Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity

September 20, 2015

Laura Brown (PI) received a $699,796 NSF grant. The title of the project is “CRISP Type 2: Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity.” The co-principal investigators of this project are Chee-Wooi Ten (ECE) and Wayne Weaver (ECE). This is a three-year collaborative project with four other institutions with a total budget of $2,499,801. The project addresses the rapid evolution of the electricity grid, from one based on few centralized generators providing power to millions of users to one where many distributed energy resources. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels that deliver electricity from the transmission grid to customers, to distribution microgrids, highly intelligent entities that actively manage production, storage and use of electricity.



CPS: Breakthrough: Toward Revolutionary Algorithms for Cyber-Physical Systems Architecture Optimization

National Science Foundation

Design optimization of cyber-physical systems (CPS) includes optimizing the system architecture (topology) in addition to the system variables. Optimizing the system architecture renders the dimension of the design space variable (the number of design variables to be optimized is a variable.) This class of Variable-Size Design Space (VSDS) optimization problems arises in many CPS applications including (1) microgrid design, (2) automated construction, (2) optimal grouping, and (3) space mission design optimization.

Evolutionary Algorithms (EAs) present a paradigm for statistical inference that implements a simplified computational model of the mechanisms embedded in natural evolution, with potential to solve this problem. However, existing EAs cannot optimize among solutions of different architectures because of the inherent strategy for coding the variables in EAs. Existing EAs resembles natural evolution in which a given architecture can evolve by improving the state of its variables but cannot be revolutionized. Inspired by the concept of hidden genes in biology, this project investigates revolutionary optimization algorithms that can optimize among different solution architectures and autonomously develop new architectures that might not be known a priori, yet are more fit solution architectures. Efficacy of the new algorithms for CPS is evaluated in the context of space mission design optimization.

Intellectual Merit:
There is an increasing demand in the scientific community for autonomous design optimization tools that can revolutionize systems designs and capabilities. Most existing optimization algorithms can only search for optimal solutions in a fixed-size design space; and hence they cannot be used for solution architecture optimization. Few existing algorithms can search for optimal solutions in VSDS problems; however these are problem-specific algorithms and cannot be used as a general framework for VSDS optimization. This project investigates the novel concept of hidden genes in coding the variables in evolutionary algorithms so that the resulting algorithms can be used for optimizing VSDS problems. The key innovation in these new algorithms is the new coding strategies. In addition, in this project, the standard operations in EAs will be replaced by new operations that are defined to enable revolutionizing a current population of solution architectures using the new coding strategy. The Pl’s recent research results, in the context of space mission design optimization, demonstrate that the hidden genes optimization algorithms can search for optimal solutions among different solution architectures, revolutionize an initial population of solutions, and construct new solution architectures that are more fit than the initial population solutions.

Investigator: Ossama Abdelkhalik

Secure Intelligent Architectures for Coordinating Agile Microgrids Research


Agile microgrids allow variable, distributed sources and loads to effectively interoperate over a broad range of conditions. Enabling large numbers of autonomously-managed micro-generators and loads must be accomplished through information-intensive architectures that create significant challenges regarding coordination and cyber security.

Active Research Projects


Research is concerned with developing concepts, techniques, and tools for enabling the design of secure and effective multi-agent systems for agile microgrids:

  • Combine cyber security, secure software, and system design, distributed control, and computational modeling to achieve a resilient and reliable control system design for agile microgrids.
  • Design and implement multi-agent system incorporating advanced distributed controls, intrinsic cyber security and safety.
  • Develop simulation-based microgrid design tools that utilize advanced in secure multi-agent distributed control to assist in microgrid development projects involving variable sources and controlled loads