Harnessing flow-induced vibrations for energy harvesting: Experimental and numerical insights using piezoelectric transducer.

Flow-induced vibrations (FIV) were considered as unwanted vibrations analogous to noise. However, in a recent trend, the energy of these vibrations can be harvested and converted to electrical power. In this study, the potential of FIV as a source of renewable energy is highlighted through experimental and numerical analyses. The experimental study was conducted on an elastically mounted circular cylinder using helical and leaf springs in the wind tunnel. The Reynolds number (Re) varied between 2300-16000. The motion of the cylinder was restricted in all directions except the transverse direction. The micro-electromechanical system (MEMS) was mounted on the leaf spring to harvest the mechanical energy. Numerical simulations were also performed with SST k-ω turbulence model to supplement the experiments and were found to be in good agreement with the experimental results. The flow separation and vortex shedding induce aerodynamic forces in the cylinder causing it to vibrate. 2S vortex shedding pattern was observed in all of the cases in this study. The maximum dimensionless amplitude of vibration (A/D) obtained was 0.084 and 0.068 experimentally and numerically, respectively. The results showed that the region of interest is the lock-in region where maximum amplitude of vibration is observed and, therefore, the maximum power output. The piezoelectric voltage and power output were recorded for different reduced velocities (Ur = 1-10) at different resistance values in the circuit. It was observed that as the amplitude of oscillation of the cylinder increases, the voltage and power output of the MEMS increases due to high strain in piezoelectric transducer. The maximum output voltage of 0.6V was observed at Ur = 4.95 for an open circuit, i.e., for a circuit with the resistance value of infinity. As the resistance value reduced, a drop in voltage output was observed. Maximum power of 10.5µW was recorded at Ur = 4.95 for a circuit resistance of 100Ω.

Capital structure and financial performance of China's energy industry: What can we infer from COVID-19?

The study aims to uncover the impact of COVID-19 and capital structure on the financial performance of 1787 renewable and nonrenewable energy firms in China from 2010 to 2022. Using the fixed effect approach, our study found that financial leverage negatively affected the return on assets and equity ratios for both renewable and nonrenewable energy. On the other hand, the study shows that COVID-19 adversely affected the financial performances of non-renewable energy firms. Conversely, COVID-19 positively affected the financial performances of renewable energy firms. The conclusions drawn by the present study are helpful for the policymakers in making corresponding financial decisions. The study suggests that policymakers must adopt profitable capital structure strategies for firms and shareholders in this context. Finally, policymakers must design more policies to overcome the adverse influence of the COVID-19 pandemic crisis and avoid any future unforeseeable pandemics.

Environmental impact assessment of ocean energy converters using quantum machine learning.

The depletion of fossil energy reserves and the environmental pollution caused by these sources highlight the need to harness renewable energy sources from the oceans, such as waves and tides, due to their high potential. On the other hand, the large-scale deployment of ocean energy converters to meet future energy needs requires the use of large farms of these converters, which may have negative environmental impacts on the ocean ecosystem. In the meantime, a very important point is the volume of data produced by different methods of collecting data from the ocean for their analysis, which makes the use of advanced tools such as different machine learning algorithms even more colorful. In this article, some environmental impacts of ocean energy devices have been analyzed using machine learning and quantum machine learning. The results show that quantum machine learning performs better than its classical counterpart in terms of calculation accuracy. This approach offers a promising new method for environmental impact assessment, especially in a complex environment such as the ocean.

Dynamic interactions of carbon trading, green certificate trading, and electricity markets: Insights from system dynamics modeling.

