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This paper proposes the development and validation of an electro-thermal model of Lithium-Ion cell, which is used to recreate the cell's temperature and voltage evolution given variable operating conditions. The obtained data is used to train and validate a Recurrent Neural Network (RNN) in predicting future cell combustion. Extensive case studies show that the proposed RNN adequately predicts possible cell combustion, so it can be applied to real life applications. [ABSTRACT FROM AUTHOR]

Load forecasting is one of the most important and studied topics in modern power systems. Most of the existing research on day-ahead load forecasting try to build a good model to improve the forecasting accuracy. The forecasted load is then used as the input to generation scheduling with the ultimate goal of minimizing the cost of generation schedules. However, existing day-ahead load forecasting models do not consider this ultimate goal at the training/forecasting stage. This paper proposes a task-based day-ahead load forecasting model labeled as LfEdNet that combines two individual layers in one model, including a load forecasting layer based on deep neural network (Lf layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed layer by updating the parameters of the Lf layer. Sequential quadratic programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test results demonstrate that the forecasted results produced by LfEdNet can lead to lower cost of day-ahead SED at the expense of slight reduction in forecasting accuracy. [ABSTRACT FROM AUTHOR]

Chitosan and its derivatives are ideal nasal vaccine adjuvant to deliver antigens to immune cells. Previously, we successfully used a chitosan derivative, O -(2-Hydroxyl) propyl-3-trimethyl ammonium chitosan chloride (O -HTCC), and a β-glucan derivative, curdlan sulfate (CS), to prepare a nanoparticle adjuvant CS/ O -HTCC which could deliver ovalbumin to antigen presenting cells (APCs) through nasal inhalation. In this article, we used SARS-CoV-2 spike receptor binding domain (S-RBD) as the antigen and CS/ O -HTCC nanoparticles as the adjuvant to develop a nasal mucosal protein subunit vaccine, CS/S-RBD/ O -HTCC. The humoral immunity, cell-mediated immunity and mucosal immunity induced by vaccines were evaluated. The results showed that CS/S-RBD/ O -HTCC could induce desirable immunization with single or bivalent antigen through nasal inoculation, giving one booster vaccination with mutated S-RBD (beta) could bring about a broad cross reaction with ancestral and different mutated S-RBD, and vaccination of the BALB/c mice with CS/S-RBD/ O -HTCC containing S-RBD mix antigens (ancestral and omicron) could induce the production of binding and neutralizing antibodies against both of the two antigens. Our results indicate that CS/ O -HTCC is a promising nasal mucosal adjuvant to prepare protein subunit vaccine for both primary and booster immunization, and the adjuvant is suitable for loading more than one antigen for preparing multivalent vaccines. [ABSTRACT FROM AUTHOR]

This paper introduces an advanced Short‐term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on the combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal Component Analysis (PCA) is used to capture the features of historical loads in low‐dimensional space compared to the original high‐dimensional load space where features are barely possible to depict. Bayesian Ridge Regression (BRR) is utilized to decide the parameters of the prediction model from a statistics perspective. Tests based on modified PJM load data demonstrate the effectiveness of the proposed STNLF method compared to the state‐of‐the‐art General Regression Neural Network (GRNN) method. Moreover, the reliability of the day‐ahead Unit Commitment (UC) solution is shown to have been improved, based on the forecasted load data using the proposed STNLF method. [ABSTRACT FROM AUTHOR]

Even though renewable energy development has gained momentum in China, thermal power generation still accounts for approximately 70% of the county's total power generation serving as the major source of carbon dioxide (CO 2) emissions in China. Facing the challenges of meeting 2030 peak target of CO 2 emission and realizing the coordinated development of thermal power generation in Beijing-Tianjin-Hebei region, this paper applies generalized Divisia Index Method (GDIM) to decompose the dynamics in the relevant CO 2 emission. The effects of five factors including electricity demand, energy consumption, technology, energy efficiency and energy-mix are considered. The decomposition suggests that electricity demand is the primary factor driving the CO 2 emission up, whereas technology effect decreases CO 2 emission the most. Given the significant roles of technology, energy-mix and energy efficiency in CO 2 emissions reduction, seven scenarios are designed to identify the optimal coordinated development pathway for thermal power generation in Beijing-Tianjin-Hebei region. Through upgrading energy structure and/or enhancing energy efficiency, the thermal power generation in Beijing-Tianjin-Hebei region can achieve coordinated development and realize the 2030 peak target under four scenarios. The detailed development pathways for CO 2 emissions and specific policy implications for Beijing, Tianjin and Hebei are provided to further govern CO 2 emissions and maintain sustainable development. • Proposed a five-factor Generalized Divisia Index Model (GDIM) for CO 2 emission analysis. • Analyzed the CO 2 from thermal power generation in China's Beijing-Tianjin-Hebei region. • Electricity demand and technology contributed the most to the positive and negative changes of CO 2 , respectively. • Designed seven thermal generation development scenarios to study the CO 2 change until 2030. • The 2030 CO 2 peak target can be achieved under four scenarios. [ABSTRACT FROM AUTHOR]

