Search Results (55,224)

This study investigates the impact of environmental regulations and marketization on energy efficiency in China using panel data from 30 provinces covering the period from 2008 to 2016. The analysis employs fixed effects regression, quantile regression, and heterogeneity analysis methods to provide comprehensive insights. The fixed effects regression results reveal that both command-and-control (CAC) regulations and market-based (MBR) regulations exert a negative impact on energy efficiency. Notably, marketization significantly mitigated the adverse effects of CAC regulations. Quantile regression analysis indicates that both negative impacts are more pronounced at lower energy efficiency levels, whereas marketization (MR) significantly mitigates these effects. Heterogeneity analysis further identified regional disparities, with CAC regulations yielding more significant negative impacts in the Western and Central regions and MBR showing stronger effects in the Western region. The results advocate for regionally differentiated policies that account for local economic, infrastructural, and institutional contexts to enhance energy efficiency outcomes rather than relying on one-size-fits-all approaches. Full article

The European Union’s energy policy, which aims to achieve climate neutrality by 2050, requires substantial changes in the structure of the energy sources used for power generation. The paper considers the possibilities of increasing the pace of the Polish energy system transformation by replacing coal sources with renewable energy sources using energy storage. It is demonstrated that in the analyzed period until 2040 it will be possible to ensure the required level of the system’s energy supply reliability by supporting the system with energy storage. The assessment of the system reliability was carried out based on the LOLE and LOLP indicators, selecting the system structure in such a way that for the hourly energy demand characteristic adopted in the analyzed year, the LOLE was less than 3 h. The required capacity and power of the storage systems depend on the level of the demand for energy and power. The results of the analyses indicate that for the linear trend in the growth in the demand for energy, nuclear power plants with the total power of 8.8 GW have to be installed in the energy system. However, with a significant rise in the power demand and the decommissioning of coal units, balancing the system using other sources with a dominant share of renewable sources will be insufficient. It will therefore be necessary to use the energy storage with a capacity above 11 GWh and a total power above 2 GW. Full article

Short-term load is influenced by multiple external factors and shows strong nonlinearity and volatility, which increases the forecasting difficulty. However, most of existing short-term load forecasting methods rely solely on the original load data or take into account a single external factor, which results in significant forecasting errors. To improve the forecasting accuracy, this paper proposes a short-term load forecasting method considering multiple contributing factors based on VAR and CEEMDAN-CNN- BILSTM. Firstly, multiple contributing factors strongly correlated with the short-term load are selected based on the Spearman correlation analysis, the vector autoregressive (VAR) model with multivariate input is derived, and the Levenberg–Marquardt algorithm is introduced to estimate the model parameters. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and permutation entropy (PE) criterion are combined to decompose and reconstruct the original load data into multiple relatively stationary mode components, which are respectively input into the CNN-BILTSM network for forecasting. Finally, the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) is used to optimize the parameters of the combinative model to improve the forecasting accuracy. The actual simulation results utilizing the Australian data validate the forecasting accuracy of the proposed model, achieving reduction in the root mean square error by 31.21% and 18.04% compared to the VAR and CEEMDAN-CNN-BILSTM, respectively. Full article

Road freight transport is the key driver of the European economy and society; thus, distortion of its operation would have negative influence on growth and well-being. For that reason, implementation of European policies, including transport decarbonization, should be comprehensively evaluated from an environmental, social and economic perspective. In that case, introduction of electric trucks will create a mutual impact on the market and on haulage companies. The main research problem is to assess the future impact of decarbonization on the international road freight transport market structure on the supply side and the competitiveness of companies operating there. Today, a number of small and medium companies, to a great extent from Eastern Europe, render transportation services, creating a competitive structure with high flexibility, accessibility and low prices. Shifting towards electric trucks, with significantly higher upfront costs, will redefine the market structure, eliminating the small carriers and activating horizontal integration. The key objective of this research is to identify the main factors and challenges related to electric truck implementation and define crucial areas of its impact on future market structure. The research shows that the improvement of environmental performance requires low- or zero-emission trucks, where the battery technology is a leading solution. Thus, fleet renewal needs additional financial support from the public side. Different measures are available in European countries, so the level of support is not equal from a competitiveness perspective. Battery truck selling, as well as sustainable strategies, refer mostly to huge transport companies. On the other hand, the case of Polish truckers shows that the economic viability of SMEs is poor; thus, the introduction of BET would be beyond its reach. The research findings could be treated as recommendations for market regulators (EC), where the tempo of implementation, as well as availability of public support programs, should be rethinking. As a result, the costs of the transition will be covered by citizens, as customers, in the prices of products and transport service, or as taxpayers, in public support programs, mainly consumed by large market stakeholders. Full article

