Frontiers in Energy最新文献

With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energy sources (RESs) has become a trend in the industry. However, the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids. In this paper, a multi-timescale optimal scheduling model is established for interconnected data centers (IDCs) based on model predictive control (MPC), including day-ahead optimization, intraday rolling optimization, and intraday real-time correction. The day-ahead optimization stage aims at the lowest operating cost, the rolling optimization stage aims at the lowest intraday economic cost, and the real-time correction aims at the lowest power fluctuation, eliminating the impact of prediction errors through coordinated multi-timescale optimization. The simulation results show that the economic loss is reduced by 19.6%, and the power fluctuation is decreased by 15.23%.

Highly efficient and stable iron electrodes are of great significant to the development of iron-air battery (IAB). In this paper, iron nanoparticle-encapsulated C–N composite (NanoFe@CN) was synthesized by pyrolysis using polyaniline as the C–N source. Electrochemical performance of the NanoFe@CN in different electrolytes (alkaline, neutral, and quasi-neutral) was investigated via cyclic voltammetry (CV). The IAB was assembled with NanoFe@CN as the anode and IrO₂ + Pt/C as the cathode. The effects of different discharging/charging current densities and electrolytes on the battery performance were also studied. Neutral K₂SO₄ electrolyte can effectively suppress the passivation of iron electrode, and the battery showed a good cycling stability during 180 charging/discharging cycles. Compared to the pure nano-iron (NanoFe) battery, the NanoFe@CN battery has a more stable cycling stability either in KOH or NH₄Cl + KCl electrolyte.

Polyimide (PI) has emerged as a promising organic photocatalyst owing to its distinct advantages of high visible-light response, facile synthesis, molecularly tunable donor-acceptor structure, and excellent physicochemical stability. However, the synthesis of high-quality PI photoelectrode remains a challenge, and photoelectrochemical (PEC) water splitting for PI has been less studied. Herein, the synthesis of uniform PI photoelectrode films via a simple spin-coating method was reported, and their PEC properties were investigated using melamine as donor and various anhydrides as acceptors. The influence of the conjugate size of aromatic unit (phenyl, biphenyl, naphthalene, perylene) of electron acceptor on PEC performance were studied, where naphthalene-based PI photoelectrode exhibited the highest photocurrent response. This is resulted from the unification of wide-range light absorption, efficient charge separation and transport, and strong photooxidation capacity. This paper expands the material library of polymer films for PEC applications and contributes to the rational design of efficient polymer photoelectrodes.

The hydrogen fuel cell vehicle is rapidly developing in China for carbon reduction and neutrality. This paper evaluated the life-cycle cost and carbon emission of hydrogen energy via lots of field surveys, including hydrogen production and packing in chlor-alkali plants, transport by tube trailers, storage and refueling in hydrogen refueling stations (HRSs), and application for use in two different cities. It also conducted a comparative study for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs). The result indicates that hydrogen fuel cell vehicle (FCV) has the best environmental performance but the highest energy cost. However, a sufficient hydrogen supply can significantly reduce the carbon intensity and FCV energy cost of the current system. The carbon emission for FCV application has the potential to decrease by 73.1% in City A and 43.8% in City B. It only takes 11.0%–20.1% of the BEV emission and 8.2%–9.8% of the ICEV emission. The cost of FCV driving can be reduced by 39.1% in City A. Further improvement can be obtained with an economical and “greener” hydrogen production pathway.

The Haber-Bosch process is the most widely used synthetic ammonia technology at present. Since its invention, it has provided an important guarantee for global food security. However, the traditional Haber-Bosch ammonia synthesis process consumes a lot of energy and causes serious environmental pollution. Under the serious pressure of energy and environment, a green, clean, and sustainable ammonia synthesis route is urgently needed. Electrochemical synthesis of ammonia is a green and mild new method for preparing ammonia, which can directly convert nitrogen or nitrate into ammonia using electricity driven by solar, wind, or water energy, without greenhouse gas and toxic gas emissions. Herein, the basic mechanism of the nitrogen reduction reaction (NRR) to ammonia and nitrate reduction reaction (NO⁻₃ RR) to ammonia were discussed. The representative approaches and major technologies, such as lithium mediated electrolysis and solid oxide electrolysis cell (SOEC) electrolysis for NRR, high activity catalyst and advanced electrochemical device fabrication for NO⁻₃ RR and electrochemical ammonia synthesis were summarized. Based on the above discussion and analysis, the main challenges and development directions for electrochemical ammonia synthesis were further proposed.

As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

Platinum (Pt)-based materials are still the most efficient and practical catalysts to drive the sluggish kinetics of cathodic oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). However, their catalysis and stability performance still need to be further improved in terms of corrosion of both carbon support and Pt catalyst particles as well as Pt loading reduction. Based on the developed synthetic strategies of alloying/nanostructuring Pt particles and modifying/innovating supports in developing conventional Pt-based catalysts, Pt single-atom catalysts (Pt SACs) as the recently burgeoning hot materials with a potential to achieve the maximum utilization of Pt are comprehensively reviewed in this paper. The design thoughts and synthesis of various isolated, alloyed, and nanoparticle-contained Pt SACs are summarized. The single-atomic Pt coordinating with non-metals and alloying with metals as well as the metal-support interactions of Pt single-atoms with carbon/non-carbon supports are emphasized in terms of the ORR activity and stability of the catalysts. To advance further research and development of Pt SACs for viable implementation in PEMFCs, various technical challenges and several potential research directions are outlined.

Bifacial photovoltaics (BPVs) are a promising alternative to conventional monofacial photovoltaics given their ability to exploit solar irradiance from both the front and rear sides of the panel, allowing for a higher amount of energy production per unit area. The BPV industry is still emerging, and there is much work to be done until it is a fully mature technology. There are a limited number of reviews of the BPV technology, and the reviews focus on different aspects of BPV. This review comprises an extensive in-depth look at BPV applications throughout all the current major applications, identifying studies conducted for each of the applications, and their outcomes, focusing on optimization for BPV systems under different applications, comparing levelized cost of electricity, integrating the use of BPV with existing systems such as green roofs, information on irradiance and electrical modeling, as well as providing future scope for research to improve the technology and help the industry.

Lithium-ion batteries (LIBs) are widely used in transportation, energy storage, and other fields. The prediction of the remaining useful life (RUL) of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery. In order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neural network and Gaussian process regression (GPR) is proposed. In the initial phase, the features related to the capacity degradation of LIBs are utilized to train the neural network model, which is used to predict the initial cycle lifetime of 124 LIBs. The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space. The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated, and the shortest distance is considered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model (DEM). In the second phase, GPR uses the DEM as the initial parameter to predict each test set’s early RUL (ERUL). By testing four batteries under different working conditions, the RMSE of all capacity estimation is less than 1.2%, and the accuracy percentage (AP) of remaining life prediction is more than 98%. Experiments show that the method does not need human intervention and has high prediction accuracy.