Renewable Energy Production Research Articles

From policy stringency to environmental resilience: Unraveling the dose-response dynamics of environmental parameters in OECD countries

In the pursuit of sustainable development and the mitigation of negative externalities arising from environmental stressors, a multitude of member countries within the Organization for Economic Co-operation and Development (OECD) have embraced various environmental policies. Nevertheless, previous empirical research has largely overlooked investigating the causal relationship between the Environmental Policy Stringency (EPS) and environmental indicators. To bridge this gap, we conduct an empirical study to examine the dose-response relationship between EPS, as a continuous treatment variable, and selected environmental indicators. Our findings reveal evidence of a mixed treatment effect of policy stringency on the environment. Notably, the implementation of EPS leads to a reduction in carbon emissions per capita and an overall decline in ecological footprint. However, despite OECD countries moving towards a negative trajectory to preserve the environment, carbon intensity remains steadfastly on a positive path. Moreover, the intensification of policy stringency demonstrates positive effects on total renewable energy production, encompassing solar, wind, and eco-innovation. Building upon these empirical results, we offer several policy implications for the adoption and implementation of environmental policies within OECD countries.

Climate mitigation technology for holistic resource management in sub-Saharan Africa: Impact on greenhouse gas emissions

This study investigates the impact of climate change mitigation technologies, specifically agricultural land management and renewable energy consumption and production, on greenhouse gas emissions (GHG) in sub-Saharan Africa (SSA) over the period of 1991 to 2015. Our analysis was conducted using an ARDL panel data model with data from 26 countries representing four sub-regions. The results demonstrated that an increase in renewable energy consumption is significantly associated with a decrease in GHG emissions, with a long-term coefficient of -0.422 and a short-term coefficient of -0.757. Additionally, natural resource rents, agricultural land use and population density have a positive impact on greenhouse gas emissions, with coefficients of 0.0605, 0.392 and 0.690, respectively. However, renewable energy production does not have a significant effect on greenhouse gas emissions. This suggests that promoting renewable energy consumption can be an effective way to combat greenhouse gas emissions in the region, and policymakers should implement policies and programs that encourage and facilitate the adoption of renewable energy whilst taking into consideration the impact of agricultural land use. Overall, this study emphasizes the importance of promoting renewable energy consumption and managing agricultural land use as a viable approach to combating greenhouse gas emissions in sub-Saharan Africa, and highlights the potential of climate mitigation technology as a tool for regulators to optimize policy development and counter climate change.

Intelligent Low-Consumption Optimization Strategies: Economic Operation of Hydropower Stations Based on Improved LSTM and Random Forest Machine Learning Algorithm

The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of variables in optimal load distribution, which limit the progress of research. In this paper, we propose a new optimal method of the economic operation of hydropower stations based on improved Long Short-Term Memory neural network (I-LSTM) and Random Forest (RF) algorithm. Firstly, in order to accurately estimate the water consumption, the LSTM model’s hyperparameters are optimized using improved particle swarm optimization, and the I-LSTM method is proposed to fit the flow characteristic curve of the hydraulic turbines. Secondly, the Random Forest machine learning algorithm is introduced to establish a load-distribution model with its powerful feature extraction and learning ability. To improve the accuracy of the load-distribution model, we use the K-means algorithm to cluster the historical data and optimize the parameters of the Random Forest model. A Hydropower Station in China is selected for a case study. It is shown that (1) the I-LSTM method fits the operating characteristics under various working conditions and actual operating characteristics of hydraulic turbines, ensuring that they are closest to the actual operating state; (2) the I-LSTM method is compared with Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Long Short-Term Memory neural network (LSTM). The prediction results of SVM have a large error, but compared with ELM and LSTM, MSE is reduced by about 46% and 38% respectively. MAE is reduced by about 25% and 21%, respectively. RMSE is reduced by about 27% and 24%, respectively; (3) the RF algorithm performs better than the traditional dynamic programming algorithm in load distribution. With the passage of time and the increase in training samples, the prediction accuracy of the Random Forest model has steadily improved, which helps to achieve optimal operation of the units, reducing their average total water consumption by 1.24%. This study provides strong support for the application of intelligent low-consumption optimization strategies in hydropower fields, which can bring higher economic benefits and resource savings to renewable energy production.