An abstract-map-based multi-layer optimization strategy is proposed to achieve an integrated planning of public fast-charging stations (PFCSs) to charge light-duty battery electric vehicles (BEVs) in a given geographical region, aiming to improve application of BEVs and finally enhance social welfare in a long term by optimally locating PFCSs and assigning their installed capacities to maximize the possibility of effectively charging BEVs, minimize PFCSs' infrastructure cost and mitigate negative impacts on both the transportation system and the power system. In the first layer of the proposed multi-layer strategy, the conditions of the transportation system are considered, while in the second layer, the conditions of the electric power system are taken into consideration. Finally in the third layer, an integrated planning of PFCSs is achieved by combining the consideration of both of the first two layers. On each layer from the first to the last layer, all the analysis, methodology, mathematical modeling, and case study are based on an abstract map which is rooted in the actual map of a representative geographical region. After the optimal results based on the abstract map are obtained, they are mapped back to the actual map of the representative geographical region. The results from the same case but considering different conditions, studied on each layer, are compared to prove the effectiveness and advantage of the integrated planning strategy of PFCSs. [ABSTRACT FROM AUTHOR]

Building a power system with a high penetration of renewable energy is the prevailing developmental trajectory of most countries. However, the current electricity spot market mechanism is predominantly tailored for conventional generators (CGs, e.g., thermal units), which ignores the notable differences between renewable energy sources (RESs) and CGs in terms of generation cost and adjustment capability. Consequently, RESs benefit from priority in market-clearing without assuming regulation responsibility during the bidding process alongside CGs, thereby impacting the fairness of the spot market. In this paper, we propose the concept of RES generation directrix (RGD) to quantify the regulation responsibility that RESs need to bear when participating in the spot market. Subsequently, a regulation energy market is introduced in the spot market to reshape the power generation curve of RESs. Finally, the validity of the proposed market mechanism is verified across different test systems. Numerous experiments indicate the proposed RGD-based market mechanism provides a more level playing field for RESs and CGs, helps form an independent offer price for RESs, thereby relieving the "missing money" problem and prolonging the effectiveness and sustainability of electricity spot markets. • A novel market mechanism for renewable-rich energy systems is proposed. • The regulation energy demand for RESs is quantified. • A fairer playing field for all energy resources is provided. • The impact of regulation energy costs on altering the zero marginal cost of RESs is uncovered. • Positive market effects on negative clearing-prices and relieving missing money problems are analyzed. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) inspection has gradually replaced manual inspection of transmission tower, which produces many images. While it is laborious and time-consuming to manually analyze these images, there are also challenges in automatically detecting small-size defective components such as bolts in transmission tower images, due to problems including complex background, small size, and many similar objects of bolts. In this paper, by virtue of multi-scale features and context information, we propose a deep neural network named Camp-Net (Context Information and Multi-Scale Pyramid Network) to identify bolts defect in transmission tower images. First, multi-scale feature fusion combines deep features and shallow features in convolutional networks to detect small-size bolts. Second, context information fusion puts the information around bolts into the detection network to remove the disturbance of complex background and similar objects. An image dataset containing defective bolts and normal bolts is constructed for model training and testing. Experimental results show that bolts with loose pins and bolts without pins among fittings in transmission tower can be accurately identified with the proposed model. The Average Precision (AP) of defective bolts detection of this model can be 11.4% higher than that of the commonly used high performance model, Faster R-CNN. [ABSTRACT FROM AUTHOR]