In the context of the evolving landscape of reduction in carbon emissions and integration of renewable energy, this study uses system dynamics (SD) modeling to explore the interconnected dynamics of carbon trading (CT), tradable green certificate (TGC) trading, and electricity markets. Using differential equations with time delays, the study provides a comprehensive analysis of structural relationships and feedback mechanisms within and between these markets. Key findings reveal the intricate interplay between carbon prices, green certificate prices, and electricity prices under various coupling mechanisms. For example, under the three-market coupling mechanism, carbon trading prices stabilize around 150 Yuan/ton, while green certificate prices reach a peak of 0.45 Yuan/KWH, impacting electricity prices, which fluctuate between 0.33 and 1.09 Yuan / KWH during the simulation period. These quantitative results shed light on nuanced fluctuations in market prices and the dynamics of anticipated purchases and sales volumes within each market. The insights gleaned from this study offer valuable implications for policy makers and market stakeholders in navigating the complexities of carbon emission reduction strategies, the integration of renewable energy and market equilibrium. By understanding the dynamics of multi-market coupling, stakeholders can better formulate policies and strategies to achieve sustainable energy transitions and mitigate impacts of climate change.

The time-varying dynamic impact of US renewable energy prices on agricultural prices in China: the case of fuel ethanol.

This paper intends to look into the time-varying dynamic impact of US fuel ethanol, one of the renewable energy sources, on the prices of agricultural products (specifically corn, soybeans, rice, and wheat) in China based on monthly price data from January 2000 to January 2023. To achieve this, a time-varying parameter vector autoregressive (TVP-VAR) model is employed, which takes into account structural changes in emergencies through time-varying parameters. The empirical results show that the equal-interval impulse responses of price fluctuations in agricultural commodities are primarily positive to variations in fuel ethanol prices and production. And the intensity and direction of the effects vary at distinct time lags. Additionally, the magnitude of these responses is most pronounced in the short term for all agricultural commodities except for corn, and the duration of the impulse responses at different time points is generally longer for corn prices compared to other commodities. The study also reveals that the influence of US fuel ethanol on Chinese agricultural commodity prices is not substantial on the whole. Therefore, there is a necessity to advance the growth of biofuels and provide policy support and financial subsidies for agricultural products earmarked for food production. These actions could shed insights into the progression of Chinese renewable energy and food policies, ensuring the stability of the market in the long run.

Biomass Ppower generation: A pathway to carbon neutrality.

In light of the pressing need to reduce carbon emissions, the biomass power generation industry has gained significant attention and has increasingly become a crucial focus in China. However, there are still considerable gaps in the historical background, status, and prospects of biomass power generation. Herein, the historical and current status of biomass power generation in China are systematically reviewed, with a particular emphasis on supportive policies, environmental impacts, and future projections. By 2022, the newly installed capacity for biomass power generation reached 3.34 MW with a total installed capacity of 41 MW. The power produced from biomass power generation is 182.4 billion kWh in China. The total installed capacity and generated power in 2022 were 1652 and 1139 folds higher than in 2006 when the first biomass generation plant was established. However, disparities in the distribution of biomass resources and power generation were observed. Key drivers of the industry development include tax, finance, and subsidy policies. Under the implementation of the 14th Five-Year Plan for renewable energy development and the goal of carbon neutrality, biomass power generation may achieve great success through more targeted policy support and advanced technologies that reduce air pollutant emissions. If combined with Bioenergy with Carbon Capture and Storage (BECCS) technology, biomass power generation will make its contribution to carbon neutrality in China.

Economic complexity, greenfield investments, and energy innovation: policy implications for sustainable development goals in newly industrialised economies.

The crucial role of environmental assessment quality has been recognised by environmental and sustainable development goals in addressing climate change challenges. By focusing on the key identifier of environmental assessment, progress can be made towards overcoming climate change issues effectively. The current study considers environmental commitments under COP28 to study the role of economic complexity, greenfield investments, and energy innovation in environmental degradation in newly industrialised economies from 1995 to 2021. We employ novel panel estimations from CS-ARDL, CS-DL, AMG, and CCEMG to confirm that economic growth and greenfield investments degrade environmental quality. On the other hand, energy innovation and urbanisation improve environmental sustainability. Lastly, we confirm the EKC hypothesis for economic complexity as well. Given the reported empirical findings, the study suggests policymakers must focus on economic complexity to transform industrial sectors' economic potential. Furthermore, foreign investment projects must be linked with environmental goals to increase renewable energy capacity.