A Research Paper on Design & Development of HHO Generator

This abstract encapsulates the essence of the research paper focusing on the design, development, and testing of an HHO (Hydrogen-Hydrogen-Oxygen) generator. The study aims to investigate the feasibility and efficacy of HHO technology in producing hydrogen gas for various applications, including engine decarbonization, renewable energy production, and industrial processes. The research begins with an in-depth exploration of HHO generation principles, electrolysis techniques, and system components. Through systematic design and engineering processes, a prototype HHO generator is developed, integrating innovative features to enhance efficiency, safety, and reliability. Experimental testing is conducted to evaluate the performance of the HHO generator across a range of operating conditions. In this study, we explore the utilization of oxy-hydrogen gas (HHO) as an supplementary source for internal combustion engines. The electrolysis process of water with an electrolyte (such as KaOH) produces HHO gas, which can address fossil fuel shortages and mitigate environmental pollution.

Sustainability of underground infrastructure – Part 1: Digitalisation-based carbon assessment and baseline for TBM tunnelling

Alongside the transition towards sustainable and renewable infrastructure and energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size and complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on the Architecture, Engineering and Construction (AEC) industry, with a shift in focus to cost and carbon management across the infrastructure lifecycle through the use of digitalisation techniques and sustainability rating systems. This paper aims to develop a systematic framework to enhance the sustainability of underground infrastructure in the early design and construction stages, aiming for “build clever and efficiently”. This framework integrates digitalisation, carbon assessment standards, numerical modelling, and optimisation techniques to assess carbon emissions and establish benchmarks for construction products and processes. The approach uses parametric models to define tunnelling-related entities such as geological formations, TBM, and segments, designed with dimensional constraints that allow for adaptive modelling and interactions. Enabled by building information modelling (BIM) through parametric models enriched with domain properties, module-based carbon accounting is adopted to evaluate the footprint of material- and asset-level key emitters with functional units. Leveraging established databases, baselines for segmental lining designs and carbon reduction strategies are established. Through project-driven case studies, the correlations among geotechnical conditions, tunnel dimensions, and TBM operating parameters are developed to quantify TBM carbon emissions. A prototype programme demonstrates the framework's application on typical metro and road tunnels at different levels of details (LoD). This paper serves as a guideline for conducting embodied carbon assessments specifically tailored to TBM tunnelling, acknowledging the substantial contribution of the TBM construction process, which is controlled by geotechnical conditions.

Optimization of syngas production via methane bi-reforming using CeO2 promoted Cu/MnO2 catalyst