This paper proposes an aggregation method for proliferated small-size data networks to apply data networks’ spatial load regulation potentials for demand response (DR). First, each data network is modeled as a virtual power network, where the Internet data center load is modeled as a set of linear constraints. Second, an aggregated virtual power network (AVPN) is proposed to demonstrate the aggregation method potentials in power systems and to emulate DR applications for multiple virtual power networks in wholesale markets. The coupling of AVPN and power network would develop a linear load model for an aggregated Internet data center. Furthermore, the supply curves representing AVPN DR are formulated to capture heterogeneous regulation costs of virtual power networks, which guarantees limited welfare losses in AVPN DR compared with the individual DR of virtual power networks. Last, an AVPN DR implementation mechanism is deduced to reveal the potentials of the aggregation method in power system applications. Simulation results verify the efficiency of the proposed aggregation method for spatially-coupled DR resources, where, the computing time of the AVPN-based OPF without welfare loss is reduced by 99.94% when there are 3×104 data networks. The proposed aggregation strategy implies that the proliferation of small-size data networks will offer a reasonable DR for enhancing the power system operation in wholesale electricity markets. [ABSTRACT FROM AUTHOR]

Vegetation encroaching on overhead power lines can cause short circuit faults and pose a major threat to the security and stability of power grids. Therefore, establishing an effective visual detection algorithm to oversee potential circuit failures of the power lines is critical to the ongoing inspection of vegetation encroachment. This paper establishes a deep learning-based detection framework that utilizes the images obtained from vision sensors mounted on power transmission towers. The proposed detection framework includes three cascaded modules: (1) detection of vegetation regions based on the Faster Region Convolution Neural Network (Faster R-CNN), (2) detection of power lines based on the Hough transform, and (3) detection of vegetation encroachment based on an advanced stereovision (SV) algorithm. In particular, the proposed SV algorithm converts the detected two-dimensional (2D) image data of the vegetation and power lines to three-dimensional (3D) height and location results in order to obtain precise geographical locations. Case studies using field captured images provided by a Transmission System Operator (TSO) demonstrate the effectiveness of the proposed framework in detecting vegetation failures, thus improving overall reliability and reducing economic loss. [ABSTRACT FROM AUTHOR]

Fragility modeling and real-time risk assessment can be widely applied to evaluate and enhances the resilience of the power system to High-Impact and Low Probability events. In previous studies, fragility modeling generally targets extreme weather conditions other than thunderstorm. This paper proposes a fragility model to describe the relationship between the duration of a thunderstorm and the probability of lightning related trip-out. The duration of thunderstorms, which can usually be forecasted from the meteorological department, together with the fragility function expression can help a power company to predict the possibility of lightning related trip-out. Furthermore, this paper proposes a real-time risk assessment model that can dynamically adjust the risk value based on the update of the location, peak current, and subsequent stroke of real-time thunderstorm. A case study conducted on the lightning related trip-out data in Southwest China demonstrates that the average risk of transmission line trip-out in high risk group is about ten times that in low risk group. It clearly demonstrates that real-time risk assessment can efficiently distinguish the trip-out risks of different real-time thunderstorms. [ABSTRACT FROM AUTHOR]

The International Energy Agency (IEA) pointed out that thermal electricity generation was the largest contributor to the CO 2 emissions in 2018, particularly in China. Inequality and spatial decomposition analyses have been extensively applied to monitor CO 2 emissions and climate change. However, there have been no studies focusing on the inequality of CO 2 emissions or aggregate carbon intensity (ACI) in the electricity sector. Furthermore, inequality analysis and spatial analysis have not been combined to analyze the ACI in the electricity sector. This study adopts the Theil index to analyze the intraregional and interregional inequality of provincial ACI in China's thermal electricity sector. In addition, the nested spatial decomposition approach is proposed to isolate the factors behind variation in the ACI at the regional and provincial levels. The ACI of most provincial thermal electricity generation grids declined substantially during 2000–2016. By 2016, half of the provinces had achieved the 2020 ACI reduction target of 865 gCO 2 /kWh ahead of schedule. Decomposition based on the Theil index demonstrated that intraregional inequality was the major factor causing the overall inequality of ACI, especially within the North and South regions. The spatial decomposition indicates that energy intensity is the most significant factor behind the decrease in ACI followed by energy structure. The emission coefficient exerted limited impact across all the six regions. Besides, Northwest rose in the rankings of based on the energy intensity, energy structure, and ACI during 2000–2016, whereas North and Northeast saw the opposite pattern. Policy implications based on empirical research are provided to further govern the ACI and curb CO 2 emissions. • Analyzed the aggregate carbon intensity (ACI) in thermal electricity generation. • Adopted Theil index combined with Kaya identity to study ACI inequality. • Proposed a nested spatial decomposition model to analyze ACI variances. • Intraregional inequality was the primary factor causing overall ACI inequality. • Energy intensity contributed the most to ACI variances. [ABSTRACT FROM AUTHOR]

