1. Dynamic modeling and experimental validation of the photovoltaic/thermal systemKlemen Sredenšek, Eva Simonič, Klemen Deželak, Marko Bizjak, Niko Lukač, Sebastijan Seme, 2025, original scientific article Abstract: The aim of this paper is to present a novel and comprehensive methodology for the dynamic modeling and experimental validation of a photovoltaic/thermal system. The dynamic model is divided into thermal and electrical subsystems, encompassing the photovoltaic/ thermal module and the thermal energy storage. The thermal subsystem of both the photovoltaic/thermal module and the thermal energy storage is described by a one-dimensional dynamic model of heat transfer mechanisms and optical losses, while the electrical subsystem is presented as an electrical equivalent circuit of double diode solar cell. Model validation was conducted on a modern experimental photovoltaic/thermal system over an extended operational period at a five-minute resolution, with validation days classified as sunny, cloudy, or overcast based on weather conditions, thereby demonstrating an applied approach. The results demonstrate the lowest deviation values reported to date, confirmed using six quantitative indicators. The added value of the proposed methodology, not previously addressed in the literature, lies in the following contributions: (i) comprehensive modeling of the entire photovoltaic/thermal system, (ii) accurate consideration of optical losses in the photovoltaic/thermal module, and (iii) long-term experimental validation. Overall, the proposed methodology provides a reliable and efficient framework for PV/T system design, optimization, and long-term performance assessment.
Keywords: photovoltaic/thermal system, thermal energy storage, dynamic modeling, experimental validation, heat transfer mechanism, temperature, electrical power
Published in DKUM: 10.11.2025; Views: 0; Downloads: 4
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2. Performance Enhancement of Grid Connected Multilevel Inverter Based Wind Energy Conversion System with LVRT Capability Using Optimized Type 2 ANFIS Based DVRCh. Sajan, P. Satish Kumar, Peter Virtič, 2024, original scientific article Abstract: A Permanent Magnet Synchronous Generator (PMSG) based Wind Energy Conversion System (WECS) holds significant importance in the realm of Renewable Energy Sources (RES) for several reasons. The permanent magnets in the generator eliminate the need for a separate excitation system, leading to improved efficiency in power conversion. This makes PMSG-based WECS an effective and reliable source of wind energy electricity. The motivation behind the proposed conceptual framework stems from the need to overcome the limitations related to the integration of RES into the power grid, specifically focusing on voltage stability and Low Voltage Ride Through (LVRT) capability of PMSG based WECS. A Dynamic Voltage Restorer (DVR), empowered by an energy storage device, is used to mitigate voltage fluctuations and disturbances. The input DC voltage to the DVR is intricately regulated by a Type 2 Adaptive Neuro Fuzzy Inference System (ANFIS) Controller optimized using the Seagull algorithm, exhibiting intelligent adaptability to dynamic conditions. The rectified output from the WECS transforms an Isolated Flyback converter. Subsequently, a 31-Level Cascaded H-Bridge Multilevel Inverter (CHBMLI) along with a Proportional-Integral (PI) controller aids in generating high-quality AC output. By addressing challenges related to voltage stability and the ability to ride through low-voltage conditions, the proposed work contributes to enhanced grid stability. The use of advanced control techniques, including the Type 2 ANFIS Controller optimized by the Seagull algorithm, adds a layer of intelligent adaptability to changing environmental and grid conditions. A lower Total Harmonic Distortion (THD) Value of 1.29% is shown during the validation of the created system utilizing MATLAB/Simulink, assuring significant LVRT capabilities.
Keywords: Permanent Magnet Synchronous Generator (PMSG), Wind Energy Conversion System (WECS), Renewable Energy Sources (RES), Low Voltage Ride Through (LVRT), Type 2 Adaptive Neuro Fuzzy Inference System, 31-Level CHBMLI, Proportional-Integral (PI)
Published in DKUM: 06.11.2025; Views: 0; Downloads: 1
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3. Research on the modelling and analysis of the penetration of renewable sources and storage into electrical networksEva Simonič, Sebastijan Seme, Klemen Sredenšek, 2025, original scientific article Abstract: To address the growing integration of renewable energy sources and storage systems into distribution networks, there is a need for effective tools that can assess the impact of these technologies on grid performance. This paper investigates the impact of integrating residential rooftop photovoltaic (PV) systems and battery energy storage systems (BESSs) into low-voltage (LV) distribution networks. A stochastic approach, using the Monte Carlo method, is applied to randomly place PV systems across the network, generating multiple scenarios for power flow simulations in MATLAB Simulink R2024b. The method incorporates real-world consumer load data and grid topology, representing a novel approach in simulating distribution network behaviour accurately. The novelty of this paper lies in its ability to combine stochastic PV placement with real-world load data, providing a more realistic representation of network conditions. The simulation results revealed that widespread PV deployment can lead to overvoltage issues, but the integration of BESSs alongside PV systems mitigates these problems significantly. The findings of this paper offer valuable insights for Distribution Network Operators, aiding in the development of strategies for optimal PV and BESS integration to enhance grid performance.