Currently, syngas plays an important role in renewable and sustainable energy production. The idea of manufacturing syngas via bi-reforming methane, which involves the combination of methane (CH4), carbon dioxide (CO2), and steam, appears very promising. As a result, the goal of this research is to improve syngas output by improving process parameters in methane bi-reforming using a 3%Ce-15%Cu/MnO2 catalyst. Optimization analysis was performed using response surface methodology (RSM). The ultrasonic impregnation (UI) method was employed to synthesize the catalysts used in this study. Following that, the catalyst was characterized using several techniques such as Brunauer-Emmett-Teller (BET), X-ray diffraction (XRD), temperature programmed reduction (TPR), temperature programmed desorption (TPD), and temperature programmed oxidation (TPO). The findings of the characterization show that the presence of CeO2 promoters has a dual effect on the size of CuO crystallites. Firstly, it reduces the size from 19.07 nm to 13.66 nm because to the dilutive effect generated by the inclusion of CeO2. Second, the presence of CeO2 promoter accelerates the transition from CuO to Cu0 metallic phase. Furthermore, the addition of CeO2 boosts the CH4 and CO2 conversion rates by 23.65% and 24.93%, respectively. As a result, the H2 yield increases significantly when compared to the unpromoted catalyst. The study investigates the influence of process parameters, specifically the reaction temperature (700–900℃), CO2 ratio (0.2–1), and gas hourly space velocity (GHSV) (16–36 L g cat−1 hr−1), on the conversion of CH4 and CO2, as well as the H2/CO ratio. The optimization study finds that the highest conversion rates for CH4 and CO2 are 78.32% and 72.45%, respectively, when the reaction temperature is 800 °C, the CO2 ratio is 0.6, and the gas hourly space velocity (GHSV) is 26 L g cat−1 hr−1. The optimum conditions result in the highest syngas ratio of 1.77. The results of the optimization are then assessed using the mean errors. The H2/CO ratio, as well as the average errors for CH4 and CO2 conversions, are discovered to be 0.15%, 0.95%, and 0%, respectively.

Smart transportation systems using learning method for urban mobility and management in modern cities

The pressing demand for an escalation in renewable energy production arises from the surge in power consumption, escalating environmental concerns, and the exhaustion of fossil fuels. Microgrids, featuring dispersed power sources, exhibit superior resilience, efficiency, and stability in contrast to conventional power grids. Noteworthy investments in eco-friendly initiatives have been made by various major corporations, particularly in institutional structures. The current research advocates for the integration of photovoltaics, energy storage, and electric vehicles (EVs) as pivotal elements in a smart grid. This study introduces an optimal energy management system (EMS) designed to efficiently allocate power from existing sources. To address the challenges within an uncertain and reliable environment, a stochastic framework leveraging the whale optimization algorithm and unscented transform has been formulated. All simulations have been executed using the digital twin of the smart grid components. Results suggest that the proposed EMS ensures uninterrupted power supply, contributing to a substantial 45% reduction in energy costs. Furthermore, the potential of EVs as storage devices is explored. When employed as energy sources, EVs led to a noteworthy 45.58% reduction in power prices, while their use as loads still yielded a significant 19.33% decrease. The study also delves into the analysis of outage effects on the continuous power supply cost.

Environmental performance analysis of textile envelope integrated flexible photovoltaic using life cycle assessment approach

The textile envelope integrated flexible photovoltaic (TE-FPV) system is an emerging technology to promote building sustainability due to its lightweight structure, textile recovery easily, and renewable energy production. We develop six novel TE-FPV prototypes to determine their advantages of environmental performance. Especially, the systems with the solar cells directly printed on the textile are designed to show the promising sustainability prospect. Their environmental performances are conducted by a cradle-to-cradle life cycle assessment, and the results are compared to traditional building integrated photovoltaic (BIPV) technologies. The life cycle inventories of some textiles and solar cells are the first time to be developed. Based on the inventories, life cycle impact assessment is implemented to figure out environmental impacts in various categories. Furthermore, uncertainty analyses and sensitivity analyses are performed to unmask the effects of parameters on the fluctuation of environmental indicators. The results present that most of TE-FPV systems perform more sustainable and more recyclable over the traditional BIPV systems. The organic solar cell is unfavorable for the TE-FPV systems due to its low power conversion efficiency and short lifespan. ETFE facade integrated perovskite solar cell systems can refund the investment of greenhouse gas emission even on the north orientation, showing vast prospective potential in environmental performances. This work contributes significantly required environmental quantifications to promote the advancement of TE-FPV systems.

Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel’s efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems’ classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study.

Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Women in power: the role of gender in renewable energy policymaking

Do female policymakers encourage the production of renewable energy compared to their male counterparts? Using instrumental variables, we conduct a cross-country analysis of 39 high-income countries for the years 1997–2020 using quota laws and women’s suffrage as instruments for women’s participation in the parliament. We find that a 1 percentage point increase in the proportion of women in the legislature increases renewable energy production by 1.54 percentage points. This study suggests that fostering policies that boost women’s participation in policy-making positions is beneficial, especially when considering the positive spillover to other countries.