Large-scale demand response (DR) is a promising solution to mitigate the problem of renewable energy (RE) curtailment with the rising proliferation of RE in both the transmission system (TS) and distribution system (DS). However, the current DR schemes face significant limitations when applied at a large scale, and the lack of consideration of TS and DS networks stymies the potential of large-scale DR in facilitating RE accommodation. To address these gaps, this paper proposes a novel hierarchical DR scheme based on locational customer directrix load (LCDL) that takes into account both TS and DS, involving interactions among tri-layer entities: the transmission system operator, distribution system operators, and customers. Firstly, the concept of LCDLs is proposed to characterize the desired load profiles at various locations, considering the constraints of both TS and DS networks. Subsequently, transmission-level and distribution-level LCDLs are formulated respectively and leveraged to induce the load reshaping of flexible resources at pertinent locations, thereby unlocking the deliverable flexibilities over TS and DS. Furthermore, the collaborative interaction among the three layers is depicted by a two-loop Stackelberg game, and a distributed algorithm is presented to achieve an equilibrium solution without compromising the privacy of the respective entities. Case studies testify that the proposed DR scheme significantly enhances the capacity to accommodate RE in both TS and DS, maintains economic balance in the trading process of DR services, and benefits all involved entities. Conducted on a practical city power grid with a substantial share of RE and DR customers, the simulation test validates the scalability of the proposed DR scheme and results show that with a 10% increment of DR customers in one DS, the RE curtailment rate could be reduced by 3% and 15% for TS and DS, respectively. • Proposing a novel LCDL-based demand response mechanism for large-scale deployment. • Characterizing desired load profiles at various locations leveraging LCDLs. • Unlocking customer deliverable flexibilities under network constraints with the guidance of LCDLs. • Enhancing the accommodation capacity of renewable energy through transmission and distribution coordination. • Achieving win-win situations for all involved entities based on a two-loop Stackelberg game. [ABSTRACT FROM AUTHOR]

Unified Power Flow Controller (UPFC) is recognized as the most powerful flexible AC transmission systems (FACTS) device for power system operation. This paper addresses how UPFC explores the transmission flexibility and facilitates the integration of uncertain and volatile wind power generation. To this end, a comprehensive unit commitment (UC) model with UPFC and uncertain wind power generation is proposed. Then, some metrics are introduced to evaluate the impacts of UPFC on the reliability, security and economy of power system operation. Further, different dispatch strategies of UPFC are compared to provide helpful guidances on making full use of UPFC to hedge against uncertainties. In addition, facing the challenging mixed-integer non-linear non-convex problems, approximate models are proposed to provide a starting point to solve the problems efficiently. All these models are easy to adapt to other types of FACTS devices. Illustrative numerical results are provided. [ABSTRACT FROM AUTHOR]

• A robust event detection method with adaptive parameter adjustment is proposed for accurate event detection. • The robust event detection method can be used in unknown households or datasets with blind parameter setting. • The robust event detection method can provide useful information to extract transient and steady-state features for load disaggregation. Nonintrusive load monitoring (NILM) can facilate the transition to energy-efficient and low-carbon buildings. Event detection is the first and most critical step in event-based NILM and can improve the efficiency and performance of NILM by decreasing inference times to the number of events with transient features extracted from events. However, existing event detection methods with fixed parameters may fail to achieve high accuracy in the case of uncertain and complicated residential load changes such as high fluctuation, long transition, and near simultaneity in both power and time dimensions. Besides, it is difficult to transfer the fixed parameter to new households with different load profiles. Furthermore, most of these methods prove that they are able to detect events but not able to extract features for load disaggregation. This paper proposes a robust event detection method with adaptive parameters to deal with such issues. Specifically, a window with adaptive margins, multi-window screening, and adaptive threshold method is proposed to detect events in aggregated load data with high sampling rate (>1 Hz). The proposed method captures the transitions by adaptively tuning parameters including window width, margin width, and thresholds. It can also achieve good performance with blind parameter setting so that it is suitable for unknown households or datasets. Furthermore, it captures complete transitions that are indispensable for transient feature extraction. Case studies on a 20 Hz dataset, the 50 Hz LIFTED dataset, and the 60 Hz BLUED dataset show that the proposed method can robustly outperform other state-of-the-art event detection methods. The robust performance of the proposed method is also verified by a cross validation of parameters among different datasets. Lastly, the proposed event detection method is demonstrated to have the merits of improving the performance of load disaggregation. [ABSTRACT FROM AUTHOR]

