Risk assessment of the UK electricity supply network: a preference-based decision support method

The resilience and reliability of essential infrastructures, such as power grids, are critical for the smooth functioning of societies. With the rapid diffusion of electric vehicles (EVs), reliance on a stable and reliable electric power supply has significantly increased. This necessitates a comprehensive risk analysis framework to understand the reliability of electric power supply systems. Identifying crucial macro-level risks involves a certain degree of uncertainty and requires expert preference elicitation. It is also prominent for a reliable preference elicitation model to appropriately handle the subjective judgments of decision makers (DMs). In this study, a multi-criteria decision analysis (MCDA) perspective is adopted by integrating a spanning trees enumeration (STE) method with the best-worst method (BWM) to capture the hesitancy and uncertainty of DMs in identifying the most crucial risks in the UK electricity supply network system. This approach considers the existence of more than one possible best (i.e., the most favorable) or worst (i.e., the least favorable) criterion in the model. To validate the proposed STE-BWM model, a set of Monte Carlo simulations and a real-world application are implemented coupled with comparative and sensitivity analyses. The simulations are conducted under various defined numerical experiments, and the results indicate a satisfactory success rate of STE (i.e., 65.80 %) in identifying the unique best or worst criterion in various experiments. The applicability of the proposed STE-BWM is shown in a case study of the UK electricity supply network risk assessment.