Universidad Pontificia Comillas. Madrid (España)
13 de marzo de 2025
Resumen:
The European Union's (EU) commitment to achieve climate neutrality by 2050 and reduce emissions by 55% by 2030 has intensified efforts towards a low-carbon energy transition. A key pillar of this transformation is the integration of Renewable Energy Sources (RES) into the electricity grid, which poses significant challenges in system reliability and market integration. In response, the EU has focused its efforts on developing collaborative and regulatory frameworks that facilitate cross-border participation to support renewable energy production while ensuring the security of electricity supply. This PhD thesis addresses these challenges, concentrating on two key areas: the development of Capacity Remuneration Mechanisms (CRMs) to protect electricity supply and cooperative mechanisms to foster cross-border collaboration and meet renewable energy targets within the EU.
Addressing these challenges requires, on the one hand, an accurate representation of future renewable energy scenarios, with special emphasis on modeling spatio-temporal dependencies within regional electricity markets at high granularity. Achieving this level of accuracy can be very demanding in terms of computational resources, mainly when applied to large-scale planning problems. The uncertainty modeling employed in traditional approaches contributes significantly to this computational burden. Therefore, it is crucial to model uncertainty conveniently to maintain high accuracy in the representation of electricity markets while maintaining an appropriate balance between accuracy and computational efficiency.
Concerning the above, this thesis identifies important gaps in the specialized literature. First, there is a lack of methodologies focused on accurately modeling RES dependence relationships in regional electricity markets. Second, traditional long-term planning models typically simulate fixed values for RES production despite the inherent reduction in forecast accuracy over long time horizons. In this context, there is a notable lack of development of solutions that can include RES uncertainty in long-term planning models in a computationally efficient way. Finally, there is a lack of methodologies to assess cross-border resource contributions to CRMs considering their equivalence with the contributions made by local resources, and to determine the optimal percentages in statistical transfers under cooperation mechanisms. These methodological gaps limit transparency, competitiveness, and participation in cross-border energy markets.
This thesis focuses on developing computational and regulatory tools for modeling realistic future scenarios with high renewable energy penetration in the context of regional electricity markets. To address these issues, this research proposes new methodologies in four key areas: 1) the development of probabilistic and regionally integrated timetable scenarios for RES, 2) an efficient approach to reduce the computational burden of large-scale, long-term generation planning models under uncertainty, 3) a framework for assessing cross-border participation in RCMs and the equivalence between foreign and local capacity resources, and 4) a methodology for allocating costs and benefits in statistical transfers in a way that accurately reflects the contributions and needs of all parties.
The main contributions of this thesis can be summarized as follows. The probabilistic approach to generate long-term RES scenarios proposed in this thesis significantly improves the modeling of spatio-temporal dependencies within regional electricity markets, resulting in more accurate and realistic scenarios. Furthermore, the proposed approach for integrating uncertainty into generation planning models in large models has demonstrated increased computational efficiency while providing a more comprehensive framework for incorporating short-term decision-making within long-term planning horizons. The results further highlight the importance of scarcity energy allocation rules for accurately estimating the contribution of foreign resources in RCMs. Finally, the proposed methodology for allocating statistical transfers demonstrates that, although achieving an optimal allocation within a cooperative mechanism is not always feasible, it is possible to identify mutually beneficial solutions that provide preferable outcomes compared to non-participation in the mechanism.
Resumen divulgativo:
Esta tesis mejora el modelado de renovables, mecanismos de capacidad y cooperación transfronteriza. Para esto, propone nuevos métodos para modelar la incertidumbre, la adecuación de recursos y las transferencias estadísticas, optimizando precisión, eficiencia y distribución equitativa de recursos.
Descriptores: Energía, Generación de Energía, Regulación Gubernamental del Sector Privado, Series Temporales
Palabras clave: Capacity Remuneration Mechanisms (CRMs), Cooperation Mechanisms, Cross-Border Mechanisms, De-Rating Factors, Electricity Market Integration, Electricity Supply Security, Energy Policy, Energy Regulation, Firm Supply Assessment, Generation Planning, Imbalance Risk Modeling, Joint Projects, Multi-Area Electricity Market, Power System Planning, Regional Electricity Markets, Renewable Energy Financing Mechanism (REFM), Renewable Energy Forecasting, Reserve Management, Resource Adequacy, Statistical Transfers, Temporal Aggregation in Energy Models, Uncertainty Modeling
Cita:
G. Marulanda (2025), New Methods for Modeling Renewable Energy Uncertainty and Cross-Border Policy Mechanisms in Electricity Markets. Madrid (España).