Keywords: photovoltaic system, battery energy storage system, low-voltage distribution network, Monte Carlo method, power flow
Published in DKUM: 03.11.2025; Views: 0; Downloads: 7
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4. Simulation-based study of structural changes in electrical time-series signalsLuka Živković, Željko Hederić, Tin Benšić, Goran Kurtović, Marinko Stojkov, 2025, original scientific article Abstract: his paper uses statistical indicators to address the detection of changes in electrical signals typical of industrial and power systems. A dedicated MATLAB algorithm was developed to identify change points by tracking shifts in signal behaviour and statistical properties. To evaluate the method, synthetic signals were generated through simulation to reproduce the common patterns observed in these systems, allowing testing under different operating conditions and varying noise levels. The results demonstrate that the algorithm detects change points reliably across multiple scenarios, showing flexibility and robustness. This study highlights the value of simulation-based signal generation as a controlled environment for testing detection methods. It provides a foundation for future applications to more complex real-world electrical signal analysis tasks.
Keywords: break points, energy system, noise, segmentation, signals, simulation, time series
Published in DKUM: 01.10.2025; Views: 0; Downloads: 1
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5. Open-source transformer-based information retrieval system for energy efficient robotics related literatureTine Bertoncel, 2025, original scientific article Abstract: Background and Purpose: This article employs the Hugging Face keyphrase-extraction-kbir-inspec machine learning model to analyze 654 abstracts on the topic of energy efficiency in systems and control, computer science and robotics. Methods: This study targeted specific arXiv categories related to energy efficiency, scraping and processing ab - stracts with a state-of-the-art Transformer-based Hugging Face AI model to extract keyphrases, thereby enabling the creation of related keyphrase networks and the retrieval of relevant scientific preprints. Results: The results demonstrate that state-of-the-art open-source machine learning models can extract valuable information from unstructured data, revealing prominent topics in the evolving field of energy-efficiency. Conclusion: This showcases the current landscape and highlights the capability of such information systems to pinpoint both well researched and less researched areas, potentially serving as an information retrieval system or early warning system for emerging technologies that promote environmental sustainability and cost efficiency.
Keywords: energy efficiency, keyphase extraction, early warning system, information system, semantic network, transformer models, industry 4.0
Published in DKUM: 07.08.2025; Views: 0; Downloads: 3
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6. The impact of financial support mechanisms and geopolitical factors on the profitability of investments in solar power plants in SloveniaIztok Gornjak, Filip Kokalj, Niko Samec, 2024, original scientific article Abstract: This article examines the impact of financial support mechanisms and geopolitical factors on the profitability of investments in solar power plants within Slovenia. The European Union’s energy policy prioritizes increases in renewable energy sources, aiming to reduce dependency on unstable and volatile fossil fuel markets. Solar power plants play a vital role in this transition. The energy policy framework also includes mechanisms and support systems to operate such facilities. This article analyzes electricity price trends over the past decade and addresses which support type—guaranteed purchase or operational support—has proven more profitable for investments in solar power plants up to 50 kW in Slovenia, considering economic and geopolitical influences on the electricity market. Although the global energy market has been affected by various significant events in recent years, it was found that the COVID-19 pandemic had minimal impact on the electricity market. In contrast, the onset of the conflict in Ukraine has contributed to rising electricity prices and has influenced the support dynamics essential for the development and sustainability of renewable energy systems. Analyses from the past decade indicate a higher return on investment in solar power plants when operational support mechanisms are chosen over guaranteed purchase support.
