Polityka Energetyczna - Energy Policy Journal

In recent years, the increase in the share of unstable energy sources classified as renewable energy sources, wind turbines, and photovoltaic installations in the national (Polish) energy mix has led to the emergence of new challenges. These challenges include what to do with excess energy production in the face of low prices and low storage capacities. The solution used in 2023 and 2024 was the forced shutdown of sources on the scale of the national power system. A gap was noticed in the methodology for assessing which installations (wind turbines or photovoltaics) should be shut down on the scale of one power connection in the so-called cable pooling cooperation formula. In this respect, a decision-making methodology was developed for shutting down generating capacity based on operating costs. Scenarios for the installed capacity of individual sources were defined: photovoltaic installations and wind turbines. Then, an analysis of scenarios of different capacities of individual farms was performed compared to one side of the power connection. It was proven that in most cases of cooperation between photovoltaic farms and wind farms, wind farms should be shut down first in the event of exceeding the capacity from the point of view of one owner for one connection for these farms. The results also voiced the discussion on compensation for installation shutdowns on the scale of the entire power system in Poland. Nevertheless, the estimates in this area have a wide area for in-depth analyses for individual sources, taking into account various conditions, e.g., age of devices, local conditions.

Energy storage plays a critical role in stand-alone hybrid renewable energy systems, ensuring a stable and reliable power supply in rural areas disconnected from the electrical grid. This study integrates batteries and a backup diesel generator to maintain continuous energy availability. A computer-based monitoring and control unit is developed to optimize system performance, increase efficiency, and enhance sustainability. The experimental setup consists of photovoltaic (PV) panels, a diesel generator (DG), a battery bank (B), a charge controller, a DC/AC inverter, and a variable electrical load. The control unit, designed using LabView software, prevents battery over-discharge, thereby improving longevity and overall efficiency. It comprises voltage and current sensors connected to data acquisition cards (DAC) linked to a PC. The system features twelve 12 V PV panels, each rated at 75 W, supplying a total of 450 W. The experimental findings indicate that battery capacity declines significantly under high discharge currents, emphasizing the necessity of effective energy management strategies. The control unit continuously monitors system parameters and regulates the operation of the backup generator. When the battery’s state of charge (SOC) drops to 35%, the system automatically disconnects the load and activates the DG, ensuring an uninterrupted power supply while prolonging the battery lifespan. These results highlight the importance of selecting suitable battery sizes and characteristics, considering both cost and load demands, to enhance the performance, reliability, and economic feasibility of hybrid renewable energy systems for off-grid applications.

Investments in the energy sector are the primary driver of its growth. Thus, trends in capital inflows in developing countries are a relevant research area. The purpose of the study is to assess investment opportunities in Azerbaijan’s oil industry while considering its role in the national economy and current state. The research examines how oil export revenues correlate with GDP growth in Azerbaijan, highlighting the industry’s significance for economic development while acknowledging the challenges of dependency on oil exports. In particular, the relationship between the level of exports in Azerbaijan and the gross domestic product (GDP) was assessed, and the elasticity of these metrics was found. This study examined the current situation both in the world in general and in Azerbaijan in particular. The study demonstrated that the investment landscape, particularly in the energy sector, has undergone a marked change, driven by economic growth and government support, particularly in regions such as Europe, the United States, and China. This surge in investment is also determined by the urgent need for the world to switch to renewable energy. The study noted that SOCAR has become the country’s leading oil supplier, accounting for most of Azerbaijan’s total crude oil exports. Developing its operations in international markets and establishing productive economic relations is one of the company’s strategic priorities. Given that oil and gas resources play an important role in Azerbaijan’s economy, the regression method revealed a close relationship between oil exports and GDP.

This article explores the principles of an integrated approach to enhance the efficiency of renewable energy utilization for small-scale, decentralized consumers, with a particular focus on Kazakhstan. The significance of this research lies in addressing the challenges faced by these consumers, including limited financial and technological resources, and proposing solutions that can reduce reliance on centralized energy systems, foster energy autonomy, and minimize environmental impacts. The study employs a multifaceted approach encompassing analytical, classification, functional, statistical, and synthesis methods to assess the effectiveness of renewable energy sources (RES), such as wind and solar power, in decentralized energy systems. Specifically, it identifies Kazakhstan’s potential for wind energy, which exceeds solar energy in capacity, and regions with substantial renewable energy potential, such as Kyzylorda, North Kazakhstan, and Zhambyl. The economic assessments indicate that wind and solar power are cost-effective, with the electricity produced from wind stations being particularly competitive. The findings emphasize the potential for wind and solar power to meet a substantial proportion of the electricity demand in various regions, with wind farms having the capacity to satisfy entire regional needs. The study concludes that an integrated approach that combines technological, economic, and social factors can substantially enhance energy efficiency, decrease environmental footprints, and contribute to the sustainable development of local communities.

