Project Description
Smart Grids (SG) are the new paradigm in energy production and management. This new paradigm brings as much opportunities for building an energy efficiency society as it raises technical challenges. In a SG there are a large number of independent agents, from independent power producers to the individual customers. These agents can make independent decisions and take control actions, but the data available for this decision making is scattered, and the operating conditions can vary widely and rapidly. Models for the system, which usually play a fundamental in decision making and control design in power systems, are not available either. This wide variation of operating conditions requires self-adjustment and adaptation in the decision making process, adjustments that each agent must proceed based only on the local data available for it. Hence, the DD approach provides a natural and powerful framework in this context, both for control and for decision making.
We have started the research on these issues by applying VRFT to the self-tuning of AVRs (automatic voltage regulators) in systems with distributed generation (DG), with promising results. Our MSc student Aline Käfer is now exploiting, under the supervision of Professor Bazanella, the application of different DD control design methods (VRFT, IFT, OCI) for the tuning of the controllers in DG units. A major issue is establishing the best guidelines for specifying the reference model, so as to develop a fully automated self-adjustment tool for the excitation system (AVR+PSS) and governor of electrical generators. Subsequent developments will include the use of hybrid systems theory to the study and development of DD methodologies for the decision making processes in SGs.