Keywords: renewable energy sources, solar power plants, support system, investment profitability factors, electricity price
Published in DKUM: 16.12.2024; Views: 0; Downloads: 13
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7. Quantifying power system frequency quality and extracting typical patterns within short time scales below one hourYounes Mohammadi, Boštjan Polajžer, Roberto Chouhy Leborgne, Davood Khodadad, 2024, original scientific article Abstract: This paper addresses the lack of consideration of short time scales, below one hour, such as sub-15-min and sub1-hr, in grid codes for frequency quality analysis. These time scales are becoming increasingly important due to
the flexible market-based operation of power systems as well as the rising penetration of renewable energy
sources and battery energy storage systems. For this, firstly, a set of frequency-quality indices is considered,
complementing established statistical indices commonly used in power-quality standards. These indices provide
valuable insights for quantifying variations, events, fluctuations, and outliers specific to the discussed time
scales. Among all the implemented indices, the proposed indices are based on over/under frequency events (6
indices), fast frequency rise/drop events (6 indices), and summation of positive and negative peaks (1 index), of
which the 5 with the lowest thresholds are identified as the most dominant. Secondly, k-means and k-medoids
clustering methods in a learning scheme are employed to identify typical patterns within the discussed time
windows, in which the number of clusters is determined based on prior knowledge linked to reality. In order to
clarify the frequency variations and patterns, three frequency case studies are analyzed: case 1 (sub-15-min scale,
10-s values, 6 months), case 2 (sub-1-hr scale, 10-s values, 6 months), and case 3 (sub-1-hr, 3-min values, the
year 2021). Results obtained from the indices and learning methods demonstrate a full picture of the information
within the windows. The maximum value of the highest frequency value minus the lowest one over the windows
is about 0.35 Hz for cases 1 and 2 and 0.25 Hz for case 3. Over-frequency values (with a typical 0.1% threshold)
slightly dominates under-frequency values in cases 1 and 2, while the opposite is observed in case 3. Medium
fluctuations occur in 35% of windows for cases 1 and 2 and 41% for case 3. Outlier values are detected using the
quartile method in 70% of windows for case 2, surpassing the other two cases. About six or seven typical patterns
are also extracted using the presented learning scheme, revealing the frequency trends within the short time
windows. The proposed approaches offer a simpler alternative than tracking frequency single values and also
capture more comprehensive information than existing approaches that analyze the aggregated frequency values
at the end of the specific time windows without considering the frequency trends. In this way, the network
operators have the possibility to monitor the frequency quality and trends within short time scales using the most
dominant indices and typical patterns.
Keywords: quantifying power system frequency quality, statistical indices, pattern extracting, machine learning, short time scales, renewable energy sources
Published in DKUM: 23.08.2024; Views: 50; Downloads: 17
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8. Towards productive and ergonomic order picking : multi-objective modeling approachBrigita Gajšek, Simona Šinko, Tomaž Kramberger, Marcin Butlewski, Eren Özceylan, Goran Đukić, 2021, original scientific article Abstract: The logistics sector should strive for sustainability alongside productivity by protecting its order pickers' health and welfare. Existing storage assignment models are mainly based on the criterion of order picking time and, to a lesser extent, the human factor. In the paper, a solution to a storage assignment problem using a multi-objective model based on binary integer linear programing is presented by developing a solution that considers order picking time, energy expenditure and health risk. The Ovako Working Posture Assessment System (OWAS) method was used for health risk assessment. The downside of solely health risk-optimization is that the average order picking time increases by approximately 33 % compared to solely time-optimization. Contrary to this, the developed multi-objective function emphasizing time has proven to be promising in finding a compromise between the optimal order picking time and eliminating work situations with a very-high risk for injuries. Its use increases the time by only 3.8 % compared to solely time-optimization while significantly reducing health risk.
Keywords: productivity, energy expenditure, order picking, order picking system, health risk, OWAS, multi-objective modeling, planning, logistics, ergonomics
Published in DKUM: 13.08.2024; Views: 109; Downloads: 12
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9. Comparative analysis of batteries for photovoltaic systems : bachelor's thesisKristina Gočanin, 2023, undergraduate thesis Abstract: Renewable energies represent unlimited power sources that are significantly reducing greenhouse emissions. The possibilities and advantages of abundantly available solar energy are immense. However, some challenges are preventing solar energy from reaching its full potential regarding its efficiency and energy storage. Therefore, the main focus of this thesis is on electrochemical storage systems. The aim is to compare the currently leading technology – Li-ion battery to the most recent breakthrough in storage systems – the solid-state battery. The thesis includes a comparative analysis of the mentioned technologies, as well as the theoretical part that introduces solar energy and photovoltaic systems with their main components.
Keywords: photovoltaic system, energy storage, analysis, Li-ion battery, solid-state battery
Published in DKUM: 30.01.2024; Views: 457; Downloads: 54
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10. Solar photovoltaic tracking systems for electricity generation : a reviewSebastijan Seme, Bojan Štumberger, Miralem Hadžiselimović, Klemen Sredenšek, 2020, review article Abstract: This paper presents a thorough review of state-of-the-art research and literature in the field of photovoltaic tracking systems for the production of electrical energy. A review of the literature is performed mainly for the field of solar photovoltaic tracking systems, which gives this paper the necessary foundation. Solar systems can be roughly divided into three fields: the generation of thermal energy (solar collectors), the generation of electrical energy (photovoltaic systems), and the generation of electrical energy/thermal energy (hybrid systems). The development of photovoltaic systems began in the mid-19th century, followed shortly by research in the field of tracking systems. With the development of tracking systems, di%erent types of tracking systems, drives, designs, and tracking strategies were also defined. This paper presents a comprehensive overview of photovoltaic tracking systems, as well as the latest studies that have been done in recent years. The review will be supplemented with a factual presentation of the tracking systems used at the Institute of Energy Technology of the University of Maribor.
Keywords: solar energy, photovoltaic tracking system, tracking strategies, drive system, degree of freedom
Published in DKUM: 15.11.2023; Views: 436; Downloads: 45
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