEL), no quantitative studies have so far tested the viability of the H 2 growth plans. This paper fills this gap through a novel mathematical programming model, which integrates H2 generation into the MIBEL. The model reveals a mismatch between the sustainability goals and the expansion plans of renewable and green H 2 for Spain and Portugal. [Display omitted] • Mathematical Programming to analyse green hydrogen production in the MIBEL. • Influence of the electricity market on hydrogen production and prices. • Generating green hydrogen is preferable to hydro pumping. • Green hydrogen integration might entail additional emissions. • MIBEL hydrogen strategy must be revised to achieve sustainability. [ABSTRACT FROM AUTHOR]

Wind power is one of the most important renewable energy sources worldwide. However, integrating these technologies in electricity markets faces challenges due to the intermittent wind speed, resulting in potential imbalances within power grids and subsequent inefficiencies. Traditional approaches for modeling the uncertainty simulate fixed wind power values in operational and planning models, which work well for the short term but are inadequate for medium-term decision-making due to lower forecasting accuracy and the inability to differentiate the imbalance risk faced in each simulated value. This paper proposes a novel methodology for incorporating wind energy imbalance risk into medium-term generation planning models. For this, we first simulate realistic wind power scenarios and perform a fitting distribution process to characterize the hourly wind power behavior. Then, we model the imbalance risk using quadratic-uncertainty cost functions for each simulated value. Subsequently, we conduct a temporal aggregation to reduce the computational burden, preserving the conceptual framework of the imbalance risk modeling. Finally, we propose a medium-term generation planning model that includes the imbalance risk model and the temporal aggregation strategy. Our approach is evaluated in a real-size case study formed by Spain, Portugal, and France's electricity markets, demonstrating accurate wind power imbalance risk modeling and lowered planning costs. • A new methodology to include wind energy imbalance risk in generation planning models. • Introducing a fitting process for modeling the distribution asymmetries of wind power. • Proposing a temporal aggregation method for efficient imbalance risk modeling. • The methodology is validated in Spanish, Portuguese, and French electricity markets. [ABSTRACT FROM AUTHOR]