TY - JOUR
T1 - Multi-period optimisation of flexible natural gas production network infrastructure with an operational perspective
T2 - A mixed integer linear programming approach
AU - Yusuf, Noor
AU - Baldacci, Roberto
AU - AlNouss, Ahmed
AU - Al-Ansari, Tareq
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/10
Y1 - 2024/10
N2 - With the increased complexities in end markets, advanced quantitative tools became essential for efficient decision-making. Traditional methods that implement average forecasted demand and prices often fail to account for the dynamic nature of network behaviour. Hence, this study introduces a multi-period mixed integer linear programming (MILP) model for flexible natural gas allocation in complex networks. The model was formulated based on Qatar as a case study, utilising a Qatar-based natural gas monetisation network that includes four direct and two indirect monetisation processes. By integrating flexible operational boundaries with annual demand and price forecasts, the model optimises annual allocation strategies to maximise profitability. A scenario-based evaluation of three cases demonstrated the model's robustness and potential for enhancing profitability. Scenarios limited to operational constraints achieved the highest profitability, especially in allocating high-value commodities such as ammonia and urea. However, the proposed production capacities surpass the current infrastructure capabilities, requiring further infrastructure investments. The validation of results showed a slight profitability decrease of 1.2% when these investment costs were included. Notably, the case considering fixed and operating cost variables together as a single cost variable in the objective function, referred to as annualised cost (case C), offered optimal cost quantification with relaxed technical constraints. Overall, the multi-level multi-period MILP model aids decision-makers in understanding system capabilities and explores the benefits of flexible operation in proactively addressing endogenous and exogenous uncertainties.
AB - With the increased complexities in end markets, advanced quantitative tools became essential for efficient decision-making. Traditional methods that implement average forecasted demand and prices often fail to account for the dynamic nature of network behaviour. Hence, this study introduces a multi-period mixed integer linear programming (MILP) model for flexible natural gas allocation in complex networks. The model was formulated based on Qatar as a case study, utilising a Qatar-based natural gas monetisation network that includes four direct and two indirect monetisation processes. By integrating flexible operational boundaries with annual demand and price forecasts, the model optimises annual allocation strategies to maximise profitability. A scenario-based evaluation of three cases demonstrated the model's robustness and potential for enhancing profitability. Scenarios limited to operational constraints achieved the highest profitability, especially in allocating high-value commodities such as ammonia and urea. However, the proposed production capacities surpass the current infrastructure capabilities, requiring further infrastructure investments. The validation of results showed a slight profitability decrease of 1.2% when these investment costs were included. Notably, the case considering fixed and operating cost variables together as a single cost variable in the objective function, referred to as annualised cost (case C), offered optimal cost quantification with relaxed technical constraints. Overall, the multi-level multi-period MILP model aids decision-makers in understanding system capabilities and explores the benefits of flexible operation in proactively addressing endogenous and exogenous uncertainties.
KW - And flexibility
KW - Decision-making
KW - Mathematical programming
KW - Natural gas allocation
KW - Natural gas monetisation
UR - http://www.scopus.com/inward/record.url?scp=85211050522&partnerID=8YFLogxK
U2 - 10.1016/j.ecmx.2024.100818
DO - 10.1016/j.ecmx.2024.100818
M3 - Article
AN - SCOPUS:85211050522
SN - 2590-1745
VL - 24
JO - Energy Conversion and Management: X
JF - Energy Conversion and Management: X
M1 - 100818
ER -