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People aim for thermal comfort inside their homes. However, this is not achievable for everyone due to several factors, such as low income, poor building envelope, expensive technology, and increased energy costs, thus leading to energy poverty. This work gathers studies regarding energy poverty and its indicators, identified by different authors and considering different regions, techno-economic, governmental, and political considerations. It was observed that renewable energy sources are a good investment in the long term but require a high initial investment. Government policy measures should be applied to mitigate the costs, especially given the increasing requirement for clean energy use in new buildings. There are still many barriers to overcoming energy poverty, and the variables for action are numerous. The best solution passes through the assessment of adequate technological solutions with economic incentives from the government for the most vulnerable individuals that should be identified by region and economic power. Energy poverty is a multidimensional problem that depends on individual characteristics such as households’ income, specific energy needs, and available technologies, as well as external factors such as energy prices, climatic conditions, and energy access. The majority of energy indicators are directly related to economic aspects, whereas social or environmental considerations are only indirectly accounted for. Full article

Increasing the generation of electric power from renewable energy sources (RESs) creates important challenges to transmission system operators (TSOs) for balancing the power system. To address these challenges, adequate system flexibility is required. In this context, TSOs carry out flexibility assessment studies to evaluate the flexibility level of the power system and ensure that a stable operation of the transmission system under high RESs integration can be achieved. These studies take into consideration numerous scenarios incorporating different assumptions for temperature, RESs penetration, load growth, and hydraulic conditions. Until now, flexibility studies usually solve the standard unit commitment problem and evaluate if the flexibility level is adequate. Although this approach provides quite accurate results, the computational requirements are significant, resulting in limiting the scenarios chosen for examination. In this paper, deep learning approaches are examined, and more precisely, an integrated system of two recurrent neural networks with long short-term memory cells is designed to carry out the flexibility assessment task, aiming at the reduction in the computational time required by the optimization process. The output of this neural network system is then used to calculate the probability of flexibility shortages. The proposed method is evaluated based on data from the Hellenic transmission system, providing quite promising results in (a) accurately calculating the probability of insufficient flexibility and (b) achieving a significant decrease in computational time. This novel approach could notably facilitate TSOs since more scenarios can be included, exploiting the computational efficiency of the method. In this way, a more complete evaluation of the flexibility level of the power system can be achieved and thus help to ensure the stable and reliable operation of the transmission system. Full article

Redox Flow Batteries (RFBs) offer a promising solution for energy storage due to their scalability and long lifespan, making them particularly attractive for integrating renewable energy sources with fluctuating power output. This study investigates the performance of a prototype Zinc-Chlorine Flow Battery (ZCFB) designed for low-cost and readily available electrolytes. The ZCFB utilizes a saltwater electrolyte containing ZnCl₂ and NaCl, paired with a mineral spirits catholyte. The electrolyte consists of a 4 M ZnCl₂ and a 2 M NaCl solution, both with a pH of 4.55. The anode was a zinc metal electrode, while the cathode comprised a porous carbon electrode on a titanium grid current collector. The cell volume was approximately 4.0 mL, with separate reservoirs for the NaCl/H₂O and mineral spirits electrolytes. Experiments were conducted under constant current conditions, with a 0.2 A charging current and a 5 mA discharge current chosen for optimal cell voltage. The study analyzed the relationship between voltage, current, power, and capacity during both charging and discharging cycles. Results from multiple charge/discharge cycles found that the current density of the battery is around 62.658 mA/cm² with an energy capacity average of 1.2 Wh. These findings can contribute to the development of more efficient and practical ZCFBs, particularly for applications requiring low-cost and readily available electrolytes. Full article

