Integrating Time-Varying Environmental Indicators into an Energy Systems Modeling and Optimization Framework for Enhanced Sustainability

Data-driven decision-making is crucial in the transition to a low-carbon economy, especially as global industries strive to meet stringent sustainability goals. Traditional life cycle assessments often rely on static emission factors, overlooking the dynamic nature of the energy grid. As renewable energy penetration increases, grid carbon intensity fluctuates significantly across time and regions, due to the inherent intermittency of renewable sources like wind and solar. This variability introduces discrepancies in emission estimations if time-averaged factors are applied, leading to sub-optimal process operations and unintended environmental consequences. To this end, we present a real-time emission-aware optimization framework, which is implemented through a mixed-integer linear programming formulation that can determine optimal design configurations and operation schedules while simultaneously mitigating emissions by utilizing electricity price forecasts, time-varying emission factors, sporadic weather data, and supply and demand variability. The optimization model integrates life cycle assessment criteria to evaluate various environmental inputs, categorized into direct on-site emissions (Scope 1), indirect emissions from energy use (Scope 2), and upstream, downstream, and construction-related emissions associated with the process system (Scope 3). The framework, demonstrated through a detailed hydrogen production case study, showcases the effect of time-varying emissions by generating a set of optimal solutions that balance the trade-offs between environmentally sustainable and economical operational strategies.