Summary:
In the energy transition towards a carbon-free society, the continuous changes that energy systems are experiencing, the increasing penetration of renewable generation, and the incorporation of short-term storage technologies such as batteries have increased the effort to model and predict its development and operation. In this context, power system models play a relevant role. However, to be suitable for the decision-making process, these tools should not require a massive computational effort. To overcome this challenge, this paper proposes a new methodology that reduces the temporal dimension of the problem while maintaining accurate results. This methodology is specially designed for medium-term operation of real-size power systems with a significant presence of renewable generation and storage systems. By means of a two-stage clustering algorithm, the proposed approach transforms the temporal structure of the model’s input parameters into different levels of time aggregation. This arrangement makes the problem manageable and computationally tractable. In addition, together with the newly incorporated model formulation, this methodology allows capturing at the same time the short- and medium-term variability present in power systems.
Spanish layman's summary:
Este estudio presenta una metodología de clustering para reducir la dimensión temporal en modelos de planificación a largo plazo con el fin de capturar la operación a corto plazo de los sistemas de almacenamiento. El rendimiento de este enfoque se ha evaluado en un caso realista del mercado MIBEL.
English layman's summary:
This paper presents a new clustering methodology to reduce the temporal dimension in long-term power system planning models with the purpose of capturing the short-term operation of storage systems. The performance of this approach is assesed in a real-size case of the MIBEL market.
Keywords: Clustering; Energy storage systems; Optimization; Power system modeling; Temporal aggregation
JCR Impact Factor and WoS quartile: 5,200 - Q1 (2022); 5,000 - Q1 (2023)
DOI reference:
https://doi.org/10.1016/j.ijepes.2021.107706
Published on paper: May 2022.
Published on-line: November 2021.
Citation:
A. Orgaz, A. Bello, J. Reneses, Modeling storage systems in electricity markets with high shares of renewable generation: a daily clustering approach. International Journal of Electrical Power & Energy Systems. Vol. 137, pp. 107706-1 - 107706-11, May 2022. [Online: November 2021]
