Energy poverty remains a significant issue in Bosnia and Herzegovina, characterized by limited access to affordable and sustainable energy sources. This paper examines the prevalence of energy poverty among 1500 retiree households and evaluates the potential of photovoltaic (PV) systems as a solution. The research highlights the multidimensional nature of energy poverty, incorporating variables such as income, energy expenditures, and heating methods. Using statistical methods, including factor analysis and regression models, the research developed an energy poverty index (EPI) to categorize households and identify key drivers of energy poverty. The findings reveal that 96.5% of households experience moderate to high energy poverty when transport costs are included, dropping to 84.3% when these costs are excluded. Households using wood for heating, with a combined rooftop area of 26,104 m2, could generate 7,831,200 kWh of solar energy annually, reducing CO2 emissions by 1,389,825 kg. The aggregated payback period for PV investments is approximately 9.3 years, demonstrating financial viability. The paper underscores the potential of energy communities in pooling resources, facilitating rooftop leasing for PV installations, and promoting policy reforms to promote renewable energy adoption. This research contributes to the understanding of energy poverty dynamics and provides actionable recommendations for integrating PV power plants, fostering energy equity, and reducing environmental impacts.
This paper presents a method based on Artificial Neural Networks (ANN) for magnetic flux density harmonics estimation in the vicinity of overhead lines. The proposed method can be employed for the magnetic flux density estimation in the cases of excitation currents with a pure sinusoidal waveform, as well as for excitation currents with harmonically distorted waveforms. The method utilizes two ANNs that are trained in a such way that enables their application for overhead lines of arbitrary phase conductor configurations and arbitrary current harmonic spectrum. In this paper, the proposed method is applied for magnetic flux density harmonics estimation in the vicinity of a typical distribution overhead line. The proposed method is validated by comparison with Biot-Savart based method. The obtained results demonstrate not only the accuracy and effectiveness of the proposed method but also the importance of considering the magnetic flux density harmonics in the vicinity of power facilities.
The building integrated photovoltaic (BIPV) systems are a popular option for integrating renewable energy sources in the power system, and for users to reduce energy bills. This paper analyzes the performance of inverters in BIPV systems with oversized PV configurations. Oversizing PV systems has become a common practice to optimize energy production, particularly in periods of low sunlight, but it raises concerns about efficiency, power quality, and potential economic implications. Performance analysis is performed on two inverters, one operating under an overloaded regime due to the oversized PV installation and another under normal conditions. Several performance metrics are compared, including efficiency, thermal behavior, THD, and economic factors. The results demonstrate that although oversizing can slightly increase the inverter’s temperature and affect power quality, the efficiency was better for the overloaded inverter, although the investment costs have increased. These results offer practical insights for designing PV systems, showing that oversizing can be beneficial if properly managed.
This paper introduces a novel method that leverages artificial neural networks to estimate magnetic flux density in the proximity of overhead transmission lines. The proposed method utilizes an artificial neural network to estimate the parameters of a mathematical model that describes the magnetic flux density distribution along the lateral profile for various configurations of overhead transmission lines. The training target data is acquired using the particle swarm optimization algorithm. A performance comparison between the proposed method and the Biot-Savart law-based method is conducted using an extensive test dataset. The resulting coefficient of determination and mean square error values demonstrate the successful application of the proposed method for a range of different spatial arrangements of phase conductors. Furthermore, the performance of the proposed method is thoroughly assessed on multiple test cases. The practical relevance of the proposed method is highlighted by contrasting its results with the field measurements obtained in the proximity of a 400 kV overhead transmission line.
Abstract This paper presents a detailed model of low-frequency oscillations and their damping within the Electric Power System (EPS) of Bosnia and Herzegovina (B&H). The system is modeled using MATLAB software, analysing the steady state and dynamic responses. This research highlights the challenges and impacts of integrating renewable energy sources, such as wind farms, on grid stability and oscillation damping. The paper utilizes eigenvalue analysis to investigate the dynamic characteristics of the system, emphasizing the need for efficient damping strategies to maintain system stability. The methodology includes a comprehensive review of existing literature, the creation of a detailed EPS model of B&H, and the application of eigenvalue and oscillation amplitude analysis to determine damping ratios. The dynamic responses of hydro power plants, HPP Mostar and HPP Jablanica, to transient disturbances are analysed to validate the model and refine damping strategies. The results indicate that the B&H EPS is well-damped, with all eigenvalues possessing negative real parts, and demonstrate the system’s resilience to small disturbances. The results are compared with the technical report on the integration of the wind power plant WPP Podveležje. This comparative analysis shows consistent patterns between the modeled calculations and empirical data, confirming the robustness of the EPS model. This alignment underscores the effectiveness of current damping mechanisms and provides a foundational strategy for enhancing system stability with increasing renewable energy penetration. The findings highlight the importance of developing advanced control strategies to sustain system stability as the integration of variable renewable energy sources continues to grow.