Research on seed hemp and pine was carried out to improve sustainability and energy efficiency. The mechanical properties of different species of lignocellulosic biomass are still undocumented in the context of granulation processes, even though lignocellulosic biomass is widely studied for biofuel production. Hemp and pine have not been thoroughly compared in the granulation process. Under compressive forces pertinent to pelletizing, the study investigated the mechanical properties of lignocellulosic materials, such as hemp and Scots pine. Based on their mechanical properties, microscopic analysis and strength tests were conducted to compare hemp pellets and pine briquettes. In recent years, a significant trend has been towards eco-friendly and innovative biofuel production, motivating research on compaction technologies and material strength enhancement. The study compared hemp (Cannabis sativa L.) with Scots pine (Pinus sylvestris) during compaction. Compared with pine briquettes, hemp pellets exhibit superior mechanical durability (durability factor = 0.98) and compressive strength (average 2.5 kN), demonstrating hemp’s potential as a renewable fuel source. The study results contribute to the development of sustainable biofuel production processes. Full article

This paper is about the characteristics of and a method to recognize the onset of limit cycle thermoacoustic oscillations in a gas turbine-like combustor with a premixed turbulent methane/air flame. Information on the measured time series data of the pressure and the OH* chemiluminescence is acquired and postprocessed. This is performed for a combustor with variation in two parameters: fuel/air equivalence ratio and combustor length. It is of prime importance to acknowledge the nonlinear dynamic nature of these instabilities. A method is studied to interpret thermoacoustic instability phenomena and assess quantitatively the transition of the combustor from a stable to an unstable regime. In this method, three-phase portraits are created on the basis of data retrieved from the measured acoustics and flame intensity in the laboratory-scale test combustor. In the path to limit cycle oscillation, the random distribution in the three-phase portrait contracts to an attractor. The phase portraits obtained when changing operating conditions, moving from the stable to the unstable regime and back, are analyzed. Subsequently, the attractor dimension is determined for quantitative analysis. On the basis of the trajectories from the stable to unstable and back in one run, a study is performed of the hysteresis dynamics in bifurcation diagrams. Finally, the onset of the instability is demonstrated to be recognized by the 0-1 criterion for chaos. The method was developed and demonstrated on a low-power atmospheric methane combustor with the aim to apply it subsequently on a high-power pressurized diesel combustor. Full article

The debate on renewable energy consumption and green growth is yet to be empirically settled. Motivated by the dearth of literature in sub-Saharan Africa on this subject, this study examines the impact of renewable energy consumption on green growth using a panel of 22 sub-Saharan countries observed between 1990 and 2019. It criticizes the existing literature for inadequately addressing cross-sectional dependence and inappropriately assuming homogeneity in the way renewable energy consumption affects green growth across countries. A comparison of the commonly applied estimator in the literature which ignores the above two violations, and the preferred estimator suggests that the current state of knowledge could be fundamentally flawed. While the estimator commonly applied in the literature replicates the largely reported positive and direct impact of renewable energy on green growth, the preferred estimator finds the impact insignificant. It instead finds that green growth is primarily driven by gross capital formation, rather than renewable energy consumption. This study additionally examines the effect of renewable energy on several types of environmental damage. Renewable energy is found to significantly reduce CO₂ emissions only. The conclusion is therefore that renewable energy does not have a direct impact on green growth, although it helps reduce CO₂ emissions. Full article

