This work presents an optimization-based tool for designing industrial carbon-neutral energy systems that can be constrained to reflect the design and operation changes over time. A multi-period optimization framework given a time horizon and a temporal resolution is built based on a proposed superstructure for carbon-neutral energy systems capable of minimizing the CO2 emissions, whilst determining the optimal energy flows and associated costs. The bi-objective model is addressed by the ϵ-constraint approach, providing the decision-maker with a range of alternatives for developing a sustainable roadmap from existing systems to future ones.
The model identifies the best energy system configuration, seasonal operations, energy mix, and technology capabilities for meeting seasonal power, heat, and cooling demand in a sustainable manner. Based on a mixed-integer nonlinear programming (MINLP) model and life cycle assessment (LCA) principles, the model has been used to evaluate a wide range of technologies under different scenarios, including solar photovoltaics (PV), wind turbines, gas boilers, electrode boilers, gas turbines, carbon capture and storage (CCS) and energy storage.
The effective integration of conventional and renewable sources and the appropriate operation of energy storage units increase the system's flexibility and sustainability, while overcoming the intermittent nature of renewable energy sources. Emissions can be significantly reduced by 20%â40% based on current economics and restrictions. These numbers can be further reduced when electric power mixes present a higher penetration of low-carbon technologies. Overall, the framework can provide guidelines for economic and energy-efficient transition to low-carbon technologies and/or fuel switching in the industrial sector.
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