Simulating a Massive Shift towards Photovoltaics without Implying Land Consumption: A Stepwise Approach for Regional Decision Making

As part of the transition to renewable energy production, the use of photovoltaics is not sustainable per se. Planning can guide PV development while minimising possible negative impacts on society, the economy, and the environment. To date, however, the lack of adequate tools and information at the local level and the lack of integrated planning in the design of energy initiatives are key challenges. This paper aims to support a sustainable energy transition at the regional level and proposes a methodology to (i) determine the area required to achieve transition goals through PV; (ii) evaluate possible alternative surface categories for PV installation without consuming additional land. Through the illustrative case study of the Veneto Region, electricity demand is calculated based on consumption data, and the area needed to cover this demand through PV is calculated. Areas available for PV installation were mapped, classified (e.g., roofs of houses, parking lots, school buildings, …), and quantified through GIS. The results show that the available areas correspond to 11 times those needed to achieve the goals. This work provides a methodology to support regional decision makers to take a concrete step toward sustainable transition policies. This methodology can be replicated for other regional contexts.

Role of financial stability, environmental regulations and uncertain economic policies in CO2 emission and investment in renewable energy: Evidence from China

Carbon emissions, ecological pollution and a steadily rising global temperature have been widely acknowledged as the most severe risks to human survival in the last few decades. Alarming increases in global temperature and sudden climatic shifts are nature's way of warning us to curb the use of fossil fuels and adopt more sustainable practices. Therefore, the present study investigates the impact of financial stability, environmental regulations and uncertain economic policies on carbon emissions and investment in renewable energy. The study used a nonparametric DEA‐DDF technique to fulfil this objective using a balanced panel dataset comprising 28 Chinese provinces from 2011 to 2021. Overall results demonstrated that financial stability reduces carbon emissions and accelerates investment in renewable energy projects. The findings imply that a financially stable economy like China encourages businesses to invest in cutting‐edge, environmentally friendly technology to boost productivity while reducing carbon emissions. Likewise, results show that stringent ecological regulations decrease carbon emissions and promote investment in renewable energy. Hence, stakeholders are keen to comply with environmental regulations in China to reduce carbon emissions by investing in renewable energy resources to avoid penalties. Finally, results suggest that uncertain economic policies increase carbon emissions and restrict access to credit from financial institutions for investment in renewable energy purposes in China. Findings imply that uncertainty in economic policies could lead to less environmentally friendly production practices that may increase carbon emissions and reduce the demand for renewable energy products.

A Life Cycle Assessment of Methane Slip in Biogas Upgrading Based on Permeable Membrane Technology with Variable Methane Concentration in Raw Biogas

While energy-related sectors remain significant contributors to greenhouse gas (GHG) emissions, biogas production from waste through anaerobic digestion (AD) helps to increase renewable energy production. The biogas production players focus efforts on optimising the AD process to maximise the methane content in biogas, improving known technologies for biogas production and applying newly invented ones: H2 addition technology, high-pressure anaerobic digestion technology, bioelectrochemical technology, the addition of additives, and others. Though increased methane concentration in biogas gives benefits, biogas upgrading still needs to reach a much higher methane concentration to replace natural gas. There are many biogas upgrading technologies, but almost any has methane slip. This research conducted a life cycle assessment (LCA) on membrane-based biogas upgrading technology, evaluating biomethane production from biogas with variable methane concentrations. The results showed that the increase in methane concentration in the biogas slightly increases the specific electricity consumption for biogas treatment, but heightens methane slip with off-gas in the biogas upgrading unit. However, the LCA analysis showed a positive environmental impact for treating biogas with increasing methane concentrations. This way, the LCA analysis gave a broader comprehension of the environmental impact of biogas upgrading technology on GHG emissions and offered valuable insights into the environmental implications of biomethane production.

