Considerations on environmental, economic, and energy impacts of wind energy generation: Projections towards sustainability initiatives

Experiment of a Cut-Out Piezoelectric Beam Energy Harvester Under Wind-Induced Vibration

To address the low electromechanical conversion efficiency associated with traditional single-degree-of-freedom (SDOF) piezoelectric energy harvesters, this study proposes a two-degree-of-freedom (2-DOF) cut-out piezoelectric beam for wind-induced vibration energy harvesting. Experimental comparisons conducted on four bluff bodies indicated that the triangular column exhibits superior aerodynamic stability, achieving an output voltage of 11.6 V at a wind speed of 7.0 m/s. Furthermore, the cut-out piezoelectric beam demonstrated a 1.9-fold increase in output voltage compared to its non-cut-out counterpart. These results underscore the potential of the 2-DOF cut-out piezoelectric beam design as an autonomous power solution for IoT nodes operating in complex environments.

RUL forecasting for wind turbine predictive maintenance based on deep learning

Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance. However, the remoteness of wind farms often renders current methodologies ineffective, as they fail to provide a sufficiently reliable advance time window for maintenance planning, limiting PdM's practicality. This study introduces a novel deep learning (DL) methodology for future-RUL forecasting. By employing a multi-parametric attention-based DL approach that bypasses feature engineering, thereby minimizing the risk of human error, two models-ForeNet-2d and ForeNet-3d-are proposed. These models successfully forecast the RUL for seven multifaceted wind turbine (WT) failures with a 2-week forecast window. The most precise forecast deviated by only 10 minutes from the actual RUL, while the least accurate prediction deviated by 1.8 days, with most predictions being off by only a few hours. This methodology offers a substantial time frame to access remote WTs and perform necessary maintenance, thereby enabling the practical implementation of PdM.

Organic Electrode Materials for Dual-Ion Batteries

Organic materials are widely used in various energy storage devices due to their renewable, environmental friendliness and adjustable structure. Dual-ion batteries (DIBs), which use organic materials as the electrodes, are an attractive alternative to conventional lithium-ion batteries for sustainable energy storage devices owing to the advantages of low cost, environmental friendliness, and high operating voltage. To date, various organic electrode materials have been applied in DIBs. In this review, we present the development of DIBs with a following brief introduction of characteristics and mechanisms of organic materials. The latest progress in the application of organic materials as anode and cathode materials for DIBs is mainly reviewed. Finally, we also discussed the challenges and prospects of organic electrode materials for DIBs.

Trifolium repens L.-Like Periodic Micronano Structured Superhydrophobic Surface with Ultralow Ice Adhesion for Efficient Anti-Icing/Deicing

Wind turbine blades are often covered with ice and snow, which inevitably reduces their power generation efficiency and lifetime. Recently, a superhydrophobic surface has attracted widespread attention due to its potential values in anti-icing/deicing. However, the superhydrophobic surface can easily transition from Cassie-Baxter to Wenzel at low temperature, limiting its wide applications. Herein, inspired by the excellent water resistance and cold tolerance of Trifolium repens L. endowed by its micronano structure and low surface energy, a fresh structure was prepared by combining femtosecond laser processing technology and a boiling water treatment method. The prepared icephobic surface aluminum alloy (ISAl) mainly consists of a periodic microcrater array, nonuniform microclusters, and irregular nanosheets. This three-scale structure greatly promotes the stability of the Cassie-Baxter state. The critical Laplace pressure of ISAl is up to 1437 Pa, and the apparent water contact angle (CA) is higher than 150° at 0 °C. Those two factors contribute to its excellent anti-icing and deicing performances. The results show that the static icing delay time reaches 2577 s, and the ice adhesion strength is only 1.60 kPa. Furthermore, the anti-icing and deicing abilities of the proposed ISAl were examined under the environment of low temperature and high relative humidity to demonstrate its effectiveness. The dynamic anti-icing time of ISAl in extreme environments is up to 5 h, and ice can quickly fall with a speed of 34 r/min when it is in a horizontal rotational motion. Finally, ISAl has excellent reusability and mechanical durability, with the ice adhesion strength still being less than 6 kPa and the CA greater than 150° after 15 cycles of icing-deicing tests. The proposed structure would offer a promising strategy for the efficient anti-icing and deicing of wind turbine blades.

