Uncertainty analysis for industrial electrification systems

Abstract

Acknowledged by the United Nations as part of their sustainable development goals, reduction of greenhouse gas emissions is paramount in preserving our planet for future generations. Electrication in the industrial sector is considered one of the energy transition pathways that can contribute to meeting the emission reduction targets of the Paris Agreement. An important barrier that needs to be overcome in order to fully adopt its potential is uncertainty and its risk to the implementation of different electrication alternatives. The absence of information that illustrates the effect of uncertainty on the performance of these alternatives decreases the stability of business cases and hinders the decision-making process. To filll a part of this knowledge gap, this research performed a case-study revolving around a mixed integer linear programming (MILP) model of an integrated chemical cluster in the Port of Rotterdam. The effect of the uncertain factors on the KPIs was analyzed using an exploratory modelling and analysis (EMA) approach. In addition, a key opportunity of the MILP model was utilized by changing the objective function to look at individual and collective actor optimization perspectives. This research implicates that EMA can be an effective approach to explore the effect of various uncertain factor on industrial systems undergoing electrication. Furthermore, when the goal is to perform a broad uncertainty analysis that allows for easy implementation of actor optimization perspectives while requiring only limited information about the uncertain factors in the form of sampling bandwidths, the combination of EMA and MILP can be a powerful tool.