Abstract:

While there have been extensive attention and works dedicated to promote in the sustainability in the energy systems, broadly termed to include the subsystems of electricity, petroleum, and natural gas, a somewhat overlooked aspect is the interdependence between energy and other infrastructure systems, especially with water systems, and the potential adverse impacts to economics, reliability, and sustainability caused by such interdependence. For example, regulations in the water sector to preserve freshwater may restrict water usage in the power sector, likely causing reduced available generation capacities and hence jeopardizing the reliability of power systems. On the other hand, environmental policies only focused on the power sector, such as those to encourage adding CO2 capture and sequestration (CCS) capabilities to existing and new coal plants would further strain the water system as coal plants with CCS are among the heaviest users of water. Thus, there is a pressing need to better understand and manage the interdependence of critical infrastructure systems such that the goal of promoting sustainability can be achieved across all systems, while in the meantime, economics and reliability will not be undermined in any particular system. This proposed work is to directly address such a need through advancing the frontiers of the theory, modeling and computation of large-scale, interdependent complex systems by way of distributed, high-scalability computing. The results will be widely disseminated through publications and seminars. Further, the project team will leverage established institutional programs to outreach to the general public, especially to high-school students and teachers, such as through the Engineering Projects In Community Service program and Purdue’s Energy Academy.

As the grand vision of this project is to promote sustainability across interdependent systems, as well as to achieve economic efficiency and to maintain reliability, through decentralized yet coordinated management of individual systems, we are to establish a complete modeling, analytical, and computational framework. Such a framework is based upon the general class of augmented Lagrangian methods (ALM) originated from convex optimization. While the ALM is not a new algorithm, the current implementation of such algorithms has not taken advantage of its distributed feature, which would be particularly suitable to deal with large-scale, interlinked systems. One of the major contributions of this work is to establish the theoretical foundations of distributed ALMs and to implement the algorithms on supercomputer clusters to demonstrate the benefits of distributed computing, which will pave way for cloud computing such that the algorithms can be used by decision-makers even without access to supercomputers. Another contribution is that the ALM algorithms will be extended to incorporate stochastic data, both in terms of theories, such as algorithm convergence, and in terms of implementation. The computational methods will be tested and validated through real-world-sized models of interdependent power and water systems.  

Collaborators: Yihsu Chen

Andrew L. Liu