In this work, a new method for identifying the optimal route of electric vehicles based on a hybrid model is offered. A combination of analytical hierarchical method and fuzzy dynamic programming was used to create the hybrid model. The model designed in this manner allows each user of an electric vehicle to decide the ideal route based on their needs. The case study was created using actual data from the city of Nis. The scenarios for two electric vehicle users for the schedule of charging stations and routes for the city of Nis with pre-defined values of travel time, charging time, and the attractiveness of the location are analyzed. Based on the acquired findings, all of the suggested model's advantages can be seen, which are principally reflected in bringing electric vehicle users to the forefront, i.e., harmonizing electric vehicle users' needs on the one hand and travel length and charging time at charging stations on the other.

One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.

Vehicle-to-grid presents a mechanism to meet the key requirements of an electric power system, using electric vehicles (EVs) when they are parked. The most economic ancillary service is that of frequency regulation, which imposes some constraints regarding the period and duration of time the vehicles have to be connected to the grid. The majority of research explores the profitability of the aggregator, while the perspective of the EV fleet owner, in terms of their need for usage of their fleet, remains neglected. In this paper, the optimal allocation of available vehicles on a day-ahead basis using queuing theory and fuzzy multi-criteria methodology has been determined. The proposed methodology is illustrated on the daily scheduling of EVs in an electricity distribution company.

In this paper, the key performance indicators related to the smart grid efficiency, as the key factor of any energy management system implementation have been analysed. The authors are proposing multi-criteria fuzzy AHP methodology for the determination of overall smart grid efficiency. Four criteria (technology, costs, user satisfaction, and environmental protection) and seven performances (according to EU and US initiatives for analysis of benefits and effects of smart grid systems) for the selection of optimal smart grid project are defined. The analysis shows that the dominant performances of the optimal smart grid project are efficiency, security and quality of supply. The methodology is illustrated on the choice of smart grid development strategy for the medium size power distribution company.