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The stochastic economic dispatch (SED) problem of active distribution network (ADN) is computationally intractable for traditional algorithms due to the randomness, nonlinearity, and nonconvexity. To solve this problem, we decompose it into a sequence of tractable subproblems, and then employ the high-order Taylor expansion (HTE)-based value functions to estimate the interaction among these subproblems. Compared with traditional value function approximation (VFA) techniques, the proposed HTE-based VFA technique extends the approximate value function from the low-order form to the arbitrarily high-order form, which facilitates describing the nonlinear characteristics. Furthermore, different from commonly used Monte Carlo-based expectation calculation (EC) techniques, which require to re-execute calculation procedures in numerous scenarios, the proposed HTE-based EC technique leverages the nature of HTE to directly obtain the expectation of value function according to the distributions of random variables. Such that the computational burden can be reduced significantly. Finally, the aforementioned two techniques are combined to form an HTE-based economic dispatch algorithm, and then applied to several ADN systems. The numerical simulations fully demonstrate the effectiveness of the proposed algorithm.

This paper proposes a multiparametric approximate dynamic programming (MADP)-based explicit online operation policy for the robust energy management problem of active distribution network (ADN). First, the online energy management problem is described from the perspective of distributionally robust optimization (DRO). Then, the risk-averse concept is introduced to quantitatively control the conservativeness of DRO. Considering that it is a complex online optimal problem, which is intractable for traditional algorithms, an MADP-based explicit online operation policy is proposed in this paper. Specifically, the approximate dynamic programming (ADP) algorithm is improved based on the multiparametric programming theory, according to which an explicit online operation policy can be further derived. Compared to traditional online decision-making policies, which need to execute an optimization program, the proposed policy is able to directly obtain solutions through an explicit expression. In this way, the calculation efficiency can be enhanced significantly. Cases on several test systems validate the effectiveness of the proposed method.

The coordinated transmission–distribution alternating current optimal power flow (TD-ACOPF) has become crucial to handle problems related to high penetration of renewable energy sources (RESs). However, obtaining all system details and solving TD-ACOPF centrally is sometimes less preferable in practice due to privacy concerns. Intermittent RESs and uncontrollable loads can swiftly change the operating condition of the power grid. Existing decentralized optimization methods can seldom track the optimal solutions of time-varying TD-ACOPFs. Here, we develop a continuous-time dynamic approach based on the primal-dual interior point method to online track the saddle points of the Lagrangian for time-varying ACOPFs from a control system perspective. To improve tracking accuracy, a prediction term denoted by the partial derivative with respect to time is added to the dynamic system. Next, a decentralized implementation for solving the dynamic system is designed based on only a few information exchanges with respect to boundary variables. Moreover, the proposed continuous-time dynamic approach is derived from the first-order optimality condition of the ACOPF model (without approximation) for coping with the nonlinear power flow equation model without the need for real-time measurements. Numerical test results reveal the effectiveness and fast-tracking performance of the proposed algorithm.

To determine the stability of a power system integrated with numerous wind power generators, considering the stochastic fluctuation of wind power output, this paper mainly studies the stability criterion of power systems under stochastic continuous disturbances (SCDs). The dynamic performance of a power system under SCDs is described through stochastic differential equations (SDEs) based on nonlinear state equations. Under the framework of SDEs, a criterion for determining the mean square stability of a power system under SCDs is proposed, and it is proved that the proposed stability criterion is a necessary and sufficient condition for determining the mean square stability. In addition, the bi-level nonlinear semidefinite programming (NLSDP) problem in the stability criterion is transformed into a single-level NLSDP problem by using the improved Karush–Kuhn–Tucker condition of the inner-layer problem, and an extended primal-dual interior point algorithm is proposed to solve the single-level NLSDP problem. In addition, the optimal objective value calculated from the proposed stability criterion is used to quantify the stability degree of the system under SCDs. Finally, case studies on the modified IEEE-39 and 118 bus systems, and an actual provincial power grid demonstrate the correctness and effectiveness of the proposed stability criterion and solution algorithm.

