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Summary: Acidic and basic sites of catalysts are essential for CO2 capture and activation. In this work, Zr, N-ZnO/ZnAl-LDH-IL composites in ionic liquid and methanol systems were fabricated, and applied to catalyze the synthesis of ethylene carbonate (EC) from ethylene glycol (EG) and CO2 with about 4.76 mmolEC gCat.−1 h−1. The composites showed more strong basic sites due to the effective induction of reactive groups on the catalyst surface by Zr doping, resulting in an increase of pyridinic-N groups from 5.48% to 22.25%. More C atoms adjacent to pyridinic-N as strong basic sites was conducive to the activation of CO2 and EG. In addition, the possible catalytic pathway and mechanism of the composites for synthesizing EC as well as the doping of La, Fe, Ce, and Cu were also investigated, which provides an effective strategy for regulating the acid-base centers on the catalyst surface through ionic liquids and methanol solvents.

Power loads usually have multiple cycles, and the traditional forecasting methods only use the historical loads under various types of cycles together as input features to construct the forecasting model, ignoring the deep features under multiple cycles. The simultaneous inputs will lead to the cycles overlapping and influencing each other, which will be difficult to deal with when building the model. Therefore, this paper proposes a GRO-Bagging day-ahead power curve forecasting model based on multi-cycle feature extraction. First, the multi-periodicity of the power load is analyzed. The one-dimensional time series is converted to two-dimensional according to the multiple cycles of power load. Then, the multi-periodic feature extraction is performed by a multi-size convolutional feature extraction layer with parallel selected based on the data characteristics and modeling mechanism, and Bootstrap Aggregating (Bagging) method is used to construct different prediction models for the datasets containing different periodic features; finally, Gold Rush Optimization (GRO) algorithm is introduced and improved by using the Tent chaotic mapping and elite strategy, and the improved optimization algorithm is used for the weight optimization of the model, the error feedback mechanism is introduced to achieve the weight dynamic Updating. To prove the superiority of the proposed model, a series of comparison experiments and ablation experiments are carried out on real datasets, and the results show that the proposed method has higher prediction accuracy, and prove that the multi-period feature extraction and dynamic weighting methods have a positive and active effect on load prediction.

This paper proposes a novel approach that unifies a demand response (DR) with a master plan of the model predictive control method focusing on scheduling maintenance and replacement for suboptimal equipment in real-time solar power plants. By leveraging DR mechanisms and MPC algorithms, our proposed framework starts with understanding the correlation between solar module temperature, surrounding temperature, and irradiation—essential for predicting and optimizing the performance of solar energy installations. It extends to evaluate the DC to AC conversion ratio, which is an indicator of the efficiency of the inverters. This integration enables proactive decisions for repair, maintenance, or replacement of equipment. Through exploratory data analysis using Python, we establish the efficiency and benefits of our anticipated approach in identifying the relationship between the factors that affect solar power generation.

In order to increase the economic benefits of offshore wind farms, reducing the wake effect on the power and fatigue load of wind farms is one of the fundamental ways. In this study, the optimal method of combing yaw offset and pitch control (CYOPC) is put forward for wind farm power and fatigue loads below rated wind speed. To quantify potential power increase and fatigue loads decrease, three turbines in China’s Binhai offshore wind farm are investigated using FAST.Farm. The simulation studies are carried out in region 2 of turbine operation and compared with under yaw control only. Results indicate that the CYOPC method has the potential to increase power and reduce fatigue load.

Abstract It is a critical part of increasing renewable energy accommodation by using virtual power plant (VPP) to attain carbon neutrality. However, VPP applications primarily consider VPP's participation in power market transactions as a whole and rarely consider the transaction interaction between internal resources. VPP's internal resources complement each other organically, and blockchain technology for distributed transactions has incorporated points. In this article, the authors undertake a study and examine the P2P scenario of VPP internal transactions in light of the issues experienced by DERs transactions. Next, the authors analyse the consensus mechanism, smart contract, inter‐blockchain technology, and game theory, and how to apply them in the P2P scenarios of VPP internal transactions. Further, the authors design the function of the DER transaction system, which lays the foundation for the realisation of the system in the future. Finally, the authors conclude that the potential of blockchain technology in P2P transactions between internal entities of the VPP is significant and warrants further investigation.

