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Efficient and secure global supply chains contribute to the Improvement of the competitiveness of the products traded on international markets by reducing their costs and delivery time while increasing the reliability and security. Global supply chains are unthinkable without transport integration, which is usually accomplished through the form of intermodal transport systems. Intermodal transport systems are much more complex than the unimodal ones due to the number of stakeholders, included transportation resources, infrastructure and processes, which in case of poor coordination in the planning, organization and implementation of transport chain logistic activities can lead to increased supply chain vulnerability. Therefore, the main challenge in the functioning of intermodal transport operations in supply chains is to increase their efficiency taking into account the problems of associated risks. The current initiatives on the topic of identification and management of risks in the intermodal supply chains do not provide a complete and clear picture of the potential problems which the intermodal supply chains are exposed to. Hence, the purpose of this paper, which is based on the literature review of the model of the intermodal transport system structure and models of risk management in supply chains in general, is to provide a framework for a holistic Consideration of risks in intermodal supply chains, which can lead to the improvement of their efficiency and competitiveness.

In last period many distribution system operators (DSO) invest significant amount of money in smart metering system. Those investments are in part due to regulatory obligations and in part due to needs of DSO (utilities) for knowledge about electric energy consumption. Term electric energy consumption refers not only on real consumption of electric energy but also on data about peak power, unbalance, voltage profiles, power losses etc. Data which DSO can have depends on type of smart metering system. Further, smart meters as source of data can be implemented in transformer stations (TS) MV/LV and in LV grid at consumer level. Generally, smart meters can be placed in any node of distribution grid. As amount of smart meters is greater, the possibility of data analysis is greater. In this paper a smart metering system of J.P Elektroprivreda HZ HB d.d, Mostar, Bosnia and Herzegovina will be presented. One statistical approach for analyzing of advanced metering data of TS MV/LV will be presented. Statistical approach presented here is powerful tool for analyzing great amount of data from distribution grid in simple way. Main contribution of this paper is in using results obtained from statistical analysis of smart meter data in distribution grid analyzing and in maintenance/investment planning.

The paper describes the impact of the load increase caused by the connection of electric vehicle (EV) charging stations in the power distribution network. The power distribution network model was created using the professional software tool DIgSILENT PowerFactory (DPF). Analyses were carried out for the cases of connecting a different number of EV charging stations to 0.4 kV busbars of distribution transformer stations (TS). The results of voltage conditions and loading of the most loaded distribution substations are shown. Increasing the load results in an increase in losses in the network, which is particularly significant today since the prices of electrical energy on the market are currently at historically high levels. Distribution System Operator (DSO) should pay attention to the problem of losses increase in the network, and in addition to classic solutions in strengthening the network, the DSO should also consider modern solutions such as the flexibility of new consumers.

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