Recent decades have seen the remarkable development of China in medical accessibility and quality index, and the application of a number of new advanced cardiovascular technologies benefits more patients. However, according to the Annual Report on Cardiovascular Health and Diseases in China published in this article, which was organized and summarized by National Center for Cardiovascular Diseases, there is still a huge population living with risk factors of cardiovascular diseases (CVD), and the morbidity and mortality of CVD are increasing. It is estimated that there are around 330 million patients suffering from CVD currently, including 245 million of hypertension, 13 million of stroke, 45.3 million of peripheral artery disease, 11.39 million of coronary heart disease (CHD), 8.9 million of heart failure, 5 million of pulmonary heart disease, 4.87 million of atrial fibrillation, 2.5 million of rheumatic heart disease, and 2 million of congenital heart disease. Tobacco use, diet and nutrition factors, physical activity, overweight and obesity, and psychological factors are what affect cardiovascular health, while hypertension, dyslipidemia, diabetes, chronic kidney disease, metabolic syndrome, and air pollution are the risk factors for CVD. In this article, in addition to risk factors for CVD, we also report the epidemiological trends of CVD, including CHD, cerebrovascular disease, arrhythmias, valvular heart disease, congenital heart disease, cardiomyopathy, heart failure, pulmonary vascular disease and venous thromboembolism, and aortic and peripheral artery diseases, as well as the basic research and medical device development in CVD. In a word, China has entered a new stage of transforming from high-speed development focusing on scale growth to high-quality development emphasizing on strategic and key technological development to curb the trend of increasing incidence and mortality of CVD.

This article proposes a distance-based distributionally robust optimization method to determine the optimal preventive maintenance schedule in hydrothermal power systems, with the aim of minimizing the operation cost in the coming year under the uncertainty of natural inflows. The ambiguity set consisting of a family of distributions close to the reference probability distribution, is used to model the uncertainty, where the reference distribution is generated according to predictive information and historical seasonal prediction error, so it is helpful to implicitly reflect the seasonality of natural inflows. The conservatism of the solution can be adjusted by regulating the scale of the ambiguity set. The column and constraint generation algorithm is employed to decompose the proposed min–max–min problem into a master problem and subproblem, where the maintenance decision is determined in the master problem and the operation decisions are made to identify the worst-case probability distribution in the subproblem. The stochastic dual dynamic programming algorithm is applied to further decompose the multistage subproblem into several smaller stage problems. Case studies were conducted on a modified IEEE 30-bus system and real hydrothermal power systems in China to demonstrate the effectiveness of the proposed method.

The stochastic energy management (SEM) of power systems is computationally intractable due to its randomness, nonconvexity, and nonlinearity. To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does not need to iteratively approach them as existing ADP algorithms, which facilitates reducing the computing time. In addition, an improved generalized polynomial chaos (IgPC) method (i.e., an extension of RSM) is proposed to calculate the expectation of the value function of ADP in the stochastic environment. Compared with the Monte Carlo method, which is commonly used in existing ADP algorithms, IgPC requires fewer sampling scenarios while providing similar results. Simulation results with two modified IEEE test systems and a real 2778-bus system demonstrate the effectiveness of the proposed algorithm in terms of accuracy and computation efficiency.

To solve the problem of uneven distribution of power flow and limited transfer capability for the transmission section in a large-scale AC/DC transmission system, a multi-objective mixed-integer convex programming method for the siting and sizing of unified power flow controllers (UPFCs) in an AC/DC transmission system is proposed. Considering the four objectives of the annual equivalent investment and operation cost, bus voltage deviation, transfer capability of the transmission section, and renewable energy accommodation, a multi-objective optimization model is established. By using the convex relaxation method, the original mixed-integer nonlinear programming model is transformed into a mixed-integer second-order cone (SOC) programming model to improve the computational efficiency. Furthermore, a successive inscribed polygon method is proposed to tighten the relaxation gap for the SOC constraints in the optimization model and improve the calculation accuracy. In addition, a direct and fast method for solving the compromise optimal solution of the multi-objective optimization model is proposed. Finally, the effectiveness of the proposed method is demonstrated by an actual 8733-bus large-scale AC/DC transmission system and the modified IEEE-39 bus AC/DC system.

The large number of connected renewable energy sources has greatly increased the flexibility demand in the system. At the same time, the ac/dc active distribution network (ADN) will also become the main form of the future distribution network. Therefore, it is very meaningful to study how the ac/dc ADN aggregates flexibility and participates in the dispatching of the transmission network as a flexibility resource. To this end, this article aggregates the ac/dc ADN into a virtual generator with ramp and power parameters. In particular, the flexibility in the ac/dc ADN is subdivided into ramp and power flexibility and incorporated into the aggregation model. Then, an energy-based flexibility cost function is constructed for the aggregated distribution network. Therefore, without relying on the electricity market, the distribution network can be used as a flexible resource to participate in the flexible dispatching of the transmission network. Finally, the effectiveness of the proposed method and model is verified by several case studies.

