Search Results (82)

The authors of this study suggest an improvement to their recently released quadruple-diode boost regulator (QDBC), which may be used in two configurations: without or with a voltage multiplier unit (VMU). This voltage multiplier unit consists of two switch capacitors diagonally connected across two diodes, or vice versa. During each operational cycle, energy can be stored and released through the switch capacitive filters and inductive chokes, increasing voltage gain and decreasing output fluctuation. ANSOFT/SIMPLORER 7, PLECS 4.9.5, and SIMULINK 2021a are further used to simulate the proposed regulator’s linearized version to investigate its frequency response and stability. Hence, to improve the harmonic performance of the proposed regulator, the authors of this study used a delta modulation current regulator (DMCR), sometimes referred to as a variable bandwidth delta modulation current regulator. The findings show that the QDBC has, when using the DMCR, a voltage gain of $1+D/{(1-D)}^2$, an efficiency of 97%, and a shorter settling time of 0.04 s when compared to other DC-DC regulators (SEPIC, boost, and quadratic boost). Finally, to validate the theoretical analysis and simulation results of the proposed QDBC structure, a 250 W regulator prototype was built utilizing similar design exercise requirements. Full article

An unprecedented compound heatwave and drought (CHD) event occurred in the summer of 2022 in Southwest China. This extreme climate event posed significant challenges to the power system and highlights the importance of disaster risk management and adaptation to extreme climate events in the power sector. This paper assesses the complementary effects of variations in hydropower, wind, solar power generation and the power load gap in response to this CHD event. The CHD resulted in a remarkable 50% decrease in hydropower generation during the summer of 2022. Similarly, wind speeds in the southwest region slightly decreased from 2.0 m/s in mid-July to 1.7 m/s in early August. On the contrary, solar power generation doubled from mid-July to mid-August. In the summer of 2022, the increase in solar power generation could not compensate for the gap between the dramatically increased cooling demand and the reduced hydropower output. Nevertheless, it highlighted the potential synergy of power source grid load storage and hydro–wind–solar power combinations in addressing future CHD events, and the importance of early-warning for extreme climate events in the new-type power system in the future. Full article

The restrictions imposed by the European Green Deal on Europe are expected to make Europe climate-neutral by 2050. In this context, this article examines the current efforts to reduce emission levels, focusing on available international scientific papers concerning European territory, particularly Poland. The study paid special attention to the sector of agriculture, which is considered a key contributor to greenhouse gas generation. It also analysed the impact of various tillage techniques and the application of organic and inorganic fertilisers, e.g., nitrogen fertilisers, digestate, or compost, on the emissions of greenhouse gases and other environmentally harmful substances. Although there are few scientific articles available that comprehensively describe the problem of greenhouse gas emissions from agriculture, it is still possible to observe the growing awareness of farmers and their daily impact on the environment. The current study demonstrated that agricultural activities significantly contribute to the emissions of three main greenhouse gases: carbon dioxide, nitrous oxide, and methane. The tillage and soil fertilisation methods used play a crucial role in their emissions into the atmosphere. The use of no-tillage (or reduced-tillage) techniques contributes to the sustainable development of agriculture while reducing greenhouse gas emissions. The machinery and fuels used, along with innovative systems and sensors for precise fertilisation, play a significant role in lowering emission levels in agriculture. The authors intend to identify potential opportunities to improve crop productivity and contribute to sustainable reductions in gas emissions. Full article