Some inconvenient truths about decarbonization, the hydrogen economy, and power-to-X technologies.

The decarbonization of the energy sector has been a subject of research and of political discussions for several decades, gaining significant attention in the last years. It is commonly acknowledged that the most obvious way to achieve decarbonization is the use of renewable energy sources. Within the context of the energy sector decarbonization, many mainly industrialized countries recently started developing national plans to establish a hydrogen-based economy in the very near future. The plans for green hydrogen initially try to (a) target sectors that are difficult to decarbonize and (b) address issues related to the storage and transportation of CO2-free energy. To achieve almost complete decarbonization, electric power must be generated exclusively from renewable sources. In so-called Power-to-X (PtX) technologies, green hydrogen is generated from electricity and subsequently converted to another energy carrier which can be further stored, transported and used. In PtX, X stands, for example, for liquid hydrogen, methanol or ammonia. The challenges associated with decarbonization include those associated with (a) the expansion of renewable energies (e.g., high capital demand, political and social issues), (b) the production, transportation, and storage of hydrogen and the energy carriers denoted by X in PtX (e.g., high cost and low overall efficiency), and (c) the expected significant increase in the demand for electrical energy. The paper discusses whether and under which conditions the current national and international hydrogen plans of many industrialized countries could lead to a maximization of decarbonization in the world. It concludes that, in general, as long as the conditions for generating large excess amounts of green electricity are not met, the quick establishment of a hydrogen economy could not only be very expensive, but also counterproductive to the worldwide decarbonization efforts.

The role of renewable energy and total factor productivity in reducing carbon emissions: A case of top-ranked nations in the renewable energy country attractiveness index.

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results. 20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates. The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

Performance enhancement of short-term wind speed forecasting model using Realtime data.

The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively.

Analyzing the effectiveness of energy aid for driving the transition towards energy decarbonization; Evidence from Asian developing countries.

Environmental degradation poses a significant challenge in many developing countries, as they heavily rely on fossil fuels to drive economic activities. The transition towards renewable energy is crucial to mitigate ecological depletion, yet numerous Asian developing countries may struggle to achieve the desired levels of renewable energy adoption due to financial constraints. Foreign aid in the energy sector can expedite this transition process. This study aims to examine the impact of foreign aid on the energy decarbonization transition in 22 Asian developing countries from 2003 to 2022 to analyze its contributions and challenges to promote renewable energy adoption. This paper incorporates the two types of foreign aid in the energy sector (nonrenewable and renewable energy aid) provided by the OECD to developing economies. Utilizing the System-Generalized Method of Moments (Sys-GMM), the findings reveal that energy aid significantly contributes to the transition towards energy decarbonization by providing financial support for embracing renewable energy technologies. Specifically, the analysis indicates that a 1% increase in energy aid leads to approximately 3% enhancement in the decarbonization transition process. Moreover, this study adds to the existing body of knowledge by examining the mediating impact of human capital and financial development as well as the moderating effect of institutional quality and demand for clean fuel. These factors play a pivotal role in energy decarbonization transition by fostering financial development and enhancing human capital through capacity-building initiatives and facilitating the adoption of renewable energy technologies.

Hybrid optimal-FOPID based UPQC for reducing harmonics and compensate load power in renewable energy sources grid connected system.

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.

Beneath the blades: Marine wind farms support parts of local biodiversity - a systematic review.

