Faculty of Electrical Engineering and Information Technologies

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    Indoor–Outdoor Particulate Matter Monitoring in a University Building: A Pilot Study Using Low-Cost Sensors
    (MDPI AG, 2026-01-30)
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    Krkoleva Mateska, Aleksandra
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    Celeska Krstevska, Maja
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    Panovski, Maksim
    Sustainable management of indoor and outdoor air quality is essential for protecting public health, enhancing well-being, and supporting resilient urban environments. Low-cost air quality sensors enable continuous, real-time monitoring of key pollutants and, when combined with data analytics, provide scalable and cost-effective insights for smart building operation and environmental decision-making. This pilot study evaluates an indoor–outdoor air quality monitoring system deployed at the Faculty of Electrical Engineering and Information Technologies in Skopje, with a focus on: (i) PM2.5 and PM10 concentrations and their relationship with meteorological conditions and human occupancy; (ii) sensor responsiveness and reliability in an educational setting; and (iii) implications for sustainable building operation. From January to March 2025, two indoor sensors (a classroom and a faculty hall) and two outdoor rooftop sensors continuously measured PM2.5 and PM10 at one-minute intervals. All sensors were calibrated against a reference instrument prior to deployment, while meteorological data were obtained from a nearby station. Time-series analysis, Pearson correlation, and multiple regression were applied. Indoor particulate levels varied strongly with occupancy and ventilation status, whereas outdoor concentrations showed weak to moderate correlations with meteorological variables, particularly atmospheric pressure. Moderate correlations between indoor and outdoor PM suggest partial pollutant infiltration. Overall, this pilot study demonstrates the feasibility of low-cost sensors for long-term monitoring in educational buildings and highlights the need for adaptive, context-aware ventilation strategies to reduce indoor exposure.
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    Distributed generation placement in distributive substations analysis using Markov Chain Monte Carlo model considering the reliability of power supply
    (IOP Publishing, 2020-06-01)
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    <jats:title>Abstract</jats:title> <jats:p>Proper operation of the power substations is of great importance for power network reliability, stability and uninterrupted power supply. Distributed generation provides higher reliability in power supply, but still, there are contingencies in the electric power production and supply process, which lead to outages in the power supply. In this paper, a method for substations’ reliability estimation with distributed generation is presented based on Markov Chain Monte Carlo method. The method considers the possible substation operation states and using random number generator in MATLAB, it simulates faults and calculates the substations’ reliability. The method is demonstrated on two cases of 110/35 kV substations, each consisting of two transformers and distributed generator, analysing the best placement for the distributed generation.</jats:p>
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    Оптимално управување со токови на енергија во микромрежи приклучени на дистрибутивната мрежа
    (2022-09)
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    Zenku, Imer
    The increased penetration of renewable energy sources, as well as the higher standards for quality electricity, actualize the implementation of microgrids into the standard power system. Microgrids provide consumers with higher reliability of power supply, and characterize with lower costs and lower power losses. Microgrids consist of many different distributed generators, more interconnected consumers, batteries and backup generators, and therefore optimization is mandatory for proper and balanced operation of the microgrid. The paper analyses the problem of optimal energy management in a hybrid system (microgrid), which consists of a photovoltaic and wind generators, connected to the distribution network. Also, the microgrid includes industrial prosumers. An upgraded genetic algorithm is applied in the optimization, which takes into account the technical limitations of the installed equipment and the voltage levels in the nodes.
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    Optimization of Grid-Connected Microgrids with Residential Prosumers Using an Improved Genetic Algorithm
    (2024)
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    With the continuous increase of the microgrids’ implementation into the power system the problem of maintaining their stability and balance arises and the necessity to adopt an appropriate energy management system emerges. This paper analyses the optimization of the grid-connected microgrid, which consists of a photovoltaic generator, a wind generator, a battery, and residential prosumers. The paper presents the application of an improved genetic algorithm, which takes into consideration the voltage levels on the connection points of the generators and the prosumers, as well as the trading with the local grid. The proposed algorithm suggests the usage of the standard genetic algorithm with the improvement in the fitness and selection process. The results of the simulation are compared with the results obtained when using a standard genetic algorithm with five different types of selection.
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    Modified genetic algorithm for unit commitment of grid connected microgrids under real time pricing conditions
    (International Journal on Information Technologies and Security, 2024-09-01)
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    <jats:p>This paper introduces a modification of the genetic algorithm aimed at enhancing the selection process for reproducing the next generation. This modification accelerates the optimization process and improves the outcome. The case study analyzes a grid-connected microgrid comprising renewable energy sources, a battery storage system, prosumers with installed photovoltaic generators, and consumers. The effectiveness of the proposed modification is validated through comparison with two selection algorithms commonly used in the standard genetic algorithms.</jats:p>