• Novel market mechanism compatible with renewable energy and energy storage. • Discovering substitute energy price to trade vector-level energy as substitutes. • Establishing and quantifying supply–demand relationship of regulation energy. • Providing a level playing field for all energy and regulation resources. • Positive market effects on global social welfare and relieving duck-curve problem. Incompatibility of current electricity market mechanisms based on locational marginal price (LMP) become prominent in power systems with increasing renewable energy (RE) and generalized energy storage (GES), resulting in soaring electricity prices, high costs of balancing RE, etc. To fundamentally solve this problem, we propose a novel substitute energy price (SEP) market mechanism, compatible with objective market value, new supply–demand relationship and nature of continuous power balance of RE power systems. SEP, reflecting market value of per-unit substitute energy, is discovered for the first time, and thus energy curves can be traded as substitutes at vector level. Based on designed regulation responsibility mechanism, any entity, especially RE generator, can transform its energy curve with regulation demands into substitute by indirect payment. To supply these regulation demands generated by REs and loads, regulation energy is proposed as independent tradable commodities with quantitative market value to adapt to GESs and dispatchable generators. Numerous experiments indicate SEP mechanism overcomes major deficiencies of existing LMP mechanism, provides a level playing field for all energy and regulation resources, promotes construction of GESs and helps improve global social welfare and relieve duck-curve problem. This work will be instrumental in the transformation of current power systems to carbon–neutral RE power systems. [ABSTRACT FROM AUTHOR]

The rapid increase of power consumption calls for efficient and effective energy usage and conservation strategies in buildings. One of the requirements of achieving such a goal is load monitoring of residential appliances. Among the available load monitoring frameworks, nonintrusive load monitoring (NILM) which is used to estimate the appliance-level power usage from the aggregated signals from smart meters, has the potential to be widely deployed. This paper presents an up-to-date review of NILM methods and the challenges existing in each step of NILM. Then this paper reviews two state-of-the-art machine learning based NILM methods including Hidden Markov Model and Deep Learning techniques. Finally, this paper discusses areas for future research and development of NILM in real-world applications, where machine learning approaches can play a more significant and even decisive role. [ABSTRACT FROM AUTHOR]

• A cross-grid DR framework for power grid operation supporting from gas DR is created. • A model of maximum quantity of DR gas usage is constructed. • A machine-learning based cost-generation model of DR gas power generation is initiated. • A numerical study is implemented for model verification. Energy integration and energy internet enable cooperation of various energy network. Thus demand response (DR) can be extended from a pure power system issue to a cross-grid format. This paper initiates a coordinating market and operation framework for gas demand response to support power system supply–demand balancing. In the proposed framework, gas turbines help to transfer gas DR into power generation. The gas aggregators collect the flexible gas resources and make offers to power grid. The cross-grid demand response model of gas flexible loads is established to obtain the equivalent 'Cost-Generation-Capacity' for power market competition. The awarded generation capacity of gas DR aggregators will also be optimally distributed to all gas DR suppliers. A numerical study is implemented to demonstrate the feasibility of the proposed framework and model. [ABSTRACT FROM AUTHOR]