Offshore wind energy developments in European waters are rapidly expanding to meet the increasing global demand for renewable energy. These developments provide new substrates for species colonisation, but also introduce changes in electromagnetic fields, noise levels, and hydrological conditions. Understanding how these man-made structures affect marine biodiversity across various species groups is crucial, yet our knowledge in this field remains incomplete. In this synthesis paper, based on 14 case studies conducted in northeastern Atlantic (North, Irish and Baltic seas), we aggregated species-level data on abundance, biomass, and other quantity proxies spanning the entire food chain from invertebrates to mammals, and compared these variables between wind farms and nearby control sites. Overall, our analysis revealed that in wind farm areas, species tend to occur at higher quantities than in control areas. Additionally, we noticed a slight trend where the positive effect of wind farms was more pronounced in newly established ones, gradually diminishing as wind farms aged. None of the tested covariates (depth, distance from coastline, years in commission) nor species' characteristics (habitat and spawning types, trophic level) showed statistical significance. When examining species groups individually, there was a tendency for wind farm areas to harbour higher quantities of polychaetes, echinoderms and demersal fishes. These findings suggest that wind farms contribute to the so-called reef-effect, providing shelter and food supplies to their inhabitants and acting as no-take-zones. Our results support the idea that wind farms could serve as zones of increased local biodiversity, potentially facilitating spillover effects to nearby areas for certain species groups. Further studies are necessary to gain a more comprehensive understanding of the adverse effects of wind farms on associated biodiversity, while also exploring avenues to amplify their positive impacts.

Does energy transition reduce carbon inequality? A global analysis.

Energy transition from fossil fuels to renewables is instrumental in mitigating climate change. Low-income countries have a higher share of renewable energy in their total energy consumption than rich countries (WDI, 2023). Thus, it is imperative to examine the role of energy transition in affecting relative CO2 emissions between rich and poor sections of the societies across income groups of the countries. In this context, our study contributes by constructing the carbon inequality models with renewable and non-renewable energy consumption as prime explanatory variables separately for 114 countries over a data period 1990-2019. The models are estimated individually for high-middle-low-income countries by controlling for foreign direct investment (FDI), economic growth, and innovations. Starting with preliminary econometric operations, we employ the dynamic simulated panel autoregressive distributed lag approach and Driscoll-Kraay standard error regression for empirical investigation. We find that energy transition reduces carbon inequality globally. Innovation has a negative impact, economic growth has a positive impact on carbon inequality, and FDI has an asymmetric impact based on the income level of the countries. The crucial global policy implications are discussed.

The crucial roles of ICT, renewable energy sources, industrialization, and institutional quality in achieving environmental sustainability in BRICS.

The BRICS countries-Brazil, Russia, India, China, and South Africa-are committed to achieving United Nations Sustainable Development Goal 13, which focuses on mitigating climate change. To attain this goal, it is crucial to emphasize the significance of ICT, renewable energy sources, industrialization, and institutional quality. This study contributes to the literature by examining the potential role of these factors in environmental sustainability in the BRICS economies from 2000 to 2021, utilizing cross-sectional augmented autoregressive distributed lag (CS-ARDL) estimation and other novel econometric techniques. Accordingly, the study suggests that BRICS governments and policymakers prioritize the use of ICT in the industrial and institutional sectors to achieve faster environmental sustainability in the short-run, as per the CS-ARDL results. However, the study advises caution in the long-term as the interaction between ICT and renewable energy sources, industrialization, and institutional quality may not favour environmental quality. Although the renewable energy sources interaction with ICT may not yield immediate progress, strong measures need to be taken to ensure that short-term gains are not nullified. In conclusion, the study highlights the potential of ICT, renewable energy sources, industrialization, and institutional quality in achieving environmental sustainability in the BRICS countries, while recommending cautious measures in the long run to safeguard the progress made.

Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impact assessment.

