The goal of this research is to model and design a grid-connected onsite generation system featuring intermittent renewable power to realize zero-carbon industrial operations. Three fundamental questions will be addressed that are confronted by the manufacturing industries: 1) is it economically variable to deploy wind, solar and other green power to achieve energy independency, 2) is it technically feasible to operate a net-zero carbon facility using intermittent power, and 3) can distributed energy resources (DER)actively participate in demand responses by forming a virtual power plant to provide two-way energy flow? To answer these questions, multi-criteria stochastic programming models will be developed to optimize the DER sizing, siting, and maintenance for minimizing energy cost with ensuring reliability, resilience and power quality. Primary DER units include wind turbine, solar photovoltaics, combined heat and power, electric vehicles, and battery banks.

The research hypothesis is that that virtually any manufacturing facility around the world could be powered with 100 percent onsite wind and solar power at an affordable cost. This hypothesis will be tested in various locations around the world with diverse climatic conditions. Research activities include analytics, system modeling and optimization, and simulation to address the operational challenges pertaining to power intermittency, voltage stability, demand response, production-inventory schedule, grid resilience, and transportation electrification. This research makes an attempt to seek a zero-carbon energy solution for enterprise systems through the integration of intermittent renewable power. Though onsite generation and microgrids have been adopted by industries, an in-depth study on return-on-investment, loss-of-load risks, and interaction with utility grid is still rare in the literature. Methodologically, a two-stage optimization algorithm will be employed. In stage 1, the sizing and siting of DER units will be optimized. In stage 2, the maintenance policy to minimize the equipment lifecycle cost will be established. A two-stage decision process is able to eliminate inferior solutions in the initial search. In modeling, a probabilistic measure to ensure grid resilience by minimizing the DER and microgrid recovery time against natural disasters or extreme events will be employed. This research is targeted 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.

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Texas State University - San Marcos
San Marcos
United States
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