Bifacial photovoltaic sunshade (BiPVS) is an innovative building-integrated photovoltaic (BIPV) technology. Vertically mounted BiPVS is capable of converting part of the incident solar radiation into electricity, regulating the indoor heat gain from solar penetration and improving daylighting. An excellent BiPVS design should comprehensively consider its impact on building performance and economic viability. This study aims to address this issue by proposing a parametric design-based multi-objective optimization (MOO) framework to maximize indoor useful daylight illuminance, minimize air-conditioning energy consumption, and shorten the payback period by optimizing BiPVS design parameters. The framework utilizes the Ladybug, Honeybee, and Wallacei plugins on the Rhino-Grasshopper simulation platform. It validates the optimization potential of BiPVS in a typical office located in a hot summer and warm winter zone. The results indicate that BiPVS has significant energy-saving and daylighting potential. Compared to the baseline model without BiPVS, useful daylight illuminance is increased by 39.44%, air-conditioning energy consumption is reduced by 12.61%, and the economically satisfactory payback period is 4.80 years. This study provides a practical solution for the competing objectives of daylighting and energy saving in buildings with significant renewable energy utilization. The developed framework is highly efficient and versatile and can be applied to other BIPV designs, which benefits the realization of carbon-neutral goals in the building sector. Full article

As a highly energy-intensive and carbon-emitting industry with significant emissions of volatile organic compounds (VOCs), the petroleum and chemical industry is a major contributor to the global greenhouse effect and ozone layer destruction. Improper treatment of petrochemical waste gas (PWG) seriously harms human health and the natural environment. This study uses CiteSpace and VOSviewer to conduct a scientometric analysis of 1384 scholarly works on PWG and carbon sequestration published between 1981 and 2022, revealing the basic characteristics, knowledge base, research topic evolution, and research hotspots of the field. The results show the following: (1) In the early stages of the petrochemical industry, it was processed tail gas, plant leakage waste gas, and combustion flue gas that were investigated in PWG research. (2) Later, green environmental protection technology was widely studied in the field of PWG treatment, such as biotechnology, catalytic oxidation technology, membrane separation technology, etc., in order to achieve efficient, low energy consumption and low emissions of waste gas treatment, and the number of publications related to this topic has increased rapidly. In addition, researchers studied the internet of things and technology integration, such as the introduction of artificial intelligence, big data analysis, and other technologies, to improve the accuracy and efficiency of exhaust gas monitoring, control, and management. (3) The department has focused on how to reduce emissions by optimizing petrochemical process lines or improving energy efficiency. Emission reduction and low-carbon transition in the petrochemical industry will become the main trend in the future. Switching from renewable carbon to feedstock carbon derived from captured carbon dioxide, biomass, or recycled chemicals has become an attractive strategy to help curb emissions from the chemical industry. The results of our analysis can provide funding agencies and research groups with information to better understand the global trends and directions that have emerged in this field from 1981 to 2022 and serve as a reference for future research. Full article

With the growth of the wind energy market and the increase in the size of wind turbines, the demand for advanced composite materials with high strength and low density for wind turbine blades has become imperative. Graphene platelets (GPLs) stand out as highly premising reinforcements due to their exceptional physical properties, resulting in their widespread adoption in the composite industry in recent years. The present study aims to analyze the applicability of a graphene-platelet-reinforced composite (GPLRC) to wind turbine blades in terms of structural performance. A finite element blade model is constructed by referring to the National Renewable Energy Laboratory (NREL) 5 MW wind turbine, and its reliability is verified through a convergence test. The performance of the wind turbine blade is quantitatively examined in terms of the deflection and stress, natural frequencies, and twist angle. The applicability of the GPL-reinforced wind blade is explored through a comparison with wind blades manufactured with glass fiber and carbon nanotubes (CNTs). The comparison indicates that the performance of a wind blade can be remarkably improved by reinforcing with GPLs instead of traditional fillers, and the weight of not only the wind blade itself but also the wind turbine system can be remarkably reduced. The present results can be useful in the development of next-generation high-strength lightweight wind turbine blades. Full article

