Optimisation of energy supply chains considering sustainability aspects

Emenike, Scholastica N. (2021) Optimisation of energy supply chains considering sustainability aspects. PhD thesis, University of Glasgow.

Abstract

The supply chain of energy sources and, in particular, natural gas is prone to endogenous and exogenous disruptions that affect the system’s operational performance and flow capacity, thereby contributing to greenhouse gases (GHG) through methane (CH4) emissions. Although there are operational strategies to improve the gas supply chain, the need for resilience-driven optimisation that provides a system-based workflow to mitigate continuous and prolonged disruptions in the midstream remains crucial. This study focuses on developing a novel optimisation model that investigates the potential of a complementary design in the natural gas supply chain as a mitigation approach, enhancing throughput delivery without disconnections, and exploring the potential retrofit benefits of an existing natural gas supply chain infrastructure. To achieve this, optimisation in the supply chain’s transmission echelon is deployed to increase flexibility capacity, reduce gas losses, and minimise emissions. In this study, a lateral relief pipeline in the transmission node is proposed as an alternative pathway for gas flow to increase the resilience of the supply chain. This proposed strategy transmits excess trapped gas between inlet and outlet nodes during plant shutdowns within operational and contractual constraints. This redundancy compensates for downtime and pressure drop caused by shutdowns of system nodes during disruptions. The objective of the optimisation problem is to maximise throughput through flow flexibility and minimise carbon dioxide (CO2) emissions through a reduction in gas losses. Different scenarios are introduced to achieve the objective function optimum. Firstly, the baseline scenario (BS) of the system’s status is analysed under normal conditions to identify the flow rate gap. Then the disruption scenario (DS) is introduced where the impact of the lateral relief pipeline to mitigate unplanned shutdowns is analysed by using defined parameters in a steady state (SS). With a fixed shutdown period, the variation in plant node performance is examined at different flow rates. Lastly, in a transient state (TS), the pressure variation between the inlet and the outlet nodes in the mainline and when the relief pipeline node is opened is investigated. All scenarios affect the supply chain’s overall performance; therefore, the resulting flow rates are compared for optimum decision making. A multi-stream, multi-period, single-product transmission model to satisfy consumer demand within a given time frame is developed for the simulation, formulated as a mixed-integer linear programming (MILP) model, and applied within an optimisation framework where interruptions to the supply chain are studied to optimise the strategic planning problem. The optimisation procedure is formulated in a deterministic environment, and the model is run using General Algebraic Modelling System (GAMS) 26.14 with the CPLEX solver 12 in an intel ® core ™ i7 and a zerooptimality gap. Data collected from gas companies in the case study country are analysed and used to forecast and calculate the gas flow rate and the required capacity to meet growing demand. The data accessed enhance the applicability of the proposed model. Also, the interactions between the nodes in the supply chain are adjusted to mitigate interruptions and increase overall efficiency. Furthermore, an economic analysis of the proposed complementary design is carried out to ascertain possible tradeoffs between costs and resilience. Finally, a sensitivity analysis is conducted to assess the impact of key parameters on the overall model’s prediction.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Energy, natural gas, supply chain, emission, mitigation, sustainability, relief pipeline, optimisation, mixed integer linear programming, resilience.
• Subjects: T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Falcone, Professor Gioia
• Date of Award: 2021
• Depositing User: Theses Team
• Unique ID: glathesis:2021-82278
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 14 Jun 2021 07:00
• Last Modified: 15 Jun 2021 14:30
• Thesis DOI: 10.5525/gla.thesis.82278
• URI: https://theses.gla.ac.uk/id/eprint/82278