Abstract When participating in demand response, the industrial adjustable load has the advantages of large capacity, high benefit, and easy management compared with commercial and residential loads. However, the stage of the production process is strongly coupled with the power consumption process, especially in the steel industry. Here, a multi‐objective optimization model for the optimal scheduling of hot rolling load considering the actual production operation conditions was proposed, the model was abstracted into a vehicle routing problem (VRP) ,which is a typical combinatorial optimization problem .To minimize the cost of electricity and the risk of delivery order default, this paper considered the jump penalty value between adjacent slabs simultaneously, constructed an integrated scheduling model of hot rolling shop scheduling and power demand response, and designed a multi‐objective production scheduling algorithm based on NSGA‐II to solve the problem. The results showed that the proposed method can realize the reasonable distribution of production load under the constraints of electricity price and production.

The efficiency of on-site consumption of new energy and the economy of dispatching strategy for that in modern microgrids are increasingly concerning, which are closely related to the microgrid control model with source-load uncertainty. To this end, this paper proposes the multiagent hierarchical IQ (λ)-HDQC regulation strategy to realize the source-load-storage-charging collaborative control of the microgrid model with high-permeable new energy. The first layer adopts the IQ (λ) strategy, which avoids the overestimation and underestimation problems of traditional reinforcement learning by the coupled estimation method. The second layer adopts the HDQC allocation strategy, which solves the problem of low utilization of new energy in the proportional allocation method and improves the adaptability of the regulation strategy in the complex stochastic environment. The interaction of the two-layer strategies realizes the source-network-load-storage-charge global dynamic interactive regulation of microgrids. Indicators of energy efficiency are constructed in this paper to measure the simulation results. And the superiority of the proposed strategy is verified through the simulation results of the microgrid system.

The separation of dimethyl carbonate (DMC) and methanol is of great significance in industry. In this study, ionic liquids (ILs) were employed as extractants for the efficient separation of methanol from DMC. Using the COSMO-RS model, the extraction performance of ILs consisting of 22 anions and 15 cations was calculated, and the results showed that the extraction performance of ILs with hydroxylamine as the cation was much better. The extraction mechanism of these functionalized ILs was analyzed by molecular interaction and the σ-profile method. The results showed that the hydrogen bonding energy dominated the interaction force between the IL and methanol, and the molecular interaction between the IL and DMC was mainly Van der Waals force. The molecular interaction changes with the type of anion and cation, which in turn affects the extraction performance of ILs. Five hydroxyl ammonium ILs were screened and synthesized for extraction experiments to verify the reliability of the COSMO-RS model. The results showed that the order of selectivity of ILs predicted by the COSMO-RS model was consistent with the experimental results, and ethanolamine acetate ([MEA][Ac]) had the best extraction performance. After four regeneration and reuse cycles, the extraction performance of [MEA][Ac] was not notably reduced, and it is expected to have industrial applications in the separation of methanol and DMC.

Internet of Energy is promoting the deep integration between power grid and communication technologies, while the production, operation and service mode of the power grid are undergoing great evolutions. The expanded services need to be carried by efficient electric power communication network (EPCN). Aiming at releasing the bottleneck problem of link capacity in EPCN, we propose a pre-cross-connected trail (p-Trail)-based protection algorithm with load balance (TPLB) in this paper. First, we introduce the current statue and developing trend of EPCN and analyze traffic flow characteristics of various electric communication services. Then the protecting principles and implementing steps of the proposed TPLB algorithm are described in detail, in which an efficient load balancing algorithm is designed to groom the traffic flow. Finally, the validity and efficiency of TPLB is verified and analyzed in a real EPCN topology from a city of Jiangsu Province, China. It is demonstrated from the simulation results that compared with the existing algorithms, the proposed TPLB can further decrease the network redundancy and blocking probability, and improve the utilization of wavelength resources.