There is an urgent need for a method of evaluating the real energy performance of vehicles that eliminates the effects of external conditions (topography, altitude, and road conditions) and human factors (driving styles), especially in the case of heavy-duty vehicles. Governmental authorities require results on the energy performance of vehicles to develop strategies that result in reductions in greenhouse gas emissions, while fleet managers require results regarding the energy efficiency of existing vehicle technologies to select the technologies that minimize energy consumption and, therefore, operational costs. Aiming to address this need, we propose a method for evaluating the global energy efficiency of road vehicles by monitoring at 1 Hz the operational variables of a vehicle under normal conditions of use for a long time. The variables monitored are engine RPM and vehicle location, speed, payload, and energy consumption. This method was verified using 49 vehicles, representing 23 vehicle technologies. These vehicles varied in size (light duty and heavy duty), application (cars, buses, and freight), energy sources (gasoline, diesel, and electric), and operational conditions (Chile, Ecuador, Colombia, and México). Testing was conducted across various altitudes (0–3600 masl) and topographies (flat and mountainous regions). The results showed that the energy efficiencies for gasoline-fueled light-duty vehicles ranged from 17 to 30%, those for diesel-fueled heavy-duty vehicles ranged from 25 to 42%, and those for electric heavy-duty vehicles (HDVs) ranged from 70 to 80%. Full article

Countries prioritize secure and cheap energy over clean energy in their energy policies, and Türkiye is no different. The Strategy Plan 2015–2019 of the Ministry of Energy and Natural Resources emphasizes the exploitation of domestic coal for energy security, while Türkiye intends to curb its emissions by 41% by 2030. These two targets contradict in terms of climate change mitigation. In retrospect, this study aims to determine the role of coal, wind, and solar power in energy policy-making through scenario analyses. The results indicate that if Türkiye continued its pre-2020 energy policy, its use of domestic coal would be important for energy security. On the other hand, both wind and solar have the capacity to contribute to the country’s efforts towards energy security and climate change mitigation. Full article

The precise estimation of influential parameters in adsorption is a key point in conducting simulations for the sensitivity analysis and optimal design of cooling systems. This study explores the critical role of a new type of granular activated carbon (GAC-208C) in adsorption refrigeration systems. By fitting experimental and numerical models to the thermophysical properties of GAC/methanol as a working pair, an advanced methodology is established for the thermal analysis of the adsorption bed, addressing the various operating conditions overlooked in prior studies. The physical properties of the studied carbon sample are determined in a laboratory using surface area and pore volume tests, thermal adsorption analysis, and weight loss. To determine the thermal properties of GAC/methanol, the adsorption process is experimentally tested inside an isolated heat exchanger. A three-dimensional (3D) model is created to simulate the procedure and then coupled with the particle swarm optimisation (PSO) algorithm in MATLAB. The optimal thermal parameters for adsorption are determined by minimising the mean square error (MSE) of the adsorption bed temperature between the numerical and experimental data. The laboratory studies yielded accurate results for the physical properties of GAC, including adsorption capacity, porosity, permeability, specific heat capacity, density, activation energy, and the heat of adsorption. The thermal analysis of the adsorption process identified the ideal values for the Dubinin–Astakhov equation constants, diffusion coefficients, heat transfer coefficients, and contact resistance. The numerical model demonstrated strong agreement with the experimental results, and the dynamic behaviour of pressure and uptake distribution showed good agreement with 1.2% relative error. This research study contributes to the improved estimation of adsorption parameters to conduct more accurate numerical simulations and design new adsorption systems with enhanced performance under different operating conditions. Full article

This study investigates the impact of energy, economic, and environmental policies on Türkiye’s energy dynamics and CO₂ emissions using climate models and an agent-based simulation (ABM) framework. By integrating climate projections with policy scenarios, it assesses how energy transitions and climate change affect renewable energy sources (RES), cooling demands, and CO₂ emissions from electricity generation. Methods include selecting suitable Global Climate Models (GCMs), evaluating climate change impacts on RES performance, and simulating policy effects through ABM across one base and nine policy scenarios from 2023 to 2040. The study highlights the critical role of policy interventions in influencing emissions trends and energy prices. It identifies renewable energy subsidies and low-carbon strategies, such as nuclear power support, as effective tools for reducing emissions and stabilizing energy costs. The methodologies and findings provide actionable insights for policymakers globally, emphasizing the importance of integrating climate data with policy planning. Full article