Efficient bio-hydrogen production by dark co-fermentation of food-rich municipal solid waste and urban pruning wastes of pine, cypress, and berry

Bio-hydrogen (bio-H2) production from dark co-fermentation of pruning wastes, from pine, cypress, and berry trees, and food-rich municipal solid waste (FMSW) was evaluated as a promising way to combine waste management and renewable energy production. The inoculum was initially optimized by heat-shock pretreatment at 65, 75, 85, and 95 ℃ for 15, 30, and 45 min to maximize bio-H2 yields. The optimum pretreatment conditions (75 °C for 45 min) were applied for dark co-fermentation analyses. The highest bio-H2 production yield of 84 ± 6 mL/g VS was obtained by dark co-fermentation of 20 g/L pruning wastes and FMSW under initially neutral conditions. The fermentation of individual substrates led to 40.9, 0.2, 0.1, and 0.2 mL/g VS from FMSW, pine, berry, and cypress, respectively, 85% lower than that obtained through dark co-fermentation. Therefore, co-processing of a “stiff substrate”, lignocellulose, with a “loose part”, starchy FMSW, leads to a higher bio-H2 yield. Dark co-fermentation led to the bio-conversion of more than 76% of starch, 31% of glucan, and 41% of hemicellulose, besides 41% lignin removal. The modified Gompertz model (MGM) and Logistic model (MLM) were well-fitted on kinetic data (with R2 values ≥0.98), and the kinetic parameters were calculated.

Impact of Energy Use on Air Pollution: Evidence from OCED Countries

Air pollution poses significant environmental and health risks, with numerous studies suggesting its dire consequences ranging from respiratory diseases to global climate change implications. In this context, understanding energy consumption patterns and their impact on air pollution is critical, especially in developed nations with high energy consumption rates. This paper empirically analyzes the impact of energy use on air pollution in OECD countries – a group of nations that play a pivotal role in global energy consumption and policy-making. Despite their economic advancements, the implications of their energy choices on air quality have not been extensively studied, presenting a gap in the literature. Spanning 12 years from 2010 to 2021, our research encompasses a total of 456 observations, employing panel data. We aim to unveil the relationship between energy use, both traditional and renewable and air pollution levels. The variables studied include Energy use per capita, Alternative and nuclear energy, Greenhouse gas emissions, Energy imports, Access to electricity, and the production of renewable energy (air, water, solar, geothermal). Leveraging various econometric models such as OLS, OLS Robust, fixed, and random effects models, our findings reveal that electricity use exacerbates air pollution levels. Particularly, the increase in carbon dioxide, coupled with the rising access to electricity, deteriorates air quality. In contrast, electricity sourced from renewables like water, wind, nuclear, and geothermal energy aids in mitigating air pollution, emphasizing the importance of sustainable energy choices for future policy considerations in OECD nations.

Thermostable CaCO3-Immobilized Bacillus subtilis Lipase for Sustainable Biodiesel Production from Waste Cooking Oil

Due to the increasing demand for green processes in renewable energy production, the extracellular Bacillus subtilis B-1-4 lipase was used as a biocatalyst for producing biodiesel from waste cooking oil. Response surface methodology was employed for the optimization of enzyme production. Lipase activity was modeled with a quadratic function of four factors that primarily influence the culture medium. Thanks to this model, an optimal lipase activity of 1.7 ± 0.082 U/mL was achieved with the best culture medium composition: 16 g/L of tryptone, 15 g/L of yeast extract, 15 g/L of NaCl, and a 0.15 initial optical density at 600 nm (OD600 nm). The maximal lipase activity was measured at 45 °C and pH 8, using para-nitrophenyl palmitate as a substrate. The enzyme maintained above 94% and 99% of its initial activity at temperatures ranging from 40 to 50 °C and at pH 8, respectively. Moreover, it exhibited a higher residual activity than other Bacillus lipases in the presence of organic solvents. Residual activities of 86.7% and 90.2% were measured in the presence of isopropanol and ethanol, respectively. The lipase was immobilized by adsorption onto CaCO3 powder. FT-IR and SEM were used to characterize the surface-modified support. After immobilization, a lipase activity of 7.1 U/mg of CaCO3 was obtained. Under the optimized conditions, the highest biodiesel yield of 71% was obtained through the transesterification of waste cooking oil using the CaCO3-immobilized Bacillus subtilis lipase. This research reveals a method for the utilization of waste cooking oil for biodiesel production using an efficient immobilized thermostable lipase, providing environmental and economic security.

