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This paper outlines a Mixed-Integer Multi-Period Linear Programming approach to identify so-called no-regret energy transition pathways. The methodology presented is essentially a trilemma-solver that supports industry, network operators, and regional governments to decide upon efficient and effective investments to meet CO2 reduction targets. The decarbonization of an industrial cluster like the Port of Rotterdam (PoR) is presented as a case study to demonstrate the approach. Different scenarios are evaluated, such as ‘accelerated decarbonization’ of the Rotterdam industrial cluster. This scenario calculates the most cost-efficient way to accelerate the transition. In addition, the methodology identifies which levers are essential for decarbonization under different scenarios. One limitation of the approach is its implicit modeling of uncertainties, currently part of two separate PhD trajectories.

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Accelerating the Energy Transition: Determining No-Regret Transition Pathways in the Port of Rotterdam

  • 会议论文

  • 发表日期：

    2025年

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  • Jaron Davelaar,
  • Jan van Schijndel,
  • Nort Thijssen,
  • Marian Bijl,
  • Rutger de Mare,
  • Edwin Perdijk,
  • Harry van Dijk

摘要

This paper outlines a Mixed-Integer Multi-Period Linear Programming approach to identify so-called no-regret energy transition pathways. The methodology presented is essentially a trilemma-solver that supports industry, network operators, and regional governments to decide upon efficient and effective investments to meet CO2 reduction targets. The decarbonization of an industrial cluster like the Port of Rotterdam (PoR) is presented as a case study to demonstrate the approach. Different scenarios are evaluated, such as ‘accelerated decarbonization’ of the Rotterdam industrial cluster. This scenario calculates the most cost-efficient way to accelerate the transition. In addition, the methodology identifies which levers are essential for decarbonization under different scenarios. One limitation of the approach is its implicit modeling of uncertainties, currently part of two separate PhD trajectories.