Environmental interactions of marine renewable energy developments vary from fine-scale direct (e.g. potential collision) to indirect wide-scale hydrodynamic changes altering oceanographic features. Current UK Environmental Impact Assessment (EIA) and associated Habitats Regulations Appraisal (HRA) guidelines have limited focus on underlying processes affecting distribution and movements (hence vulnerability) of top predators. This study integrates multi-trophic ship survey (active acoustics and observer data) with an upward-facing seabed platform and 3-dimensional hydrodynamic model as a process-driven framework to investigate predator-prey linkages between seabirds and fish schools. Observer-only data highlighted the need to measure physical drivers of variance in species abundances and distributions. Active acoustics indicated that in situ (preferable to modelled) data were needed to identify temporal changes in hydrodynamics to predict prey and consequently top predator presence. Revising methods to identify key habitats and environmental covariates within current regulatory frameworks will enable more robust and transferable EIA and HRA processes and outputs, and at larger scales for cumulative and strategic-level assessments, enabling future modelling of ecosystem impacts from both climate change and renewable energy extraction.

The refinery of the future.

Fossil fuels-coal, oil and gas-supply most of the world's energy and also form the basis of many products essential for everyday life. Their use is the largest contributor to the carbon dioxide emissions that drive global climate change, prompting joint efforts to find renewable alternatives that might enable a carbon-neutral society by as early as 2050. There are clear paths for renewable electricity to replace fossil-fuel-based energy, but the transport fuels and chemicals produced in oil refineries will still be needed. We can attempt to close the carbon cycle associated with their use by electrifying refinery processes and by changing the raw materials that go into a refinery from fossils fuels to carbon dioxide for making hydrocarbon fuels and to agricultural and municipal waste for making chemicals and polymers. We argue that, with sufficient long-term commitment and support, the science and technology for such a completely fossil-free refinery, delivering the products required after 2050 (less fuels, more chemicals), could be developed. This future refinery will require substantially larger areas and greater mineral resources than is the case at present and critically depends on the capacity to generate large amounts of renewable energy for hydrogen production and carbon dioxide capture.

Risk allocation schemes between public and private sectors in green energy projects.

In this research, we analyse how project risk allocation strategies impact the volume of private investment in renewable energy projects with the participation of both the public and private sectors. To this purpose, we analyse a sample formed by 2215 projects performed in 73 developing countries in the period 1997-2019 involving the following technologies: solar, hydro, wind, waste, biogas, biomass, and geothermal. Our findings reveal that those projects performed through governance schemes in which the private partner takes more project responsibilities attract more private money. Additional drivers for attracting private investment at the project level and institutional level are found. Furthermore, we reveal that the transference of project risks to the private partner emerges as a very relevant project feature that interacts with some of the project and institutional factors, revealing both complementary and substitution effects. The significance of this research extends beyond academia, since there are factors influencing private investment that can be controlled by various stakeholders in projects (such as policymakers, private investors, and project managers). Understanding their impact, significance, and interaction effects-factors that sometimes moderate or accentuate private investment-is crucial. The identified patterns illuminate optimal risk allocation practices, offering practical insights to enhance the effectiveness and sustainability of projects.

Perception of photovoltaic energy consumption in the Spanish primary sector. An environmentally profitable alternative.

Agriculture and livestock farming are activities that depend on energy consumption. Photovoltaic self-consumption systems can reduce the production costs of these actors, especially in periods of high-energy price volatility. This work aimed to determine the degree of implementation of photovoltaic self-consumption systems, their relationship with the economic impact of the energy crisis, and the perception of producers to use renewable energy sources in the Spanish agricultural system as it is one of the most important at European level. For this purpose, a survey of the Spanish agricultural and livestock system, involving 396 primary producers, was carried out between December 2022 and March 2023. The results suggest that self-consumption systems are in place in 49.1% of all farms and that these have had a positive effect in alleviating the rising energy costs suffered by the Spanish primary sector. In summary, Spanish primary producers generally have a favorable perception of the use of renewable energies on their farms, especially photovoltaic. However, the cluster analysis shows the fact that there are two types of producers, active and passive, from an environmental point of view. The characterization of this type of producers can help the Spanish Administration to improve the efficiency of its energy strategy, and can be a source of inspiration for the governments of other countries.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles.

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.