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and engineering frequently involve multiple risk considerations. In this study, we constructed a three-stage comprehensive risk management model of HPGE, which can help to overcome the issues of redundant risk indicators, imprecise risk assessment techniques, and irrational risk warning models in existing studies. First, we use the fuzzy Delphi model to identify the key risk indicators of HPGE. Then, the Bayesian best–worst method (Bayesian BWM) is adopted, as well as the measurement alternatives and ranking according to the compromise solution (MARCOS) approach, to evaluate the comprehensive risks of projects; these methods are proven to have more reliable weighting results and a larger sample separation through comparative analysis. Finally, we established an early warning risk model on the basis of the non-compensation principle, which can help prevent the issue of actual risk warning outcomes from being obscured by some indicators. The results show that the construction of the new power system and clean energy consumption policy are the key risk factors affecting HPGE. It was found that four projects are in an extremely high-risk warning state, five are in a relatively high-risk warning state, and one is in a medium-risk warning state. Therefore, it is necessary to strengthen the risk prevention of HPGE and to develop a reasonable closed-loop risk control mechanism. Full article

The growing urgency for sustainable energy solutions necessitates a deeper understanding of the environmental impacts of renewable technologies. This article aims to synthesize and analyze Life Cycle Assessments (LCA) in this domain, providing a comprehensive perspective. We systematically categorized 2923 articles into four sectors: (1) photovoltaic systems, (2) wind energy systems, (3) solar thermal systems, and (4) materials for auxiliary industry supporting these systems. A comparative analysis was conducted to identify methodological consistencies and disparities across these sectors. The findings reveal diverse methodological approaches and a range of environmental impacts, highlighting the complexities in assessing renewable energy systems. The article underscores the significance of material selection in photovoltaic, solar, and wind systems, providing a critical overview of the current state of LCA research in renewable energy and stressing the need for standardized methodologies. It also identifies gaps in recent research, offering insights for future studies focused on integrating environmental, economic, and social considerations in renewable energy assessments. Integrating environmental assessments provides a robust framework for making informed decisions on sustainable technologies. The findings are critical for projects that balance technological needs with sustainability goals. Full article

In this study, we successfully realized the hydrolytic ring-opening co-polymerization of ε-caprolactam (CPL) and lysine derivative. A novel antibacterial modified polyamide 6 with a branched structure was obtained after the quaternization of the co-polymers. The co-polymers exhibited a significant increase in zero shear viscosity, melt index and storage modulus at the low frequency region. The quaternized co-polymers displayed thermal properties different from pure PA6 and good mechanical (tensile) properties. The antibacterial activity of the quaternized co-polymers depends on the quaternary ammonium groups’ incorporated content. At 6.2 mol% incorporation of quaternary ammonium groups, the strong antibacterial activity has been introduced to the co-polymers. As the quaternary ammonium groups approached 10.1 mol%, the antibacterial polymers demonstrated nearly complete killing of Staphylococcus aureus (Gram positive) and Escherichia coli (Gram negative). The above research results provided a new approach for the study of high-performance nylon. Full article

Gold is neither a critical mineral nor a metal that is central to the global energy transition in terms of demand from new energy production technologies. Yet, gold is unique among mined commodities for its role in financial markets and for its global production footprint including in numerous developing economies. Since the production of gold incurs CO₂ emissions and other environmental risks including water pollution and land degradation, gold producers seek to adopt clean production solutions through electrification and renewable energy adoption. Further, gold’s unique role as a store of value creates new potential green business models in gold, such as the digitalisation of in-ground gold inventories, which can further reduce negative environmental externalities from gold mining. A net-zero emissions, future global gold industry, is possible. Major gold producers are targeting net-zero Scope 1 and 2 emissions by 2050, coupled with a lower overall environmental footprint to meet heightened societal expectations for cleaner production. An analysis of emissions data from Australian gold mines shows systematic differences between mining operations. Further clean energy investment in gold production is required to reduce emission levels towards the target of net zero. Full article