Embracing the future of circular bio-enabled economy: unveiling the prospects of microbial fuel cells in achieving true sustainable energy

Sustainable development and energy security, highlighted by the United Nations Sustainable Development Goals (SDGs), necessitate the use of renewable and sustainable energy sources. However, upon careful evaluation of literature, we have discovered that many existing and emerging renewable energy systems (RESs) prioritize renewability over true sustainability. These systems not only suffer from performance inconsistencies and lack of scalability but also fall short in fully embodying the principles of sustainability and circular economy. To address this gap, we propose considering microbial fuel cells (MFCs) as a viable alternative and integral part of the renewable energy ecosystem. MFCs harness the omnipresence, abundance, and cost-effectiveness of their essential components, making them a promising candidate. Through our comprehensive analysis, we shed light on the limitations and advancements of this technology, which underscore the remarkable potential of MFCs to revolutionize our perception of clean, sustainable energy.

EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation

Wind power is the most promising renewable energy source after hydropower because of its mature technology and low price, and has great potential for carbon emission reduction. Long-term forecasts of its power generation can help power companies to develop operational plans, grid configuration and power dispatch, and can also provide a basis for the government to formulate energy and environmental policies. However, due to the characteristics of China's monsoon climate and wind power industry development, wind power generation data are characterized by nonlinear cycles and small data volume, which makes accurate prediction more difficult. To this end, this paper develops a new prediction model and applies it to the long-term prediction of wind power generation in China, and proposes some targeted policy recommendations based on the prediction results to promote the development of China's wind power industry.

Surface-Treated Recycling Fibers from Wind Turbine Blades as Reinforcement for Waste Phosphogypsum

An attempt at the treatment of the waste fiber (WF) from the wind turbine blade (WTB) was made through the modifier of dopamine hydrochloride and the compound modifier of dopamine hydrochloride and 2,5-dihydroxy terephthalic acid or 3,4-dihydroxy cinnamic acid or 3,4-dihydroxy benzonitrile, corresponding to obtain four modified waste fibers (MWF1, MWF2, MWF3, and MWF4). The MWFs samples' microstructure properties were characterized using SEM, EDS, XPS, FTIR analyses, and water contact angle tests. The results revealed that all the MWF surfaces were wrapped by a distinct coating layer and had different elemental compositions and chemical groups, demonstrating the significant effect of the four modifications on the WF surfaces. The hydroxyl, amino, or nitrile groups were grafted onto the WF surfaces causing improvement of the hydrophilicity and reactivity. Furthermore, all the MWFs as the reinforced materials were incorporated into the industrial waste phosphogypsum (PG) to manufacture the phosphorous-building gypsum composites (PBGC). The effects on the micro-morphology and mechanical properties of the PBGC were evaluated. The results also show the improvement in flexural and compressive strength with the addition of MWFs into the PBGC, due to the enhancement of the compactness between the MWF and phosphogypsum matrix. In particular, the effects of three compound modifiers on the flexural and compressive strength are more significant. The highest flexural and compressive strength was contributed by the PBGC-MWF4 with 2% dosage using a compound modifier of dopamine hydrochloride and 3,4-dihydroxy benzonitrile, which were enhanced 61.04% and 25.97% compared with the PBG.

Artificial neural networks to differentiate the composition and pyrolysis kinetics of fresh and long-stored maize

In this study, a novel methodology to determine plant biomass composition using artificial neural networks (ANN) is presented. This study was performed to determine the changes in the composition of fresh and 12 month-long stored biomass samples. The production of biofuels is a common method used to manage agricultural waste. However, owing to the seasonal characteristics of cultivation, storage is necessary in the production chain. The results indicated that cellulose and lignin were stable over time, with a maximum drop of 2.82 pp and 1.72 pp, respectively. Hemicellulose was determined to be less stable, with a drop of up to 9.19 pp after 12 months of storage. Regarding the kinetic parameters, the stored samples required a lower activation energy, but only for the active phase of pyrolysis. The accuracy of the proposed tool was extremely high, with a relative percentage difference as low as 12.9%.