23 de noviembre de 2023
Resumen:
Electricity market liberalization and the increased penetration of renewable energy sources are transforming power systems worldwide. These changes introduce new challenges for analyzing market performance and incentives.
In this context, market equilibrium models become a very valuable forecasting tool for the electricity market. These models are particularly suited for the medium-term planning of electricity markets due to their incorporation of the competitive interactions between suppliers, which influence prices, dispatch, and other outcomes. In liberalized sectors, this competition and the resulting equilibrium are crucial market drivers.
Market equilibrium models are also a great solution for high-renewable energy systems, as they allow modeling of the variability and uncertainty of renewable energy as well as the role of different flexibility options to integrate it. This assists in quantifying the impacts of renewable growth and evaluating support options. Overall, they provide a platform for the systematic evaluation of policy changes, technological trends, and other market drivers.
This thesis proposes a novel methodology to model the medium-term operations of large-scale integrated electricity systems with increasing penetration of renewable energy and storage technologies. The work is presented in three articles that collectively address the computational challenges of such system-level modeling.
The first article proposes an original method for modeling medium-term market equilibrium in multi-area electricity systems, considering multiple market splitting possibilities and conjectured-price responses. This method reduces the possible network configurations, ensuring computational tractability.
The second article addresses the challenge of temporal aggregation in interconnected energy systems by proposing a new methodology for multi-area energy system models. By applying a multi-dimensional clustering algorithm, the original hourly data is transformed into system states, significantly reducing the computational burden while maintaining an accurate representation of system variability.
Finally, the third article presents a novel methodology for reducing the temporal dimension of medium-term operation models in real-size power systems with significant renewable generation and storage systems. The proposed two-stage clustering algorithm transforms the input parameters' temporal structure into different levels of time aggregation, enabling computational tractability and capturing the short- and medium-term variability present in power systems.
Together, these studies contribute to the development of more efficient and accurate modeling techniques for interconnected electricity systems with high renewable and storage penetrations. The dissertation demonstrates these methodologies through case studies of European electricity systems and shows their benefits over conventional modeling approaches.
Overall, this work makes a novel contribution by developing advanced techniques to overcome the computational challenges of medium-term power system modeling that can prove valuable insights for decision-makers during the ongoing energy transition.
Resumen divulgativo:
Esta tesis propone técnicas avanzadas para solventar los retos computacionales del modelado de sistemas eléctricos multi-area en el medio plazo. Estos métodos permiten una representación precisa de la variabilidad del sistema, lo que supone una valiosa aportación durante la actual transición energética.
Descriptores: Teoría de Juegos, Programación Entera, Programación Lineal, Análisis de Datos, Generación de Energía, Modelos Econométricos, Estructura del Mercado, Energía
Palabras clave: market equilibrium; multi-area system; optimization models; power system modeling; temporal aggregation; energy storage systems
Cita:
A. Orgaz (2023), Multi-area electricity market modeling using intelligent data techniques and an advanced temporal framework. Madrid (España).
