Due to the increasing threat of global warming, today’s grid system faces large changes and challenges as more renewable sources are being implemented in the grid. In order to handle these changes and secure future distribution, new technologies and components are necessary. This study investigates the innovation – IT/OT integration and its rate of adoption among potential adopters – Distribution System Operators. Based upon 8 expert interviews, 19 interviews with Swedish DSOs and literature, the study has concluded the following: - Increased micro production in the Swedish electricity grid system is the main drivers for IT/OT integration. IT Security and Swedish Energy Market Inspectorates current pricing model are two of the main inhibitors for IT/OT integration. - Key factors, such as perceived attributes of the innovation and business transformation speed are of high importance when analyzing rate of adoption. - Medium-sized DSOs with high ambition are likely to adopt before other customer segments. Thus, they are potential target customers for suppliers, such as Ericsson. The thesis contributes to literature by providing research of a technical innovation within a complex market. Future research of interest is to apply similar methodology for predicting rate of adoption of IT/OT integration in other nations, since drivers and regulations might differ. / Det ökade hotet från klimatförändringar har medfört att dagens elnätssystem står inför stora förändringar och utmaningar då allt fler förnyelsebara källor implementeras i elnätet. För att hantera denna förändring och säkra framtidens eldistribution krävs att ny teknik och nya komponenter implementeras i elnätet. Denna rapport undersöker innovationen - IT/OT integration och hur denna sprids bland potentiella kunder – elnätsdistributörer. Baserat på 8 expertintervjuer, 19 intervjuer med svenska elnätsdistributörer och litteratur har studien kommit fram till följande slutsatser: - Ökad mikroproduktion i det svenska elnätet är den främsta drivaren för IT/OT integration. IT säkerhet och Energimarknadsinspektionens nuvarande regleringsmodell är idag två av de främsta barriärerna för IT/OT integration. - Huvudfaktorer, så som förväntade uppfattningen av innovationen och företags omvandlingshastighet är av stor betydelse för att uppskatta spridningshastigheten av innovationen. - Mellanstora DSOer med höga ambitioner kommer troligast ta till sig tekniken tidigare än andra kundsegment och bör därför vara potentiell målgrupp för leverantörer, så som Ericsson. Rapporten bidrar till forskningen genom att en teknisk innovation analyserats i en komplex marknad. Vidare undersökningar som kan genomföras är att applicera motsvarande metodik för estimera spridningen av IT/OT integration i andra länder, då drivare och regleringar där kan skilja sig från Sverige.

Physical asset management in the electric power sector encompasses the scheduling of the maintenance and replacement of grid components, as well as decisions about investments in new components. Data plays a crucial role in these decisions. The importance of data is increasing with the transformation of the power system and its evolution toward smart grids. This thesis deals with questions related to data management as a way to improve the performance of asset management decisions. Data management is defined as the collection, processing, and storage of data. Here, the focus is on the collection and processing of data. First, the influence of data on the decisions related to assets is explored. In particular, the impacts of data quality on the replacement time of a generic component (a line for example) are quantified using a scenario approach, and failure modeling. In fact, decisions based on data of poor quality are most likely not optimal. In this case, faulty data related to the age of the component leads to a non-optimal scheduling of component replacement. The corresponding costs are calculated for different levels of data quality. A framework has been developed to evaluate the amount of investment needed into data quality improvement, and its profitability. Then, the ways to use available data efficiently are investigated. Especially, the possibility to use machine learning algorithms on real-world datasets is examined. New approaches are developed to use only available data for component ranking and failure prediction, which are two important concepts often used to prioritize components and schedule maintenance and replacement. A large part of the scientific literature assumes that the future of smart grids lies in big data collection, and in developing algorithms to process huge amounts of data. On the contrary, this work contributes to show how automatization and machine learning techniques can actually be used to reduce the need to collect huge amount of data, by using the available data more efficiently. One major challenge is the trade-offs needed between precision of modeling results, and costs of data management. / <p>QC 20210330</p>