The objective of the article is to estimate the impact of energy commodity prices, particularly oil, coal, and natural gas, on disaggregated inflation in Poland. The empirical part was based on multiple regression models constructed using simple OLS methods, providing short-run analysis. The study focused on inflation in the areas of food, housing, transportation, and core inflation. The study revealed that energy commodity prices have a significant impact on three out of four considered inflation categories, with the nature of this interaction varying across sectors. Coal and natural gas prices had a substantial effect on housing sector inflation (ECPI), while oil prices exerted the most significant influence on transportation sector inflation (TCPI). None of the variables affected inflation in the food area. This type of differentiation underscores the importance of disaggregated inflation analysis. Macroeconomic control variables, such as the output gap, exchange rate, and interest rates, also played a crucial role in the model. A positive output gap increases inflationary pressure, and exchange rates, particularly the real effective exchange rate (REER), can suppress price growth, especially in import-dependent sectors like transportation. Interest rates also had a pronounced impact on inflation, highlighting their role as a monetary policy tool. The study contributes to the development of knowledge on the impact of energy commodity prices, particularly oil, coal, and natural gas, on disaggregated inflation in Poland.

The study is conducted to optimize technologies for automated control of heat supply systems based on renewable energy sources that can increase energy efficiency and reduce environmental impact. The study uses machine learning methods for predicting heat energy consumption, intelligent monitoring and diagnostics systems, and control automation algorithms to optimize the operation of heat supply systems based on renewable energy sources. As a result of the study, an automated heat supply management system based on renewable energy sources is analyzed, which demonstrated high energy efficiency and flexibility in operation. The use of intelligent algorithms allows optimising the distribution of heat energy, considering fluctuations in weather conditions and loads. Automation of control processes reduces operating costs and minimizes human intervention. It is also established that the integration of solar collectors and geothermal sources into a single system reduces dependence on traditional energy sources and carbon dioxide emissions. The study shows that optimizing the use of renewable sources with automated control not only increases the reliability of heat supply but also contributes to reducing operating costs in comparison with traditional systems. This confirms the prospects of such technologies for broad application in municipal and industrial heat supply systems. In addition, it is determined that automated control systems contribute to more accurate forecasting of thermal energy needs, which reduces the risk of overloads and interruptions in heat supply. The study also shows that the use of combined sources of renewable energy, such as solar and geothermal installations, increases the overall efficiency of the system.

This study explores the potential of olive stones as a renewable biofuel for small-scale heating systems. Olive oil production generates approximately 4 million tonnes of olive stones annually, often classified as waste. By analyzing their elemental and physical properties, this research evaluates the energy potential of olive stones, offering a sustainable alternative to traditional fuels. A sample from Spain underwent elemental, technical, and thermogravimetric analyses. The results revealed a high calorific value of 18.26 MJ/kg, which can be attributed to the considerable carbon (47.4%) and hydrogen (6.1%) content, along with minimal sulfur levels. This composition makes olive stones a promising low-emission fuel. Thermogravimetric analysis showed that pyrolysis occurs in four phases, with 65% of the mass lost between 170 and 866°C, indicating the material’s suitability for thermal energy applications. The findings suggest that olive stones hold significant potential for use in renewable energy systems. Their utilization aligns with circular economy principles, transforming waste into energy and reducing environmental impact. Olive stones have low ash and moisture content, improving their efficiency as a fuel. Their high volatile matter content also supports energy-efficient gasification processes, further enhancing their energy potential.

In conclusion, this study confirms that olive stones are a viable alternative to fossil fuels, particularly for small-scale heating applications. With their high energy value, low emissions, and minimal residual waste, olive stones offer a sustainable and efficient energy solution. Their use not only supports green energy production but also contributes to reducing the carbon footprint and promoting sustainability.

The objective of this study is to thematically examine how South Africa can achieve its Just Energy Transition plan through a developmental state model. The heart of the argument reclines in assessing the country’s capacity to see through a fair and inclusive just energy transition. The capacity of South Africa to discern a just energy transition is prudently located between the type of state model South Africa (liberal and socialist state model) retains and the kind of state model (developmental state) it aspires to be through the National Development Plan. The research seeks to investigate how the two development models can either endorse or impede the country’s just energy transition strategies and thus gauge the country’s capacity to realize a just energy transition. South Africa has developed a Just Energy Transition plan to rigorously facilitate the country’s energy transition from coal to cleaner energy sources. This qualitative desktop study adopts an empirical and thematic research design. The key finding in this research suggests that development models extensively influence how an energy policy can be apprehended. Moreover, the JUST part of the transition is the most prominent factor.