The integration of a large number of distributed energy resources into the distribution system poses operational challenges and creates opportunities to generate profits in the electricity market. In this study, a multileader one-follower model is built to elaborate the Nash–Stackelberg game of the electricity market, where a distribution company participates in a competition with a generation company. The distribution company, as one leader, decides on the bidding price and quantity to maximize its profits in the market by integrating the distributed energy resources of multiple distribution systems after satisfying the local demand, whereas the generation company, as another leader, participates in the competitive market. As the follower, the independent system operator clears electricity markets. Moreover, a tailored algorithm is proposed to obtain the global Nash equilibrium point of the multileader one-follower Nash–Stackelberg game model. First, the proposed game model is transformed into a generalized Nash equilibrium game model; then, the transformed game model is converted to a potential game model for a solution. Finally, case studies on a constructed transmission and distribution joint system and a practical transmission and distribution joint system are performed to demonstrate the effectiveness and scalability of the proposed model and algorithm.

In this paper, a quadratic Taylor expansion-based approximate dynamic programming (QTE-ADP) algorithm is proposed for the decentralized solution of multi-area AC-optimal power flow (MA-ACOPF). Different from traditional ADP algorithms, the proposed algorithm does not need to approximate the value function via a given function structure, but directly obtains the quadratic Taylor expansion (QTE) of value function based on KKT conditions. Moreover, compared with the commonly used linearized value function approximation techniques, the information used to approximate the value function is also extended from first order to second order in the proposed algorithm, which helps to improve the accuracy and efficiency of ADP. When using QTE-ADP to solve the MA-ACOPF problem, only the boundary voltage information needs to be interchanged between adjacent areas, which facilitates preserving the information privacy and decision independence of each area. Numerical simulations on several test systems demonstrate that the proposed algorithm has good performance in terms of accuracy, efficiency, and adaptability.

In active distribution networks (ADNs), mobile energy storage vehicles (MESVs) can not only reduce power losses, shave peak loads, and accommodate renewable energy but also connect to any mobile energy storage station bus for operation, making them more flexible than energy storage stations. In this article, a multiobjective optimal MESV dispatch model is established to minimize the power loss, renewable energy source curtailment, and total operating cost of ADNs. Additionally, a method to directly obtain the compromise optimal solution (COS) of the Pareto optimal solutions (POSs) of a three-objective optimization problem is proposed. Based on the normalized normal constraint (NNC) method for solving the POSs of a three-objective optimization problem, a bilevel optimization model is established to directly obtain the COS. The innerlayer model uses the NNC method to calculate the POS corresponding to a point on the Utopia plane, and the outerlayer model uses the improved Nelder–Mead (INM) method to directly search the POS with the minimum distance to the Utopia point as the COS. Compared with the traditional method, which first solves the POSs of the three-objective optimization problem and then selects the COS, the proposed INM method has higher computational efficiency and obtains a better COS. Finally, case studies on the modified IEEE-33 bus distribution network and an actual 180 bus distribution network demonstrate the effectiveness of the proposed method.

In this paper, an improved dynamic programming (IDP)-based decentralized algorithm is proposed for the AC-optimal power flow (AC-OPF) of radially connected transmission-distribution-microgrid networks (RCTDMNs). Most of the existing decentralized algorithms need to iterate among networks, which may lead to additional computational and information exchange burden. Different from these algorithms, we decompose the original AC-OPF problem into small-scale subproblems based on dynamic programming (DP) theory, and then solve them in a decentralized manner without iteration among networks. In order to improve the computational performance of DP, its value functions are directly obtained by solving Galerkin projection equations (GPEs) instead of traversing the whole state space. Moreover, a parallel technique is proposed to further accelerate the proposed IDP algorithm when dealing with the AC-OPF problem of RCTDMNs. Case studies on various test systems demonstrate the effectiveness of the proposed method.

Probabilistic models are used to describe the uncertainty of injected power in traditional probabilistic energy flow (PEF) calculations. Owing to the difficulty of obtaining the accurate distribution parameters of the probability models, the uncertainty of the distribution parameters needs to be considered in the PEF calculation of a combined cooling, heating, and power campus microgrid (CCHP-CMG). In this article, the parameterized probability box (p-box) model is applied for describing the uncertain distribution parameters of the probability model of injected power, and the interval PEF (IPEF) calculation model of a CCHP-CMG is developed. An affine arithmetic-based interval point estimation method (AIPEM) is put forward to solve the IPEF calculation model. The location intervals of injected powers are calculated from their p-boxes, and the interval energy flow calculation is performed with each location interval to estimate the moment intervals of the output variables. Affine arithmetic is used to reduce the interval expansion in the interval calculation, and to further reduce the interval expansion caused by affine multiplication/division operation, a method of removing the accumulation of extra noise terms is proposed. In addition, based on the Nataf inverse transformation and the p-box model, an AIPEM considering the correlation of injected powers is proposed that considers the uncertain correlation coefficients. A case study on a CCHP-CMG shows that the proposed AIPEM has higher computational accuracy than the existing interval cumulant method, especially when the uncertain fluctuation range of the injected power is large, although it consumes more CPU time.

