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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]

The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection. • Probabilistic feature selection approach for improved asset lifetime estimation. • Integration of environmental features for improved renewable-operated asset lifetime. • Systematic and robust feature weighting methodology. • Improved transformer lifetime estimation including sensor and environmental data. • Validated on two real photovoltaic power plant case studies. [ABSTRACT FROM AUTHOR]

• Evaluation of the most recent Spanish grid code requirements. • A new tool to speed up the commissioning of new solar PV power plants is presented. • The accuracy of the aggregated PV power plant dynamic model proposed is demonstrated. • The aggregated model is shown to be ten times faster than the detailed equivalent. • The aggregated solar PV power plant simulation model approved by the Spanish TSO. Renewable energy and, in particular, solar PV power, will play a crucial role in achieving net-zero emissions scenarios over the coming decades. Increasing the share of renewables in countries' energy mixes is therefore a major challenge that must be urgently addressed. Within this context, the present work proposes an innovative tool that may accelerate the commissioning process of new solar PV power plants, in the form of an aggregated simulation model to assess a power plant's responses under the technical requirements established in the Spanish grid code. Our analyses, based on the comparison of the proposed model with a detailed model -both representing the same installation, show, from a quantitative and qualitative viewpoint, the accuracy and usefulness of the dynamic simulation model proposed. The aggregated model is shown to be ten times faster than the detailed equivalent, while the errors encountered in the simulation responses of both models are below 0.1% in all cases. It must also be noted that the present paper has allowed for the introduction, in the most recent version of the Spanish grid code and following approval of the Spanish Transmission System Operator (TSO), Red Eléctrica de España (REE), of this new and advantageous PV power plant model. Finally, it is worth mentioning that this study could form the basis for encouraging other countries to also adopt this model in their grid codes as a new tool to help integrate PV power into power grids. [Display omitted] [ABSTRACT FROM AUTHOR]