Energy Internet is a complex nonlinear system. There are many stakeholders in the load trading market, which is usually regarded as a multi-player gaming. Although gaming theory has been introduced to solve Multivariate Load trading problems, different conditions should be considered to accurately optimize the multivariate load trading problem. For example, the selling side needs to reduce the reserve capacity and improve profits, but the consumer side needs to reduce costs and minimize the impact on its own electricity consumption. These contradictory conditions require multiple Nash equilibrium to achieve obviously. To address this issue, a unified architecture of the power system cloud trading is constructed in this paper, which is combined with the multiple load classification of the power system. In addition, according to the power market operation mechanism, a price-guided multivariate load trading game strategy is designed. More importantly, a multivariate load trading optimization method based on LSTM (Long Short-Term Memory) and gaming theory is proposed in this work. LSTM is introduced for real time prediction, which can be combined with the game theory for strategy searching. The global stability and optimal solution theory prove the feasibility of the proposed neural network, and finally the effectiveness of the proposed method is verified by using numerical simulation.

Smart grid construction is an important carrier and an effective way to promote the development of low-carbon economy. Demand response (DR) is commonly regarded as an important core technology in smart grid field, and it reflects the flexible and interactive features of the core business in smart electricity. It is the developing direction of automated demand response (ADR) technology, and its main features are the standardization of information exchange, together with the intelligence of decision-making and the automation of implementations. ADR technology can improve the efficiency of the whole power system and enhance the ability to accept new energy sources. This paper analyzes the role of demand response in improving efficiency and low-carbon energy saving power systems. The automated demand response system architecture is investigated, and the ADR roadmap of commercial/industrial and residential customer is proposed. The key technologies for ADR system are analyzed, including demand response strategy, information exchanging model, measurement and verification techniques, and multi-agent scheduling techniques. To ensure the interoperability between the grid side and the user side, the ADR business in smart grid user interface standards is concluded to support further demand side management project.

Objective: To study whether children with peptic ulcer would have abnormalities in cellular and humoral immune functions, and whether Helicobacter pylori (Hp) infection would affect the immune function of children with peptic ulcer. Methods: This is a retrospective study. The subjects of study were 72 children with diagnosed and cured peptic ulcer (ulcer group), and 50 healthy children with physical examination (control group) at Baoding Hospital, Beijing Children's Hospital Affiliated to Capital Medical University from June 2020 to December 2022. Further detection was conducted on T lymphocyte subsets (CD3+, CD4+, CD8+, and CD4+/CD8+ ratio) and immunoglobulin levels. Results: Of the 72 children with peptic ulcer, 53(73.6%) were positive for Hp (Hp-positive group) and 19 (26.4%) were negative (Hp-negative group). The levels of CD3+, CD4+, and CD4+/CD8+ ratio in the control group were significantly higher than those in the ulcer group, with statistically significant difference (P<0.05); while the level of IgG in the control group was lower than that in the ulcer group, with statistically significant difference (P<0.05). Meanwhile, there were statistically significant differences in that the levels of CD3+, CD4+ and CD8+ were increased in Hp-positive group than those in Hp-negative group before treatment (P<0.05); while CD4+/CD8+ ratio was lower in the former group than that in the latter group, with statistically significant difference (P<0.05). Conclusion: Hp infection can induce the elevation of T lymphocyte subsets. The development of peptic ulcer has an intimate association with the disorder of cellular and humoral immune functions. [ABSTRACT FROM AUTHOR]

With the improvement of digitalization and intelligence in the power system, the safe operation of the power system is facing enormous challenges. The safe use of electricity on the load side is the key to achieving safe and reliable power system operation. The detection party needs amounts of human and material resources when the power network is attacked. In response to the current difficulties of low detection ability and high detection costs, this paper proposes an attack and defence game model that considers the differences between different nodes, ensuring the safety and economy of electricity consumption while reducing energy waste. At first, the structure of smart meters and the attack characteristics of intruders are summarized, and a basic attack and defence game model is constructed. The Nash equilibrium is then solved, and the optimal strategy for the game between the defender and the intruder is given to balance the relation between detection performance and energy consumption. In response to the differences generated by each node, strategies for attackers to launch attacks on different nodes and the setting of optimal thresholds for other nodes in the defence system are explored. Finally, case studies verify that the proposed model could reduce the cost of intruder detection while ensuring a specific detection rate. [ABSTRACT FROM AUTHOR]