Economy, robustness and computational efficiency are of paramount metrics for an operation strategy of an integrated energy system (IES). To achieve the trade-off of the three metrics, a multi-layer framework is extensively exploited in existing operation strategies. This work, however, proposes a single-layer multi-timescale framework which can coordinate different operation performances associated with various timescales simultaneously. Based on the framework, an improved stochastic model predictive control (SMPC) operation strategy is further developed by embedding the proposed framework into its prediction horizon. To solve the multi-timescale optimization of the improved SMPC, the constraints and cost function are presented in the multi-timescale form, and the supplied and demands are forecast by the least square support vector machine. A simulator of an IES is thereafter constructed to mimic real system and used to evaluate the performance of the proposed strategy by operation cost, accumulative error and computation time with respect to economy, robustness and computational efficiency, respectively. Finally, the improved SMPC strategy is compared with a traditional single-layer and a hierarchical strategy by a case study. The results show that the improved strategy has the best tradeoff performance aforementioned. The multi-timescale framework can be also integrated into other operation strategies. [Display omitted] • A single-layer multi-timescale framework is proposed. • An improved stochastic model predictive control operation strategy is developed. • Renewables and demands are forecast by least square support vector machine. • Comparisons of the improved strategy with a traditional single-layer and a hierarchical strategy are performed. [ABSTRACT FROM AUTHOR]

• A compact yet efficient decentralized economic dispatch (ED) method is proposed. • The iterative process is theoretically proved to have a guaranteed convergence. • Fault tolerance and privacy awareness are achieved based on the bottom-up style. • The adaptive method is strongly robust to the scale of participants and parameters randomness. This paper introduces a decentralized economic dispatch method and an architecture suitable for the virtual power plant (VPP) aggregating massive distributed energy resources (DERs). The convergence condition is given for quadratic cost functions, and is extended to the case of general increasing function of incremental cost (IC). Further analysis shows that the step of this method is adaptive, which is generated from the bottom up according to the responsiveness of each DER unit (DERU). Combined with the decentralized architecture based on message queue (MQ), the algorithm design considers the hosting mechanism of the coordinator failure, which not only improves the efficiency of calculation and communication without losing privacy-protection, but also makes it more fault-tolerant. The correctness and effectiveness of the method are verified in the case studies. The iterative process can respond and converge quickly when DER units reach capacity limits or devices fail/join. Due to the adaptability of the step, the method has strong robustness to the quantity and parameters randomness of underlying units. Therefore, it can be applied to the VPP with a massive number of DERs in order to get consensus solution by rapid economic dispatch. [ABSTRACT FROM AUTHOR]

• A novel bottom-up approach is proposed for the exploitation of flexibility. • Autonomous decentralized system theory is introduced to achieve online aggregation. • Stimulus-response strategy is proposed for system automatic closed loop control. • Dynamic, automatic and adaptive response rules are established for participants. To adequately utilize flexible resources on demand side, Virtual Power Plant (VPP) is an effective solution through the aggregation and application of distributed energy resources (DER). While centralized control approaches are easy to achieve global optimum for the scheduling of every DER, they have limitations when dealing with massive number of complex and heterogeneous DERs with time-varying states. Existing decentralized control approaches are mainly based on the assumption that all DERs are completely rational, which is quite far from the reality. In this paper, using a bottom-up approach, we propose a stimulus–response control strategy to realize exploitation of flexibility by VPP. In such a strategy, DERs are dynamically aggregated through autonomous decentralized system, and interact with each other via subscription and publication of topics, regardless of the source and recipient of the messages, thus removing the direct coupling relationship between VPP Operator and DERs. Furthermore, each DER makes an independent decision through edge computing at an agent that has a general End-to-End structure and is driven by the stimulus message received from VPP Operator. We develop a simple yet efficient double deep q-network (DDQN) algorithm to optimize the state sequence of DER agents. A simulation study is conducted with over 1000 DERs including photovoltaics, electric vehicles and air conditioners. Results indicate that the proposed approach can dynamically aggregate DERs and exploit their flexibility with each DER agent dynamically adapting to the change of stimulus signals, thus achieving dynamic, automatic and adaptive exploitation of flexibility by VPP. [ABSTRACT FROM AUTHOR]

