Description

The goal of this doctoral thesis is to design non-centralized optimization-based control methods for LSCSs based on the ideas behind (non-cooperative) evolutionary game theory. The focus on evolutionary game theory is motivated by recent developments on the field of non-centralized optimization-based control, and on the signicant gap in research that exists between the classical and evolutionary approaches. Through this research, we seek to develop novel non-centralized optimization-based methods suitable for the control of LSCSs, and to illustrate their application to practical control engineering problems. Moreover, we seek to further complement the areas of optimization-based control and evolutionary game theory by establishing equivalences and/or relations between the concepts of both fields. All considered, this research seeks to contribute, both in theory and practice, to the areas of non-centralized
optimization-based control, non-cooperative game theory, and LSCSs.

The work is under the scope of the following projects:

• L-BEST: Supervision and fault-tolerant control of smart infrastructures based on advanced learning and optimization (web)
• MASHED: Efficient Management of Energy Systems including Hybrid Electrochemical Energy Storage using Digitalisation Technologies (web)