-Deregulation and an increasing competition in electricity markets urge energy suppliers to optimize the utilization of their equipment, focusing on technical and cost-effective aspects.As a respond to these requirements utilities introduce methods formerly used by investment managers or insurance companies. The article describes the usage of these methods, particularly with regard to asset management and risk management within electrical grids. The essential information needed to set up an appropriate asset management system and differences between asset management systems in transmission and distribution systems are discussed.The bulk of costs in electrical grids can be found in costs for maintenance and capital depreciation. A comprehensive approach for an asset management in transmission systems thus focuses on the "life-cycle costs" of the individual equipment. The objective of the life management process is the optimal utilisation of the remaining life time regarding a given reliability of service and a constant distribution of costs for reinvestment and maintenance ensuring a suitable return.In distribution systems the high number of components would require an enormous effort for the consideration of single individuals. Therefore statistical approaches have been used successfully in practical applications. Newest insights gained by a German research project on asset management systems in distribution grids give an outlook to future developments.

For an optimized large-scale roll-out of EVs in Europe whilst at the same time maximizing the potential of DER integration, an optimized and enhanced grid architecture for EVs in Europe has to be considered. The work in this paper is addressing this topic and summarizing the corresponding project findings. The aim of this approach is to provide a framework for the further investigation of selected use cases which allows implementing and comparing scenarios of different DSOs. Following a Smart Grid approach, the developed grid architecture implements energy grid entities and ICT components. The general framework was described including all its relevant clusters and indicating related entities. The network types used for this architecture are following the SGAM and Smart Grid Standards Map approach. A so called "Smart Grid Connection Point", which is a generic system interface, is used in this work to allow a more simplified graphical architecture model and increase its readability. Similar to the concept and purpose of the Smart Grid Connection Point, also the principle of an integration bus for entity clusters was introduced. From the Integration bus, the information from/to external systems passes through the Smart Grid Connection Point using one of a range of possible technological options. The position of EVs charging infrastructure within the framework is defined at the border between the domains DERs (generation) and consumption, which takes into account future V2G scenarios, where EVs may act as consumption and generation devices. EVSEs and DERs may be connected as standalone systems directly to the grid, or indirectly as part of one of the clusters at the customer premises domain which refers to the three location-wise types of charging, public, semipublic and private charging. Regarding controlled charging of EVs this optimized architecture allows a variety of different local, distributed or aggregated options which may involve different types of actors.

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In times where the reduction of the yearly expenditures of utilities and therefore the optimal assignment of assets have a very high priority, the knowledge about future expenditures of the following 40 or 45 years is very useful. This paper shows a method to calculate the yearly capital and operational expenditures CAPEX and OPEX, respectively with the aid of ageing models consisting of different condition states. Furthermore, a procedure in order to calculate the required transition rates between successive condition states will be described, and the calculated values will be verified. The ageing model is useful for planning and operation of power systems under market conditions. The main advantage results from considering both the condition as well as the age of the assets in a power system. Therefore the asset manager receives lifelike results over a certain time horizon. The simulations will be performed by means of an appropriate system dynamics software.Index Terms-Ageing model, asset management, CAPEX, OPEX, planning and operation of power systems.

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