Bibliometric analysis of smart control applications in thermal energy storage systems. A model predictive control approach

Abstract

In the existing literature, the importance of control methods used to manage the operation of thermal energy storage systems increased in the last years. However, the application of smart control strategies is still far to become a significant part of the available scientific publications. Within the employed techniques, model predictive control appeared as the most promising method to control thermal energy storage systems. Therefore, in this paper, the application of this control strategy is widely studied. Regarding this analysis, significant literature gaps that have to be studied more in detail are found in the current scientific publications. The main goal of this study is to find out these gaps through a bibliometric approach, identifying the key knowledge areas using both databases Web of Science and Scopus. Results show that the main knowledge gaps in the literature are the ones related with a validation of model predictive control, its implementation in smart grids, an optimized sizing and management of the physical parts of the system, an accurate weather forecasting, and to exploit as much as possible the available renewable energy resources. Moreover, the tendency in publications during the whole period, the main authors, countries, and organisations are analysed.

This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31 - MCIU/AEI/FEDER, UE). The authors would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is a certified TECNIO agent in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme.