LAPSE:2025.0273v1
Published Article
LAPSE:2025.0273v1
Integrating Time-Varying Environmental Indicators into an Energy Systems Modeling and Optimization Framework for Enhanced Sustainability
June 27, 2025
Abstract
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.
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.
Record ID
Keywords
Life Cycle Assessment, Optimization, Real-time carbon accounting, Renewable and Sustainable Energy, Time-varying indicators
Subject
Suggested Citation
Sousa MPD, Kakodkar R, Flores BM, Suresh S, Shah HB, Kenefake D, Pappas I, Fu X, Demirhan DC, Ruggiero B, Mutlu M, Pistikopoulos EN. Integrating Time-Varying Environmental Indicators into an Energy Systems Modeling and Optimization Framework for Enhanced Sustainability. Systems and Control Transactions 4:753-758 (2025) https://doi.org/10.69997/sct.171539
Author Affiliations
Sousa MPD: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Kakodkar R: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Flores BM: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Suresh S: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Shah HB: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Kenefake D: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Pappas I: Shell Global Solutions International B.V., Amsterdam, Netherlands
Fu X: Shell Global Solutions International B.V., Amsterdam, Netherlands
Demirhan DC: Shell Global Solutions International B.V., Houston, Texas, USA
Ruggiero B: Shell Global Solutions International B.V., Houston, Texas, USA
Mutlu M: Shell Global Solutions International B.V., Houston, Texas, USA
Pistikopoulos EN: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Kakodkar R: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Flores BM: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Suresh S: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Shah HB: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Kenefake D: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Pappas I: Shell Global Solutions International B.V., Amsterdam, Netherlands
Fu X: Shell Global Solutions International B.V., Amsterdam, Netherlands
Demirhan DC: Shell Global Solutions International B.V., Houston, Texas, USA
Ruggiero B: Shell Global Solutions International B.V., Houston, Texas, USA
Mutlu M: Shell Global Solutions International B.V., Houston, Texas, USA
Pistikopoulos EN: Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, Texas, USA; Texas A&M University, Texas A&M Energy Institute, College Station, Texas, USA
Journal Name
Systems and Control Transactions
Volume
4
First Page
753
Last Page
758
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 0753-0758-1219-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0273v1
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https://doi.org/10.69997/sct.171539
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Jun 27, 2025
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References Cited
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