Stand-alone carbon pricing is still predominantly advanced as the policy instrument of choice for abating greenhouse gas emissions, although it has enjoyed limited support in practice. Against this background, the current paper investigates whether combinations of climate policy measures constitute a promising avenue for reaching zero emissions in the power, transport, buildings, and industry sectors. Adopting a sectoral approach, we review the international literature on instrument mixes and consolidate previous findings according to four widely employed evaluation criteria, namely environmental effectiveness, economic efficiency, social equity, and political feasibility. The performance of mixes along these dimensions is critically assessed by studying the inter-relationships between decarbonisation levers, decarbonisation barriers, and policy instrument characteristics. Our analysis suggests that carefully designed combinations of measures may perform better than stand-alone instruments in many instances, although trade-offs between policy objectives are inevitable. We show evidence that desirable instrument mixes should be tailored to the characteristics of targeted agents and technologies, but argue that some level of carbon pricing typically offers high potential for synergies with complementary measures in all sectors. The study focuses on Switzerland for illustrative purposes, yet the policy implications are far-reaching and drawn from experiences across Europe and North America. Full article

This study presents a novel, multifaceted approach to evaluating decarbonisation technologies by integrating advanced text-mining tools with comprehensive data analysis. The analysis of scientific documents (2011–2021) and mapping 368 technologies from the IEA’s Energy Technology Perspectives identified 41 technology domains, including 20 with the highest relevance and occurrence. Domain readiness was assessed using mean Technology Readiness Levels (TRLs) and linked to six decarbonisation pathways. The “Electrification of uses” pathway ranked highest, demonstrating significant CO₂ mitigation potential and high readiness (mean TRL 7.4, with two-thirds of technologies scoring over 7) despite challenges in hard-to-electrify sectors. The findings provide actionable insights for policymakers, highlighting the need for pathway-specific strategies, a deeper understanding of synergies between pathways, and balancing innovation with deployment to accelerate decarbonisation. Full article

This study explores the long-run relationship among the environmental footprint (EnF), renewable energy consumption, energy use, industrial growth, and urbanization in Saudi Arabia from 1990 to 2023, employing the Autoregressive Distributed Lag (ARDL) model, alongside Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) for robustness checks. Results indicate a significant long-term relationship among the variables, with renewable energy adoption emerging as a crucial factor in reducing carbon emissions. The ARDL bounds test confirms the existence of cointegration, revealing the dynamic interplay among renewable energy, economic growth, and environmental sustainability. The findings show that renewable energy consumption significantly reduces the environmental footprint (CO₂ emissions), supporting Saudi Arabia’s Vision 2030 goals for economic diversification and sustainable development. However, industrial expansion, while critical for economic growth, still contributes to increased emissions, underscoring the need for further investment in clean technologies. The study also highlights the role of urbanization, which, while essential for development, poses challenges for environmental sustainability. Short-term dynamics, represented by the Error Correction Model, indicate a fast adjustment speed toward equilibrium, with deviations corrected by approximately 52% each period. The study offers valuable insights for policymakers aiming to balance industrial growth with environmental protection, emphasizing the need for strategic investments in renewable energy and energy efficiency. This research contributes to the understanding of energy–economy–environment interactions in oil-rich economies, providing a foundation for future studies to explore the impact of advanced technologies and policy interventions on sustainable development Full article

Ultra violet (UV) solar energy can cause several negative effects to the skin and eyes in case of overexposure. To protect people from erythemal damage, personal erythemal exposure must be carefully assessed when outdoor activities are carried out. The direct measure with scientific instruments is impracticable to common people, and indirect methods assess the exposure only on the horizontal plane: this work developed a mathematical model to assess erythemal exposure to all the body districts. UVA irradiance and erythemal irradiance were measured on several inclined planes, oriented to the four cardinal directions, in seven environments with multiple sky conditions. The UV erythemal (UVE) ratio between erythemal irradiance on an inclined plane (Iery°) and UVA irradiance on a horizontal plane (IUVAh) was calculated. The results indicate that the UVE = Iery°/IUVAh is variable across the day and depends on the plane orientation, its degree of inclination, and sky conditions. Mathematical models to calculate erythemal exposure in clear, intermediate and overcast sky conditions on planes with different inclinations and orientations were derived from the daily trends of the UVE = Iery°/IUVAh. The application procedure of the mathematical model to the vertical plane oriented to the south is provided as an example. Full article

This study investigates the impact of vertical greenery systems (VGSs) applied to several typical wall configurations on indoor thermal conditions in a building module situated in the Mediterranean climate of Catania, Italy. By means of dynamic simulations in TRNSYS vers.18, the research compares the thermal behavior of walls made of either hollow clay blocks (Poroton) or lava stone blocks against a lightweight wall setup already in place at the University of Catania. The primary focus is on evaluating the VGSs’ capability of reducing peak inner surface temperatures and moderating heat flux fluctuations entering the building. The findings indicate that adding an outer vertical greenery layer to heavyweight walls can decrease the peak inner surface temperature by up to 1.0 °C compared to the same bare wall. However, the greenery’s positive impact is less pronounced than in the case of the lightweight wall. This research underscores the potential of green facades in enhancing the indoor thermal environment in buildings in regions with climates like the Mediterranean one, providing valuable insights for sustainable building design and urban planning. Full article

In the era of digitalization, the large availability of data and innovations in machine learning algorithms provide new potential to improve the prediction of energy efficiency in buildings. The building sector research in the Kingdom of Saudi Arabia (KSA) lacks actual/measured data-based studies as the existing studies are predominantly modeling-based. The results of simulation-based studies can deviate from the actual energy performance of buildings due to several factors. A clearer understanding of building energy performance can be better established through actual data-based analysis. This study aims to predict the energy efficiency of residential buildings in the KSA using supervised machine learning algorithms. It analyzes residential energy trends through data collected from an energy audit of 200 homes. It predicts energy efficiency using five supervised machine learning algorithms: ridge regression, least absolute shrinkage and selection operator (LASSO) regression, a least angle regression (LARS) model, a Lasso-LARS model, and an elastic net regression (ENR) model. It also explores the most significant explanatory energy efficiency variables. The results reveal that the ENR model outperforms other models in predicting energy consumption. This study offers a new and prolific avenue for the research community and other building sector stakeholders, especially regulators and policymakers. Full article

This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap. Full article

Using renewable energies is one of the alternatives to mitigate climate change. Among them, photovoltaic energy has shown a relevant growth of participation in the electric sector. In the backdrop of such growth, in countries such as Brazil, photovoltaic energy has surpassed the generation of electricity by petroleum derivatives since 2019. The significant growth in photovoltaic generation around the world can be attributed to several key factors, including abundant sunlight, supportive government policies, falling solar panel costs, environmental concerns, energy diversification goals, investor interest, job creation, and local manufacturing. However, photovoltaic system performance is heavily tied to weather variability. Different models are used to account for this meteorological dependence; however, there is a gap regarding the differences in the outputs of these models. The study presented here investigates the variability and sensitivity of the models used to estimate photovoltaic production ($P_{pv}$). Six models were compared by percentage difference analysis. Statistical analyses from the perspective of variability revealed that the difference between the $P_{pv}$ estimated by these models reaches a 12.89% absolute power difference. Considering that temperature and solar irradiance are the meteorological variables that most influence $P_{pv}$, the sensitivity analysis focused on these. Regarding sensitivity, in the context of temperature changes, the average relative difference in $P_{pv}$ between models can reach up to 5.32% for a 10 °C change, while in the context of changes in solar irradiance, the average relative difference can reach up to 19.05% for a change of 41.67 W/m². The consideration of the variability and sensitivity of the main sets of equations used to estimate the potential of photovoltaic energy production can help refine methodologies and assumptions in future research in this area. There are variations and sensitivities, as observed, of such magnitude that, depending on the set of equations adopted in the study, they can alter the conclusion about photovoltaic energy production in a given region. Accurate estimations are pivotal not only for feasibility analyses but also for gauging economic and socio-environmental impacts. These divergences can, in turn, reformulate feasibility analyses and compromise the reliability of photovoltaic energy systems, thus leading to different economic and socio-environmental consequences. Full article