CyberSEES: Type 1: Collaborative Research: Sustainability-aware Management of Interdependent Power and Water Systems

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While extensive attention has been given to sustainability in the energy systems, including the subsystems of electricity, petroleum, and natural gas, an oft-overlooked aspect is the interdependence between energy and other infrastructure systems, such as water and transportation 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 encouraging retrofitting or installing carbon dioxide capture and sequestration capabilities to existing and new coal plants would further constrain the water system as coal plants with carbon sequestration are among the heaviest users of water. Thus, there is a clear need to better understand and manage the interdependence of critical infrastructure systems to promote sustainability across all systems, while not undermining economic and reliability considerations. This proposed work aims to address this need through the theory, modeling and computation of large-scale, interdependent complex systems by way of distributed, highly scalable computing. The results will be widely disseminated through publications and seminars. Further, the project team will leverage established institutional outreach programs 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. 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 by establishing a complete modeling, analytical, and computational framework based upon the general class of augmented Lagrangian methods originating from convex optimization. While the augmented Lagrangian method 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 goals of this work is to establish the theoretical foundations of distributed Lagrangian methods and to implement the algorithms on supercomputer clusters to demonstrate the benefits of distributed computing. This research aims to pave the way for cloud computing such that the algorithms can be used by decision-makers even without access to supercomputers. Another contribution is that the augmented Lagrangian method algorithms will be extended to incorporate stochastic data, both in terms of theoretical issues such as algorithm convergence as well as practical implementation. The computational methods will be tested and validated through real-world models of interdependent power and water systems.