• Elaborating realization method of decentralized transfer of contingency reserve. • Instantaneous conservative response: millisecond-level and error-tolerance. • Adaptive latent response: accurate frequency restoration by real-time correction. • Communication-free active response considering load priority and magnitude. • Conservative response capacity helps avoid adverse frequency by minimum loads. The increasing penetration of renewable energy comes with decreasing system inertia and much faster frequency drop when contingency of large power loss occurs, which seriously threatens the security of power system operation. Meanwhile, the conventional contingency reserves will be of serious shortage and unable to satisfy security requirements in the future. To solve these problems, the concept of decentralized transfer of contingency reserve (DTCR) has been recently proposed to partially transfer the centralized contingency reserve from the supply side to the demand side, attempting to realize smart decentralized reserves with higher security and lower cost. To continue this work, this paper further elaborates the methods of implementing DTCR. Firstly, the framework of the DTCR system is formulated. Then, a refined load frequency control for contingencies is developed with millisecond-level speed and appliance-level control accuracy. The proposed three-stage control strategy is composed of instantaneous conservative response (ICR), adaptive latent response (ALR), and optimal dynamic control (ODC). As the basis of all responses, an estimation method of the range of power imbalance and frequency nadir is given, considering communication mechanism and parameter errors. In the ICR, a communication-free active response scheme is proposed considering load priority and magnitude to achieve rapid nadir control, and the setting formula of conservative response capacity (CRC) to avoid unacceptable low frequency by reliable minimum load resources is presented for the first time, which can mitigate the adverse impact caused by mis-shedding and enhance the error-tolerance. In the ALR, an online adaptive correction method is presented for key parameters to achieve accurate frequency restoration and decrease the impact of uncertainties in the sliding time window. Finally, the effectiveness of the proposed DTCR realization method is demonstrated through the simulation on a modified small-inertia IEEE 14-bus system with wind power penetration. Further tests indicate the ICR and ALR possess high security performance in the frequency control for handling contingencies. [ABSTRACT FROM AUTHOR]

Objective: Mesalazine is a widely used medication for treating mild to moderate inflammatory bowel disease (IBD). First identified as a potential cause of acute pancreatitis (AP) in 1989, the link between mesalazine and AP has primarily been established through case reports and a limited number of retrospective studies. This study aims to explore the characteristics of mesalazine-induced AP., Methods: The databases of CNKI, Wanfang Data, VIP, PubMed and Web of Science were searched (up to March, 2024), and the case reports of mesalazine-related AP in IBD patients were collected and descriptively analyzed., Results: Thirty-four reports were included, describing 42 patients (22 males, 16 females, 4 unspecified) with mesalazine-related AP. The onset of pancreatitis occurred a median of 14 days (range 1-730 days) after starting mesalazine. Common symptoms included abdominal pain (100%), vomiting (38.1%), fever (21.4%), and nausea (21.4%). Most patients had elevated serum amylase and lipase levels, with some showing raised C-reactive protein and erythrocyte sedimentation rate. Imaging tests, such as computed tomography and B-scan ultrasonography, revealed edematous infiltration and inflammation. Discontinuation of mesalazine led to symptom resolution in all patients, with 93.3% improving within a week. Alternative treatments or switching to other forms of 5-aminosalicylic acid may be considered for ongoing management. Rechallenge with mesalazine led to recurrence of AP in 21 cases, with a shorter median time to symptom onset., Conclusion: Mesalazine-induced AP is a rare but significant adverse reaction, not related to drug dosage, and can occur at any point during treatment, typically within two weeks. The reaction can recur upon rechallenge. Discontinuation of mesalazine and symptomatic treatment typically resolves the condition., Competing Interests: The authors declare that they have no competing interests in this work., (© 2025 Pan et al.)

