The primary aim of this proposal is to develop a fundamental system science for the design and control of dynamically coupled infrastructures, with the objective of influencing their collective performance. We will examine the flow of resources and commodities between infrastructures via devices and junctions we refer to as multi-role intermediaries (MRIs). Examples of MRIs include: (i) building energy systems that couple the built environment with electricity infrastructures (ii) GPS-enabled mobile phones that couple transportation and telecom infrastructures to manage traffic congestion and (iii) plug-in hybrid electric vehicles (PHEVs), which couple transportation and electricity infrastructures by using grid electricity for transportation, possibly sending stored electricity to the grid (vehicle to grid, or V2G) when appropriate.
This proposal focuses on plug-in hybridization and V2G integration as testbed application problems. PHEVs could facilitate sustainable transportation by utilizing electricity produced by renewable resources (e.g. wind and solar energy). PHEVs also have the potential to improve resilience (transient and steady-state integrity) by making responses to major disruptions (e.g. a blackout or petroleum shortage) more robust. However, without proper system-level design, PHEVs could have negative impacts if, e.g., they are charged at peak hours, or their control objectives are not balanced with those of the power system.
To develop foundations of a new coupled infrastructure system science, we propose the following tasks:
1. Combine agent-based modeling and life cycle assessment into a novel framework for evaluating the long-term sustainability of dynamically coupled infrastructures.
2. Develop hybrid state diffusion approximation methods to model the dynamics of resilience in coupled infrastructures with stochastically available resources and MRIs.
3. Create a fundamental hierarchical (multi-scale) framework for optimizing the design and configuration of MRIs with respect to resilience and sustainability within each infrastructure.
4. Use stochastic dynamic programming and Poincare' map techniques to optimally control intermediaries in light of their stochastic dynamic switching between different infrastructures.
5. Construct Lyapunov energy functions for MRI-coupled systems to control for stability and resilience.
6. Develop statistical, energy-based model reduction techniques to reduce complexity in infrastructure and intermediary models to facilitate analysis and design of coupled infrastructures.
The methods and tools developed in these tasks will provide fundamental theoretical contributions to engineering disciplines that are rooted in dynamics and controls, as well as to the social sciences and the field of industrial ecology. With respect to PHEVs, we will (1) quantify their impact on sustainability and resilience of the transportation and electricity generation infrastructures, (2) design and configure PHEV powertrains that balance conflicting needs of the transportation and electricity generation infrastructures, (3) develop power and energy management strategies in PHEVs, taking into account their transportation role, their role in providing distributed storage to the electrical grid, and the switching between such roles and (4) develop grid power and energy management methods that capitalize on the distributed capacity provided by V2G integration and the resulting ability to accommodate renewable resource intermittency and prevent and recover from catastrophic failures.
The intellectual merit of this work is related to the challenge of developing a framework to design and control MRIs whose functions are influenced by stochastic processes, interactions across space and time scales, and human decision-making. The framework developed in this research will enable practical and efficient identification of infrastructure configurations that are globally sustainable and resilient. The broader impact of the proposed work has several components: First, it will bring together investigators from the fields of mechanical and power systems engineering, economics, and industrial ecology who together will train six graduate students. Second, the methods and tools we develop will be disseminated to industry via an external advisory and technical publications. Finally, results (including a sustainability simulation tool) will be incorporated directly into classroom instruction through education and outreach programs that target underrepresented students at the high school and undergraduate level.
- A Microsimulation of Energy Demand and Greenhouse Gas Emissions from Plug-in Hybrid Electric Vehicle Use
- An Agent-Based Model of Energy Demand and Emissions from Plug-In Hybrid Electric Vehicle Use
- Energy and Water Interdependence, and Their Implications for Urban Areas
- Environmental Assessment Of Plug-In Hybrid Electric Vehicles Using Naturalistic Drive Cycle And Usage Pattern Information
- Environmental Assessment of Plug-In Hybrid Electric Vehicles Using Naturalistic Drive Cycles and Vehicle Travel Patterns
- Environmental Assessment of Plug-In Hybrid Electric Vehicles Using Naturalistic Drive Cycles and Vehicle Travel Patterns: A Michigan Case Study
- Physical and Behavioural Determinants of Resilience in the Transportation System: A Case Study of Vehicle Electrification and Trip Prioritization