Renewable-Driven Microgrid Design, Planning, and Operation of Integrated Gasification Fuel Cell for Biomass Upgradation to Biofuels

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Original abstract

Biofuel production and upgrading offer a promising pathway for reducing the carbon intensity of liquid fuels. However, biomass-to-biofuel systems are energy intensive and require coordinated supplies of electricity, heat, and hydrogen. In a typical process, biomass is converted to bio-oil through pyrolysis, hydrogen is produced using an electrolyzer, and the resulting bio-oil is upgraded to transportation-grade biofuel. When the pyrolyzer is electrically heated and the upgrading unit uses an electrochemical pathway, the overall process imposes a large and time-varying electrical demand. Supplying this demand with renewable energy is attractive, but the variability of solar and wind generation can lead to renewable curtailment, grid dependence, and operational challenges. These issues motivate the development of integrated microgrid scheduling frameworks that can coordinate renewable generation, storage, grid exchange, and onsite fuel-based power generation. This work develops a renewable-driven microgrid optimization framework for supporting a biofuel production and upgrading plant. The proposed microgrid includes solar photovoltaic generation, wind turbines, battery energy storage, backup generators, grid interconnection, and an integrated gasification fuel cell (IGFC) subsystem. The IGFC subsystem consists of an electrified biomass gasifier, syngas storage, and a solid oxide fuel cell (SOFC). A fraction of the available biomass is fed to the gasifier to produce syngas for SOFC power generation, while the remaining biomass is supplied to the main biofuel production plant. The intermediate syngas storage decouples gasifier operation from SOFC power generation, allowing syngas to be produced when energy is available and consumed by the fuel cell when dispatchable electricity is needed. High-fidelity equation-based models are developed for the electrified gasifier and SOFC to capture the key thermochemical and electrochemical behavior of the IGFC subsystem. Directly embedding these nonlinear models in the microgrid scheduling problem would lead to a computationally expensive MINLP formulation. Therefore, ReLU neural-network surrogate models are trained from simulation data generated using the detailed first-principle gasifier and SOFC models. ReLU activation functions are used because they provide piecewise-linear surrogate representations that can be embedded explicitly into an algebraic optimization model via OMLT (Ceccon, et al., 2022). The trained surrogates are reformulated as mixed-integer linear constraints and incorporated into a mixed-integer linear programming framework for microgrid scheduling. The resulting optimization model coordinates renewable generation, battery charging and discharging, syngas production and storage, SOFC dispatch, generator operation, and grid electricity purchase while meeting the energy demand of the biofuel production plant. Case-study results demonstrate that the IGFC-enabled microgrid can improve renewable utilization by converting otherwise curtailed renewable electricity into stored syngas and dispatchable SOFC power. The results also show how energy storage provides an additional form of energy flexibility, particularly during periods of low renewable generation and peak demand times for the main grid. By optimally coordinating gasifier operation, SOFC power production, and grid imports, the proposed framework reduces reliance on external electricity purchases and supports more economical operation of the biofuel facility. This work provides a machine learning-based optimization framework for integrating thermochemical conversion, electrochemical power generation, renewable energy, and biofuel production within a unified optimization architecture. The proposed approach highlights the potential of IGFC-enabled microgrids to support low-carbon fuel production while improving renewable energy utilization, reducing curtailment, and enhancing the operational flexibility of biomass-to-biofuel systems.

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