Renewable energy and carbon-neutral gaming: A holistic approach to sustainable electricity

Carbon emissions are a global issue around the world due to energy consumption, and to resolve this, increasing focus has been placed on attaining carbon neutrality. This research bridges the gap in related literature by considering perspectives on electricity. We develop a regression model from renewable energy consumption and production and apply game theory to get different decisions on electricity production from renewable energy (RE) production-consumption and subsidies on RE resources. Subsequently, a simulation-driven optimization method is employed to generate various scenarios. Various experiments are manipulated to find the optimal decisions of carbon-neutral indicators. Results indicate that subsidies on renewable energy that generate electricity exhibit a significant role in maximizing CO2 emission reduction. It is found that subsidies on wind energy consumption and production play a pivotal role in mitigating carbon emissions. This study elaborates on a valuable contribution in providing optimal decisions for achieving carbon neutrality in RE production and consumption. It offers guidelines to pay attention to the policies about RE resources to make the environment clean. Ultimately, this research not only presents optimal pathways for attaining carbon neutrality but also marks the necessity of targeted policies focused on RE resources to promote a cleaner and sustainable environment globally.

How do digital financial inclusion, ICT diffusion, and education affect energy security risk in top energy-consuming countries?

Energy security has become a crucial issue as the world economy depends more and more on energy supplies. In the context of the top energy-consuming nations, this research examines the connection between digital financial inclusion, information and communication technology (ICT), education, and energy security risk from 2011 to 2022. To that end, the study applies the two-stage least squares and system generalized method of moments estimation techniques. According to our study's findings, digital financial inclusion and associated factors, including automatic teller machines, bank branches, debit cards, and electronic payments, mitigate energy security risks. The energy security risks are also reduced due to ICT, education, gross domestic product, and renewable energy production. However, carbon emissions increase energy security risks. These findings suggest that policymakers in top energy-consuming economies should focus on the digital inclusiveness of the financial sector, ICT diffusion, and human capital to increase the opportunities for investment in the energy sector to mitigate energy security risks.

A Modeling Analysis of Wastewater Heat Recovery Effects on Wastewater Treatment Plant Nitrification

A global shift towards renewable energy production, driven primarily by the challenges posed by climate change, is currently underway. In this context, the utilization of heat recovery from municipal wastewater emerges as a promising green technology. Notably, the advantage of implementing energy recovery in sewers, as opposed to wastewater treatment plants (WWTPs), lies in the higher temperature of the wastewater and its proximity to potential heat users. Despite these benefits, concerns arise regarding the potential adverse effects on biological wastewater treatment processes downstream of the heat recovery section, particularly during colder seasons. This paper seeks to assess the impact of a heat recovery system along the sewer network on the efficiency of biological wastewater treatment processes. The methodology involves a modeling analysis of a real sewage network in Italy. Under typical northern Italy climate conditions, the results demonstrate the feasibility of heat recovery in sewers for WWTPs designed with a sludge residence time under aerobic conditions (SRTaer) greater than 13 days. In such cases, the nitrification process remains relatively unaffected. However, for lower SRTaer values, a case-specific feasibility assessment is recommended to evaluate the overall process efficiency comprehensively.