The rapid global shift toward renewable energy necessitates innovative solutions to address the intermittency and variability of solar and wind power. This study presents a comprehensive review and framework for deploying Integrated Energy Storage Systems (IESSs) to enhance grid efficiency and stability. By leveraging a Multi-Criteria Decision Analysis (MCDA) framework, this study synthesizes techno-economic optimization, lifecycle emissions, and policy frameworks to evaluate storage technologies such as lithium-ion batteries, pumped hydro storage, and vanadium flow batteries. The framework prioritizes hybrid storage systems (e.g., battery–supercapacitor configurations), demonstrating 15% higher grid stability in high-renewable penetration scenarios, and validates findings through global case studies, including the Hornsdale Power Reserve (90–95% round-trip efficiency) and Kauai Island Utility Cooperative (15,000+ cycles for flow batteries). Regionally tailored strategies, such as Kenya’s fast-track licensing and Germany’s H2Global auctions, reduce deployment timelines by 30–40%, while equity-focused policies like India’s SAUBHAGYA scheme cut energy poverty by 25%. This study emphasizes circular economy principles, advocating for mandates like the EU’s 70% lithium recovery target to reduce raw material costs by 40%. Despite reliance on static cost projections and evolving regulatory landscapes, the MCDA framework’s dynamic adaptation mechanisms, including sensitivity analysis for carbon taxes (USD 100/ton CO₂-eq boosts hydrogen viability by 25%), ensure scalability across diverse grids. This work bridges critical gaps in renewable energy integration, offering actionable insights for policymakers and grid operators to achieve resilient, low-carbon energy systems. Full article

In recent years, the increase in energy demand has been an incentive to search for new ways to generate energy. An alternative is producing this energy from daily human activities. To do this, piezoelectric devices have been used in different human activities to collect energy. Some of these potential activities are transportation, biomedicine, and electronic devices. Harvesting energy from the mechanical force applied by a pianist during their performance is one of these activities that can be used. The implementation of piezoelectric devices under the keys of an electric organ was carried out. A theoretical model was developed to estimate the amount of energy we could recover. The system was characterized by controlled forces. The volume generated by the forces was measured via a Musical Instrument Digital Interface (MIDI) using the open-source music production software “LMMS (Linux MultiMedia Studio) 1.2.2 version”. The electric potential difference was measured as a function of the volume generated by the pianist. The voltages generated for different frequencies of the pianist’s rhythm were studied. The efficiency calculated in the mathematical model agreed with that obtained in the implemented system. The study results indicate that the batteries were recharged, which resulted in 53 s of organ operation. Full article

Accurate wind power forecasting (WPF) is crucial to enhance availability and reap the benefits of integration into power grids. The time lag of wind power generation lags the time of wind speed changes, especially in ultra-short-term forecasting. The prediction model is sensitive to outliers and sudden changes in input historical meteorological data, which may significantly affect the robustness of the WPF model. To address this issue, this paper proposes a novel hybrid machine learning model for highly accurate forecasting of wind power generation in ultra-short-term forecasting. The raw wind power data were filtered and classified with the local outlier factor (LOF) and the voting tree (VT) model to obtain a subset of inputs with the best relevance. The time-varying properties of the fluctuating sub-signals of the wind power sequences were analyzed with the optimized variational mode decomposition (OVMD) algorithm. The Northern Goshawk optimization (NGO) algorithm was improved by incorporating a logical chaotic initialization strategy and chaotic adaptive inertia weights. The improved NGO algorithm was used to optimize the least squares support vector regression (LSSVR) prediction model to improve the computational speed and prediction results. The proposed model was compared with traditional machine learning models, deep learning models, and other hybrid models. The experimental results show that the proposed model has an average R² of 0.9998. The average MSE, average MAE, and average MAPE are as low as 0.0244, 0.1073, and 0.3587, which displayed the best results in ultra-short-term WPF. Full article

