Enhancing transformer ageing prediction in NOMs methodology: incorporating demand data analysis

In response to escalating electrical demands from Electric Vehicles (EVs) and Heat Pumps (HPs), the impact on transformer aging has been comprehensively investigated. By incorporating extensive demand data analysis, this study enhances the Network Output Measures (NOMs) methodology to predict transformer aging more accurately. The penetration of EVs and adoption of HPs, which are expected to double the typical household electricity consumption, are quantified and simulated. This simulation uses detailed data from a lead asset database, applying comprehensive equations to predict the aging and degradation of transformers under increased load conditions. This study presents a critical advancement in the understanding and management of transformer aging under the increased stress imposed by modern energy demands. The results highlight the accelerated aging process, quantified as a rate substantially higher than under normal conditions. A revised approach to the NOMs methodology is proposed, emphasizing the need for integrating real-time monitoring and advanced analytics to better manage the life cycle of electrical network assets.