September 28, 2017
The National Science Foundation (NSF) has awarded a $199K research grant to support the ME-GREEN project: Manufacturing for Environment: Generating Renewable Energy via Enterprise Network. Built upon the success of USDA B-GREEN project, two Engineering faculty members, Dr. Tongdan Jin and Dr. Clara Novoa, will lead a multi-disciplinary team comprised of IE, MfgE, EE, MSEC and Computer Science students to implement this three-year project. The goal of this project is to model, optimize and operate micro-grid systems featuring intermittent renewable power to realize zero-carbon manufacturing and supply chain operations.
Manufacturing and transportation represents 35-40% of the global carbon emissions. A typical factory consumes as much electric energy as 5,000-10,000 households. ME-GREEN project aims to address three fundamental questions: First, is it economically viable to deploy wind, solar and utility-scale storage to achieve energy independency? Second, is it technically feasible to operate a net-zero energy manufacturing or warehousing facility using intermittent renewables? Third, can distributed energy resources (DER) actively participate in demand responses, create virtual power plants, form islanded microgrid and realize vehicle-to-grid operations under extreme conditions? To answer these questions, multi-criteria stochastic programming models will be developed to optimize the DER sizing, siting, and maintenance for minimizing energy cost while ensuring reliability and resilience against natural disasters. If successful, the project accelerates the transition of large industries from energy users into “green energy” consumers.
The research hypothesis is that that virtually any manufacturing facility around the world could be powered with 100 percent onsite wind and solar energy at an affordable cost. Research tools include big data analytics, deep learning, neural network, systems modeling and agent-based simulation to address the operational challenges arising from DER units, ranging from power intermittency, voltage stability, demand uncertainty, production schedule, grid resilience, and utility-scale storage. This research contributes to the development of a new stochastic optimization algorithm and probabilistic modeling techniques. The ultimate goal is to assist the U.S. manufacturing industry in gaining unprecedented competitive advantages by transforming from power-intensive, carbon producers to environmentally-benign and energy-independent entities in the smart grid era.