Documentos de Trabajo
http://hdl.handle.net/11531/4153
WorkingPaper, ponencias invitadas y contribuciones en congresos no publicadas2024-03-29T07:56:42ZA comparative analysis of Cournot equilibrium and cost minimization models for electricity and hydrogen integration
http://hdl.handle.net/11531/87718
A comparative analysis of Cournot equilibrium and cost minimization models for electricity and hydrogen integration
Herrero Rozas, Luis Alberto; Campos Fernández, Francisco Alberto; Villar Collado, José
; The relationship between hydrogen and electricity has gained attention due to their interconnected roles in the energy transition. Existing joint electricity and hydrogen market models often overlook the dependence between electricity and hydrogen prices, where electrolyzer production influences electricity prices. Conversely, the price of electricity significantly impacts the costs of hydrogen production. Considering this price-based interdependency, this study compares a Cournot equilibrium and a perfect competition market model. These models are transformed into a new quadratic optimization problem to facilitate resolution. The analysis highlights the potential of the Iberian region for hydrogen production. Moreover, it can be seen how renewable energy is prioritized for electricity production under perfect competition and the impact it has on the hydrogen market compared to the Cournot scenario.
Solar photovoltaic energy scenarios generation: a novel methodology for multi-area electricity markets
http://hdl.handle.net/11531/87645
Solar photovoltaic energy scenarios generation: a novel methodology for multi-area electricity markets
Benito Adrados, Diego; Marulanda García, Geovanny Alberto; Cifuentes Quintero, Jenny Alexandra; Bello Morales, Antonio; Reneses Guillén, Javier
; In this paper, a novel methodology is introduced for the generation of solar photovoltaic energy scenarios within multi-area electricity markets. Diverging from the existing short-term focus in the literature, the proposed approach tackles the challenge of creating realistic long-term scenarios for solar energy, taking into account the inherent variability of solar availability and the correlation between different areas. A distinctive feature of this methodology is the segmentation of historical data on a monthly basis and the application of unique Seasonal Auto Regressive Integrated Moving Average (SARIMA) models for each month and area. The accuracy of scenario generation is further enhanced by the production of hourly multivariate residuals. These residuals, being correlated and conforming to normal distribution functions, integrate spatial correlations derived from covariance matrices of monthly historical data series. Then, Monte Carlo simulations are employed, effectively merging the temporal dependencies captured by the SARIMA models with the spatial dependencies gleaned from historical data covariances. This integrated process results in more comprehensive and reliable solar energy scenarios. The focus of the study, encompassing Spain, Portugal, and France, involves an evaluation of the scenario quality over a two-year period. Main results indicate that the inclusion of a monthly-level dependencies modeling significantly enhances the quality of solar power scenarios, thereby improving their applicability across various temporal scales.
De-rating factors in regional capacity mechanisms with cross-border participation: the role of asymmetric national non-served energy values
http://hdl.handle.net/11531/87616
De-rating factors in regional capacity mechanisms with cross-border participation: the role of asymmetric national non-served energy values
Marulanda García, Geovanny Alberto; Bello Morales, Antonio; Rodilla Rodríguez, Pablo; Mastropietro, Paolo; Reneses Guillén, Javier
; Capacity mechanisms (CMs) are regulatory frameworks often implemented to address market failures and ensure the long-term reliability of electricity supply. In the context of regional markets, these mechanisms can maximize overall efficiency if they allow for the participation of cross-border generators. When implementing national CMs, it is common to establish a methodology to determine the de-rating factors of the different resources participating in the scheme which represent the effective contribution of generation resources in meeting adequacy requirements. If the objective is to allow the participation of cross-border resources in the national CM, it is also necessary to compute these de-rating factors to the generators located in the neighboring system. The literature on this topic is scarce and this is the gap to which this paper aims to contribute. This paper proposes a methodology to compute de-rating factors to the multi-area case. In addition, we explore how the non-served energy cost established by the countries sharing the common border can influence the results.
Explaining the solutions of the unit commitment with interpretable machine learning
http://hdl.handle.net/11531/87615
Explaining the solutions of the unit commitment with interpretable machine learning
Lumbreras Sancho, Sara; Tejada Arango, Diego Alejandro; Elechiguerra Batlle, Daniel
; The energy transition needs mathematical models to address the complexity of shifting towards sustainable energy sources. In addition to providing accurate solutions, these models must be explainable and available for discussion among stakeholders to facilitate informed decision-making and ensure a successful transition. This paper contributes to the explainability of power systems models by applying interpretable machine learning techniques to improve understanding of the solutions to the unit commitment problem. It applies them to a case study based on the IEEE 118N system. The developed methodology aims at describing the optimal commitment solutions as a function of the conditions of the system in a compact manner that is understandable by a human being. This type of information takes the form of 'which plants are needed under which conditions' and is routinely learned by experience by system operators and other agents participating in the system. This experiential knowledge is realized in an approximate form that is simple enough to help make or justify decisions. By applying interpretable machine learning techniques, our methodology can automatically extract what was previously only available through human experience and reflection. Our approach involves model trees and node clustering to find a concise description of the different situations where the system can be found. Our results show that the methodology can explain these modes of operation for the 118N system in a sufficiently simple manner to be understood by a human unfamiliar with the system. This shows that interpretable machine learning can provide valuable insights into real solutions of the unit commitment and help improve decision-making in this area.