The sustainable development of a nation must encompass all three aspects: economic, social, and environmental. Strong development of wind power contributes to reducing greenhouse gas emissions, a commitment Vietnam has made to the international community. However, to successfully achieve this goal, public acceptance plays a crucial role in ensuring the social aspect of the energy transition process and sustainable development in Vietnam. Therefore, it is essential to conduct studies on the current state of public acceptance of wind power projects and the factors influencing this acceptance. Based on these insights, appropriate interventions and solutions can be proposed to enhance public support. This study aims to identify the factors affecting residents’ acceptance of wind power projects in Vietnam and compare the differences in acceptance between survey groups from the perspective of Behavioral Reasoning Theory (BRT). Such research is crucial in the context of Vietnam’s strong focus on developing wind energy in the coming years. The results reveal both similarities and differences with existing research. It also emphasizes the important role of intermediate variables, such as „reasons for” and „reasons against” within the model. The newly introduced variable, Government policy on wind power development, demonstrates an influence on acceptance comparable to other significant factors, such as reducing greenhouse gas (GHG) emissions and decreasing dependence on other energy sources. The research findings also provide a basis for proposing adjustments to the regulations regarding the minimum distance between residential areas and wind turbines.

Enhancing the thermal efficiency and operational stability of solar water heating collectors necessitates optimizing heat transfer and ensuring uniform heating of the working fluid. This study examines the impact of flow velocity and turbulence on heat exchange processes and temperature distribution within the ribbed tubes of a solar collector. The problem is significant for maximizing solar energy utilization and minimizing auxiliary energy losses. Based on a review of recent advances in thermal-fluid modeling, the study aimed to identify optimal flow regimes that balance heat transfer enhancement with energy efficiency. Numerical simulations were conducted using the finite difference method, assuming steady-state turbulent flow in a 1.2 m ribbed tube with a 0.02 m internal diameter, wall temperature of 360 K, and inlet water temperature of 300 K. Thermophysical properties of water were set at 330 K. Results showed that an optimal flow velocity range of 3–4 m/s ensured efficient heat transfer and minimized energy losses. At 3.5 m/s, the outlet temperature reached 348 K, while the heat transfer coefficient peaked at 520 W/m²·K. The uniform heating coefficient ���� decreased from 0.0135 at 0.5 m/s to 0.0095 at 3.5 m/s, indicating significant improvement in temperature homogeneity. Ribbed surfaces enhanced the heat transfer coefficient by 25–35% compared to smooth tubes, even at moderate velocities. The study successfully implemented its objective and offered practical recommendations for optimizing the design and operation of solar thermal systems to ensure stable and energy-efficient performance.

Given the constantly changing market situation for electricity prices, driven by shifts in the energy mix, regulatory reforms, and broader socio-economic factors, it is necessary to reassess the understanding of price forecasting periodically. Traditional statistical methods may struggle when faced with heightened volatility, nonlinear dependencies, and rapidly changing input features. In contrast, machine learning models, particularly Artificial Neural Networks (ANNs), can adapt more effectively to complex, non-stationary patterns in price time series. In this study, six distinct artificial neural network (ANN) architectures were developed and trained using eight years of historical Polish Day-Ahead Market electricity price data (2016–2024). Four of these were plain deep learning models: a Multilayer Perceptron (MLP), a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) model, and a Gated Recurrent Unit (GRU) model. Two others were hybrid models combining convolutional layers with recurrent layers. The hybrid architectures, namely CNN+LSTM and CNN+GRU, were designed to leverage the capacity of CNN to automatically extract features from narrower sliding windows of past prices and the LSTM/GRU layers’ ability to capture long-term temporal dependencies. The models’ performances were evaluated using three metrics: Mean Absolute Error (RMSE), and the coefficient of determination (R²). The top-performing CNN+LSTM achieved an (MAE), Root Mean Squared Error MAE of 75.21 PLN/MWh, an RMSE of 103.64 PLN/MWh, and an R² of 0.59. Results were also compared against several models previously reported in the literature. These results may be used to improve price forecasting by indicating the optimal pathways for building forecasting models and, in extension, lead to more efficient power system planning.