Bestämning av ekonomiskt optimala utbytesåldrar för elkraftkomponenter i distributionsnät : En Weibullbaserad modell för livslängds- och kostnadsanalys

Sammanfattning

Electricity distribution companies face reinvestment needs due to aging infrastructure while the requirements for reliability of supply and cost efficiency continue to increase. Decisions regarding when components should be replaced are complex since they are influenced by both investment costs and the consequences of future failures. The purpose of this study is to contribute to an increased understanding of how optimization-based decision models can be used to support reinvestment planning in distribution networks by analyzing how costs and risks can be integrated into a structured decision-support framework. The objective is to develop an optimization model for determining economically optimal replacement strategies for power system components in distribution networks. The study was conducted in collaboration with Sundsvall Elnät. An optimization model was developed in which the probability of component failure was modeled using the Weibull distribution. The model combines investment costs, outage costs, and lifetime data to calculate the long-term average cost of different replacement strategies. To investigate the sensitivity of the model, scenario-based analyses with varying Weibull parameters were also carried out. The results show that the model can be used to identify economically optimal replacement ages and strategies for different component types and that the results are influenced by the interaction between failure risk, outage costs, and reinvestment costs. The analysis also shows that geographical location and the consequences of outages are, in many cases, more important for reinvestment decisions than the age of the components. The study shows that data regarding component lifetimes, replacement costs, and outage costs can be combined in a model that can be used as a basic support tool for the planning and prioritization of reinvestments in distribution networks.