Abstract The methodology for the evaluation of long-term exposure to the overhead line magnetic field is presented, in this paper. The developed methodology is based on the ambient temperature measurements and phase conductors’ height measurements to find a linear regression model to determine phase conductors’ height changes for different ambient temperatures. Based on the overhead transmission line geometry, and datasets about historical overhead line phase current intensity values and ambient temperatures long-term magnetic field exposure can be determined. For magnetic flux density determination, a method based on artificial neural networks is used. The methodology is applied to the case study of overhead line that connect substations Sarajevo 10 and Sarajevo 20. A period of one year is analyzed and magnetic flux density values are determined. The obtained results indicate that during the analyzed period for significant amounts of time magnetic flux density values surpass the recommended values for long-term exposure.
Abstract This paper presents an artificial neural network (ANN) based method for overhead lines magnetic flux density estimation. The considered method enables magnetic flux density estimation for arbitrary configurations and load conditions for single-circuit, multi-circuit, and also overhead lines that share a common corridor. The presented method is based on the ANN model that has been developed using the training dataset that is produced by a specifically designed algorithm. This paper aims to demonstrate a systematic and comprehensive ANN-based method for simple and effective overhead lines magnetic flux density estimation. The presented method is extensively validated by utilizing experimental field measurements as well as the most commonly used calculation method (Biot - Savart law based method). In order to facilitate extensive validation of the considered method, numerous magnetic flux density measurements are conducted in the vicinity of different overhead line configurations. The validation results demonstrate that the used method provides satisfactory results. Thus, it could be reliably used for new overhead lines’ design optimization, as well as for legally prescribed magnetic flux density level evaluation for existing overhead lines.
The decrease in overall inertia in power systems due to the shift from synchronous generator production to renewable energy sources (RESs) presents a significant challenge. This transition affects the system’s stable frequency response, making it highly sensitive to imbalances between production and consumption, particularly during large disturbances. To address this issue, this paper introduces a novel approach using Multivariate Empirical Mode Decomposition (MEMD) for the accurate estimation of power system inertia. This approach involves applying MEMD, a complex signal processing technique, to power system frequency signals. The study utilizes PMU (Phasor Measurement Unit) data and simulated disturbances in the IEEE 39 bus test system to conduct this analysis. MEMD offers substantial advantages in analyzing multivariate data and frequency signals during disturbances, providing accurate estimations of system inertia. This approach enhances the understanding of power system dynamics in the context of renewable energy integration. However, the complexity of this methodology and the requirement for precise data collection are challenges that need to be addressed. The results from this approach show high accuracy in estimating the rate of change of frequency (RoCoF) and system inertia, with minimal deviation from actual values. The findings highlight the significant impact of renewable energy integration on system inertia and emphasize the necessity of accurate inertia estimation in modern power systems.
In this paper, a comparative analysis of different methods for magnetic induction estimation in the vicinity of overhead power lines is presented. The methods for determining magnetic induction, considered in the paper, include the recently proposed artificial neural network based method and the Biot-Savart law based method. In addition, the paper considers a method that employs the genetic algorithm to fit a considered mathematical model to the field measurements. The performance of various methods is evaluated on an actual 400 kV overhead power line. The method based on the artificial neural networks is able to accurately evaluate magnetic induction values along the lateral profile without relying on field measurements using only the description of power line conductor configuration and the current intensity value.
The paper presents an algorithm for determining the optimal connection location and power of a photovoltaic plant in a distribution network. The proposed algorithm is based on the use of the fuzzy logic and power flow calculation method. The fuzzy logic is used for the selection of candidate buses for the photovoltaic plant connection, while load flow analysis is used for the verification of voltage conditions and power losses in the distribution network. For each of the candidate buses photovoltaic plant of a certain power range was considered. The practical application of the considered algorithm was demonstrated on a part of Sarajevo's 10 kV distribution network.
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