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently. Full article

South Africa is currently experiencing an energy crisis because of a mismatch between energy supply and demand. Increasing energy demand necessitates the adequate operation of generation and transmission facilities to maintain the reliability of the power system. Transmission line compensation is used to increase the ability to transfer power, thereby enhancing system stability, voltage regulation, and reactive power balance. Also, in recent years, the introduction of renewable energy sources (RES) has proven to be effective in supporting the grid by providing additional energy. As a result, the dynamics of power systems have changed, and many developing nations are adopting the integration of renewable energy into the grid to increase the aspect ratio of the energy availability factor. While both techniques contribute to the grid’s ability to meet energy demand, they frequently introduce technical challenges that affect the stability and protection of the systems. This paper provides a comprehensive review of the challenges introduced by transmission line compensation and the integration of renewable energy, as well as the various techniques proposed in the literature to address these issues. Different compensation techniques, including fault detection, classification, and location, for compensated and uncompensated transmission lines, including those connected to renewable energy sources, are reviewed. This paper then analyzes the adaptive distance protection schemes available in the literature to mitigate the impact of compensation/integration of RES into the grid. Based on the literature reviewed, it is essential for protection engineers to understand the dynamics introduced by network topology incorporating a combination of RES and heavily compensated transmission lines. Full article

The bacterial anode of microbial electrolysis cells (MECs) is the limiting factor in a high hydrogen evolution reaction (HER). This study focused on improving biofilm attachment to a carbon-cloth anode using an alginate hydrogel. In addition, the modified bioanode was encapsulated by a filter bag that served as a physical barrier, to overcome its low mechanical strength and alginate degradation by certain bacterial species in wastewater. The MEC based on an encapsulated alginate bioanode (alginate bioanode encapsulated by a filter bag) was compared with three controls: an MEC based on a bare bioanode (non-immobilized bioanode), an alginate bioanode, and an encapsulated bioanode (bioanode encapsulated by a filter bag). At the beginning of the operation, the Rct value for the encapsulated alginate bioanode was 240.2 Ω, which decreased over time and dropped to 9.8 Ω after three weeks of operation when the Geobacter medium was used as the carbon source. When the MECs were fed with wastewater, the encapsulated alginate bioanode led to the highest current density of 9.21 ± 0.16 A·m⁻² (at 0.4 V), which was 20%, 95%, and 180% higher, compared to the alginate bioanode, bare bioanode, and encapsulated bioanode, respectively. In addition, the encapsulated alginate bioanode led to the highest reduction currents of (4.14 A·m⁻²) and HER of 0.39 m³·m⁻³·d⁻¹. The relative bacterial distribution of Geobacter was 79%. The COD removal by all the bioanodes was between 62% and 88%. The findings of this study demonstrate that the MEC based on the encapsulated alginate bioanode exhibited notably higher bio-electroactivity compared to both bare, alginate bioanode, and an encapsulated bioanode. We hypothesize that this improvement in electron transfer rate is attributed to the preservation and the biofilm on the anode material using alginate hydrogel which was inserted into a filter bag. Full article

An integrated zero-carbon power plant aggregates uncontrollable green energy, adjustable load, and storage energy resources into an entity in a grid-friendly manner. Integrated zero-carbon power plants have a strong demand response potential that needs further study. However, existing studies ignore the green value of renewable energy in power plants when participating in demand response programs. This paper proposed a mathematical model to optimize the operation of an integrated zero-carbon power plant considering the green value. A demand response mechanism is proposed for the independent system operator and the integrated zero-carbon power plants. The Stackelberg gaming process among these entities and an algorithm based on dichotomy are studied to find the demand response equilibrium. Case studies verify that the mechanism activates the potential of the integrated zero-carbon power plant to realize the load reduction target. Full article

The primary purpose of this research is to determine the most economical approach to installing a solar dish Stirling engine (SDSE) system on a building for residential purposes in Mafraq while taking into account the local weather, usual monthly consumption of energy and the prices charged by the local powered utility. The house uses an average of 622.25 kWh of energy every month, with the highest consumption in February and the lowest in May. A range of optical efficiencies between 50% and 98% are used to mount the SDSE system. This study evaluated the relationship between the price of electrical energy and the amount of power consumed to identify the times of day when energy consumption is highest. Another approach relevant to consider is solar power, which likewise varies across the whole year. When the available intensity of the sun and power rates are at their peak, an SDSE system is regarded as a feasible solution for fulfilling the energy requirements. This is because SDSE systems can still make electricity even during cloudy days. This work also includes a comprehensive analysis of the solar power that an SDSE receives and the generated electrical power. Full article