With recent technological advancements, advanced communication technology, sensors and distributed generation (DG), it is an undeniable fact that modern power systems are flooded with massive amounts of data. These vast amount of generated data are difficult to interpret and comprehend, and are slow to sort through and explain. With ever increasing renewable power generation, grid operators should gain insights on identifying the vulnerabilities, behaviour and interactions of various power system components and anticipate challenges to enhance power system resiliency. Visualisation offers a means to reveal patterns, trends and connections in data that speed up and present information to a power system operator in a way that can be well understood topographically and provide an ability to accommodate increasing DG resources. Hence, this paper presents a comprehensive literature review of several visualisation techniques that can be embedded for improving operational efficiency and resiliency in modern power grids embedded with distributed and renewable energy resources. Full article

One of the approaches to limit the negative impact on the environment from the burning of coal in the production of heat and electricity is to limit their use by blending them with biomass. Blended fuel combustion leads to the generation of a solid ash residue differing in composition from coal ash, and opportunities for its utilization have not yet been studied. The present paper provides results on the carbon capture potential of adsorbents developed through the alkaline conversion of ash mixtures from the combustion of lignite and biomass from agricultural plants and wood. The raw materials and the obtained adsorbents were studied with respect to the following: their chemical and phase composition based on Atomic Absorption Spectroscopy with Inductively Coupled Plasma (AAS-ICP) and X-ray powder diffraction (XRD), respectively, morphology based on scanning electron spectroscopy (SEM), thermal properties based on thermal analysis (TG and DTG), surface parameters based on N₂ physisorption, and the type of metal oxides within the adsorbents based on temperature-programmed reduction (TPR) and UV-VIS spectroscopy. The adsorption capacity toward CO₂ was studied in dynamic conditions and the obtained results were compared to those of zeolite-like CO₂ adsorbents developed through the utilization of the raw coal ash. It was observed that the adsorbents based on ash of blended fuel have a comparable carbon capture potential with coal fly ash zeolites despite their lower specific surface areas due to their compositional specifics and that they could be successfully applied as adsorbents in post-combustion carbon capture systems. Full article

This article presents the findings of a bibliometric analysis of scientific publications in journals and materials indexed in the SCOPUS and Web of Science databases, covering the broad topic of underground hydrogen storage (UHS). The use of VOSviewer software for keyword analysis enabled the identification of four key research areas related to UHS. These areas include hydrogen and hydrocarbon reservoir engineering; hydrogen economy and energy transformation; processes in hydrogen storage sites, including lessons from CO₂ sequestration; and the geology, engineering, and geomechanics of underground gas storage. The interdisciplinary nature of UHS research emphasises the synergy of research across diverse fields. A bibliographic analysis allowed for the identification of areas of intensive research and new directions of work related to UHS, key research centres, and the dynamics of the development of research topics related to UHS. This study revealed the chronological dispersion of the research results, their geographical and institutional variability, and the varying contributions of major publishing journals. The research methodology used can serve as an inspiration for the work of other researchers. Full article

The swift global proliferation of solar photovoltaic (PV) technology has significantly contributed to the acceleration of the transition to renewable energy. Projections indicate a significant rise in installed capacity by 2050, suggesting that the extensive implementation of solar panels is transforming energy systems while simultaneously highlighting important issues regarding end-of-life waste management and long-term sustainability. The environmental advantages of photovoltaic (PV) systems are overshadowed by the prevalent reliance on landfilling and inadequate recycling practices, revealing a substantial deficiency in sustainable waste management, especially in areas with underdeveloped policy frameworks. This research study examines the solar panel supply chain, highlighting critical stages, sources of waste generation, existing management practices, and potential areas for enhancement. Waste is classified into four categories, solid, hazardous, electronic (WEEE), and environmental, each necessitating specific management strategies. Regions such as Europe exhibit comprehensive legal frameworks and advanced recycling technologies, whereas others, including Latin America and certain areas of Asia, continue to encounter deficits in policy and infrastructure. The research highlights the implementation of the 6R principles—Recycle, Recover, Reduce, Reuse, Repair, and Refine—within a circular economy framework to improve sustainability, optimize resource utilization, and reduce environmental impact. The findings highlight the necessity for coordinated policies, technological innovation, and international collaboration to ensure a sustainable future for solar energy. This study offers important insights for policymakers, industry stakeholders, and researchers focused on enhancing circularity and sustainability within the photovoltaic sector. Full article

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article