Journal of Energy - Energija

Empirical Assessment of Wind Power’s Capacity Credit: A European Case Study

Dubravko Sabolić, Igor Ivanković — Wed, 23 Jul 2025 00:00:00 +0200

This paper presents an empirical analysis of the intrinsic capacity credit of onshore wind power across twelve European countries, based on hourly measured data for 2019 and 2024. Capacity credit was calculated as a function of acceptable default risk, under the assumption that wind power alone must satisfy total demand. The results show a wide range of outcomes across individual countries and highlight the effects of spatial aggregation, where the combined regional system performs better than its constituents. A nonlinear model was fitted to describe the relationship between capacity credit and risk probability. Additionally, several hypothetical spatial distributions of installed capacity were evaluated using statistical criteria, illustrating how coordination could affect adequacy outcomes. The analysis was based exclusively on observed data, without additional modeling assumptions, and the proposed method is generalizable to other generation technologies and planning scenarios.

Arc Flash Calculation for 110/20 kV HV/MV Substation

Viktor Milardić, Amir Tokić, Anamari Nakić — Wed, 24 Sep 2025 00:00:00 +0200

This paper presents arc flash calculation for 110/20 kV HV/MV substation that follows IEEE 1584:2018 Standard. The IEEE 1584:2018 standard has been completely revised compared to the IEEE 1584:2002 standard, and according to the IEEE 1584:2002 standard, the calculation of an electric arc can no longer be performed. The paper shows how calculating the risk assessment to identify potential electrical hazards and implement appropriate safety measures and ultimately to protect workers from arc-flash hazards. Arc flash calculation for the case of real 110/20 kV substation is shown.

Accurate Photovoltaic Power Forecasting in 5G Networks: A Novel Neural Network Approach

Mohammed Moyed Ahmed — Wed, 24 Sep 2025 00:00:00 +0200

This study addresses the challenge of integrating photovoltaic (PV) power generation into 5G base stations to reduce energy consumption. A novel improved Firefly Algorithm-Back Propagation (IFA-BP) neural network model is proposed for enhanced PV power prediction accuracy. The methodology combines Circle chaos mapping for optimized population initialization with nonlinear mutational perturbation to strengthen global search capabilities. Critical input parameters are selected through grey correlation analysis to improve model efficiency. Comparative analysis with conventional BP and FA-BP models is conducted using historical operational data from 5G base station installations. Experimental results across diverse weather conditions demonstrate the model’s superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.7943% and coefficient of determination (R2) of 0.9895 under optimal solar conditions. The system maintains robust prediction capability in challenging environments, yielding a MAPE of 12.1988% and R2 of 0.9793 during cloudy weather. These findings highlight the model’s effectiveness in stabilizing power supply systems for 5G infrastructure while optimizing renewable energy utilization.

Model-Based Comparison of Nuclear and Renewable Energy Based Strategies for Slovenia

Mon, 29 Sep 2025 00:00:00 +0200

Decarbonizing the primary energy is essential for a more sustainable energy supply, requiring a shift away from fossil fuels. To address the climate crisis, significant changes in energy use and supply are necessary, with a focus on reducing electricity generation based on fossil fuels. In the future, coal and gas plants should serve as strategic reserves, operating only during emergencies or other critical situations. Other technologies must replace the lost capacity and meet growing energy demand. By 2050, Slovenia's energy mix is expected to include onshore wind, solar photovoltaic, hydropower, gas plants, coal plants, and nuclear power with pressurized water reactors. Each has a distinct role, requiring careful integration based on environmental and socio-geographic factors.
At the Faculty of Mechanical Engineering, University of Ljubljana, we have developed a dimensionless model of the energy system that allows for the study of various technologies for electricity supply. Using hourly demand data and boundary conditions like primary energy availability and plant technical characteristics, the model simulates energy flows from nuclear, thermal, solar, wind, and hydropower sources, including pumped-hydro storage.
Although initially developed for educational purposes, the model, when provided with appropriate boundary conditions, enables studies of real-world energy systems. Based on reference scenarios derived from national energy and climate strategies, we will analyze the operation of proposed energy systems. The focus will be on two fundamental scenarios from Slovenia's National Energy and Climate Plan (NEPN): one based exclusively on renewable energy sources. and another based on a combination of nuclear and renewable energy sources. A comprehensive analysis of system performance will be conducted, considering technical, environmental, and socio-economic aspects. The primary evaluation criteria will include system stability, greenhouse gas emissions, EROI indicators, import/export dependency, energy costs, and other environmental indicators, such as spatial and material efficiency of the system.