The main objective of this project is to formalize ground-breaking general principles in support of investment planning and operating protocols for the changing electric energy systems. Having this is crucial for assessing effects of candidate hardware and software technologies and for creating an environment in which flexible technologies are utilized by complementing each other’s performance and by jointly meeting the societal needs. Since the performance of many technologies of interest greatly depends on how well they are integrated over time and stakeholders (system modules), an effective framework needs to support decisions that are data-enabled and interactive over time and among different system decision makers. In this project a complex dynamical systems point of view is taken which recognizes such distributed nature of decisions and introduces novel modeling, control and protocol principles in support of system integration in time and space. In sharp contrast with the lack of theoretically-provable performance in the evolving electric power system architectures, this project explores fundamentals and takes major steps toward solving this long-overdue problem. The most important impact will be on the way electric power industry architectures evolve. In particular, the project will catalyze the adoption of data-enabled decision making and automation. Notably, this approach will help educate students to think about emerging electric energy systems as complex dynamical systems for which much innovation is needed and possible. Results of this research will be disseminated through a workshop on design and control of complex energy systems.
The next generation Supervisory Control and Data Acquisition (SCADA) can evolve based on protocols defined according to a dynamic monitoring, and decision system (DyMonDS) framework envisioned in this project. Such framework is crucial for assessing effects of candidate hardware and software technologies and for creating an environment in which technologies are integrated and utilized by complementing each other’s performance and by jointly meeting the societal needs. In this project a complex dynamical systems point of view is taken which recognizes such distributed nature of decisions and introduces novel modeling, control and protocol principles in support of temporal and spatial system integration. The main question is how to ensure stability and efficiency of these systems without increasing complexity of information management and consequent implications on cyber-security. Once principles for answering this complex question are formalized, it becomes possible to support selection of “right†technologies and for utilizing them based on data obtained through sensing, communications and control. To establish such principles and not radically change the industry, this project introduces a generalization of today’s Automatic Generation Control by the Balancing Authorities (BAs) which is fundamentally cooperative in nature. The interactions between BAs are managed by each area compensating its own Area Control Error (ACE) which is the net power imbalance contributed by both internal power deviations from schedules and by the tie-line flow deviations into the BA. This is a great example of distributed control through cooperation based on simple protocols of balancing ACE. In this project the ACE is generalized into a notion of an interaction variable (intVar) associated with each intelligent Balancing Authority (iBA). Notably, an intVar has a physical interpretation in terms of power and rate of change of power, but it is not restricted to the assumptions made by the industry today, and it allows for different unconventional architectures, such as small iBAs nested within the existing BAs. iBAs can be components or subsystems of diverse spatial and temporal granularity, and are technology agnostic; their specifications, modeling and interactive multi-layered control are in terms of an intVar common to all. This project targets specifically principles for protocols and enhanced standards for a general DyMonDS framework so that following this framework one can catalyze performance enhancements based on the PI’s recent finding that the intVar dynamics can be interpreted in terms of rates of exergy (potential to perform useful work) and anergy (wasted work).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
