Probabilistic Life Cycle Costing : A Monte Carlo Approach for Distribution System Operators in Sweden

Abstract: Investments in power systems are characterized by large investment costs and uncertainties doto extended time frame. New consumption patterns in the electricity grid, as well as an aginggrid calls for modernization, new solutions and new investments. Components in the electricalsystem is characterized by most of their costs that are caused after their acquisition. One stateof the art method in analyzing investments over long time frames and provide long-term costestimation is life cycle costing (LCC). In LCC a "cradle to grave"-approach is performed whichenables comparative cost assessment to be made. This thesis reviews the existing literature inprobabilistic life cycle costing and gives a step by step methodology for DSOs to systematicallyaddress uncertainty in cost and technical parameters.This thesis proposes a Monte Carlo sampling method in combination with a Markov Chainfailure model to model failures is providing a comprehensive method of reaching nancial benetwhen comparing dierent investment decisions. The model evaluates nancial implications andtechnical properties to demonstrate the total cost of components. This thesis analyses a casefor Swedish distribution system operators and their investment in transformers. The proposedmodel includes an all-covering model of costs and incentives. The main conclusion is that probabilisticlife cycle costing benets investment decisions and the applied method shows promisingresults in addressing uncertainty and investment risks. The developed PLCC model is used onan investment decision where two transformers are compared. Results shows that PLCC is apowerful tool and could be used in power system applications.