Objectives: Recently, more and more evidences suggest that β-glucans can induce trained immunity and non-specific protections against pathogens. However, most of the reports evaluated the immunological activities of β-glucans through injection route but no nasal inhalation. In this study, the effects of curdlan sulfate-based nanoparticles, CS/O-HTCC on trained immunity through intranasal administration were evaluated., Methods: Macrophages were treated with CS/O-HTCC and the metabolisms of the macrophages were detected. Mice were intranasal administered with CS/O-HTCC for 3 times with a 14 days interval, then the antitumor or infection prevention effects were assessed., Results: In vitro, CS/O-HTCC enhanced the macrophage metabolism significantly through upregulating glycolysis (26.1 ± 4.3 mpH/min) and oxidative phosphorylation (36.0 ± 9.0 pmol/min) compared with that of negative group (7.5 ± 2.3 mpH/min and 19.5 ± 4.9 pmol/min). In vivo, CS/O-HTCC inhibited lung metastasis of B16F10 tumor cells and improved the survival time (26.5 days) of the nmice compared with negative group (19.5 days). Moreover, CS/O-HTCC prevented the lung infections by Escherichia coli or Streptococcus pneumoniae (less bacterial residual) and reduced lung damages., Conclusions: CS/O-HTCC can induce trained immunity through enhancing the metabolism of macrophages and enhance the non-specific protection against pathogens through intranasal immunization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Urinary incontinence (UI) is a common condition in female. Oxidative stress and inflammation levels play important roles in UI progression. Mediterranean diet (MD) as a healthy anti-inflammatory dietary pattern has been reported to be associated with several inflammatory diseases. This study aimed to assess the association between the adherence to Mediterranean diet (aMED) and female UI., Methods: Data of study women aged ≥18 years old and diagnosed as stress UI and urgency UI were extracted from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Dietary intake information was obtained by 24-h dietary recall interview. Covariates included sociodemographic information, physical examination, and history of diseases and medication were extracted from the database. The weighted univariable and multivariate logistic regression models were used to assess the association between aMED and different types of UI, with odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup analysis were further evaluated this association based on different age, body mass index (BMI), neutrophil to lymphocyte ratio (NLR), depression and smoking., Results: Totally, 13,291 women were included, of whom 5,921 (44.55%) had stress UI, 4276 (32.17%) had urgency UI and 2570 (19.34%) had mixed UI. After adjusted all covariates, high aMED score was associated with the lower odds of urgency (OR = 0.86, 95%CI: 0.75-0.98) and mixed UI (OR = 0.84, 95%CI: 0.70-0.99), especially in female, aged 45-60 years old, NLR ≥1.68 and had smoking history. No relationship was found between the aMED and stress UI (P >0.05)., Conclusion: Greater aMED was connected with the low odds of urgency UI and mixed UI among female. Adherence to an anti-inflammatory diet in daily life are a promising intervention to be further explored in female UI., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: As synthesis technology advances, novel and efficient derivatives of tetracyclines are found. Three new antibiotics were approved within the past 18 years, and represent a new era in the use of tetracyclines. To gain further insight into adverse events linked to tetracyclines and better protect pediatric patients, ongoing monitoring of safety data is crucial., Methods: The FAERS data from the first quarter of 2004 to the third quarter of 2023 in the AERSMine were extracted to conduct disproportionality analysis. The association between five tetracyclines and adverse events was evaluated using reporting odds ratio, and their risk factors were explored by multivariate logistic regression analysis., Results: Our study showed that thyroid gland disorders had the strongest signal in children. Patients aged 12-18 and treatment with minocycline are risk factors for thyroid adverse events (12-18: OR = 10.727 [7.113-16.177], p  < 0.0001; minocycline: OR = 17.025 [10.475-27.678], p < 0.0001). Second-generation tetracycline and third-generation tetracycline ADR patterns differed. Blood fibrinogen decreased and hypofibrinogenaemia was primarily reported with tigecycline and eravacycline., Conclusion: This study provided basic evidence for further research on tetracyclines-related adverse events. However, the safety of third-generation tetracycline in children requires additional validation through a large-scale prospective study.

Research into vinyl-linked covalent organic frameworks (COFs) has grown significantly in recent years due to various attractive properties. Herein, we design and synthesize two highly crystalline and stable 2,4,6-collidine-derived vinylene-linked 2D COFs. Both COFs can act as efficient photocatalysts to facilitate visible-light-driven aerobic oxidation. The TM-TBT-COF was observed to exhibit superior activity and recyclability owing to its excellent semiconducting properties.

Novozym-435 has been found to be an effective biocatalyst for the kinetic resolution of a series of racemic 2,3-allenols, affording highly optically active (S)-(-)-2,3-allenols and (R)-(+)-2,3-allenyl acetates in high yields and with excellent ee values. The reaction of 3-(n-butyl)-3,4-pentadien-2-ol (1 a) was successfully performed on a 10 g scale to afford the corresponding (S)-(-)-2,3-allenol (1 a) and (R)-(+)-2,3-allenyl acetate (2 a) in synthetically useful amounts and with high ee values. The advantages of this reaction are the ready availability of the starting materials, high stereoselectivities for both (-)-2,3-allenols and (+)-2,3-allenyl acetates, the use of a relatively high substrate concentration, and a lower catalyst loading. The resulting (S)-(-)-2,3-allenol 1 a can be converted into the corresponding chiral 2,5-dihydrofuran and the vinylic epoxide.