With lots of distributed renewable generators and storage devices integrated into distribution networks, the coordinate economic dispatch (ED) between transmission and distribution networks is needed. A stochastic ED algorithm based on distributed approximate dynamic programming for integrated transmission and distribution networks considering the uncertain renewable generator outputs is proposed. First, the convex power flow models of transmission and distribution networks are proposed. Then, the reservoir levels of pumped-storage hydro stations and the remaining stored electric energy of battery storage stations are deemed as the storage quantities, while the renewable generator outputs are deemed as the exogenous stochastic variables to establish the stochastic storage model for the stochastic ED problem of integrated transmission and distribution networks. The approximate value functions represented by the sum of the piecewise linear functions of the storage quantities are constructed to decouple the multiperiod ED problem into a series of single-period ED problems. Finally, by using the alternating direction method of multipliers, each single-period ED problem of integrated transmission and distribution networks is solved in a distributed manner among transmission and distribution networks. Test results on the modified T6D7 system and a practical Shenzhen city network demonstrate the correctness and efficiency of the proposed algorithm.

Track irregularity is strictly controlled in low-speed maglev system. Vertical deflection due to vertical temperature difference is one of the important factors in track irregularity, which should be considered in structural design. In the field of low-speed maglev, few researches have been developed directly about the measurement of temperature and corresponding effect. In this paper, the surface temperature and deflection of a steel box girder in the low-speed maglev experimental line in Shanghai have been measured for several months. Measured surface temperature data are decomposed into temperature trend and fluctuating part with wavelet based MRA and the former is highly relevant with local climatic. In the analysis of probability distribution characteristics of the vertical temperature difference, three weighted probability models are compared, and the weighted sum model of one Weibull distribution and one normal distribution is appropriate, based on which the standard values of the temperature difference with 50-year return period are derived. To determine the correlation between the temperature difference and deflection, scatter plots of equivalent linear temperature difference obtained by deflection and measured temperature difference were fitted, and the results shows an approximate linear relationship.

With the spread of the epidemic worldwide, an increasing number of doctors abroad have observed the following atypical symptoms of coronavirus disease 2019 (COVID‐19): olfactory or taste disorders. Therefore, clarifying the incidence and clinical characteristics of olfactory and taste disorders in Chinese COVID‐19 patients is of great significance and urgency. A retrospective study was conducted, which included 229 severe acute respiratory syndrome coronavirus 2 confirmed patients, through face‐to‐face interviews and telephone follow‐up. Following the completion of questionnaires, the patients participating in the study, were categorized according to the degree of olfactory and taste disorders experienced, and the proportion of each clinical type of patient with olfactory and taste disorders and the time when symptoms appeared were recorded. Among the 229 patients, 31 (13.54%) had olfactory dysfunction, and 44 (19.21%) had gustatory dysfunction. For the patients with olfactory dysfunction, 6 (19.35%) developed severe disease and became critically ill. Olfactory dysfunction appeared before the other symptoms in 21.43% of cases. The proportion of females with olfactory and gustatory dysfunction was higher than that of males (P< 0.001). The incidence of olfactory and gustatory dysfunction was much lower than that reported abroad; the prognosis of patients with olfactory dysfunction is relatively favorable; olfactory and gustatory dysfunction can be used as a sign for early screening; females are more prone to olfactory and gustatory dysfunction. It clarify the incidence and clinical characteristics of olfactory and gustatory dysfunction in Chinese COVID‐19 patients.This investigation compensates for the previous lack of data on olfactory and gustatory dysfunction in Chinese COVID‐19 patients.It provides important clues and evidence for the current screening of patients, especially targeting asymptomatic carriers and worldwide epidemic prevention and control. It clarify the incidence and clinical characteristics of olfactory and gustatory dysfunction in Chinese COVID‐19 patients. This investigation compensates for the previous lack of data on olfactory and gustatory dysfunction in Chinese COVID‐19 patients. It provides important clues and evidence for the current screening of patients, especially targeting asymptomatic carriers and worldwide epidemic prevention and control.

Alongside the rapid growth of wind power installation, wind curtailment is becoming more serious in China. As a type of clean and high-energy-density secondary energy, hydrogen will play a vital role in large-scale energy storage in future low-carbon energy systems. Incorporating hydrogen energy storage into integrated energy systems is a promising way to enhance the utilization of wind power. Therefore, a bi-level optimal configuration model is proposed in which the upper-level problem aims to minimize the total configuration cost to determine the capacity of hydrogen energy storage devices, and the lower-level problem aims to minimize the operational cost considering the change in hydrogen production efficiency. Owing to the non-convex characteristic of the lower-level problem containing integer variables, the bi-level model cannot be directly converted into a single-level model. Hence, a data-driven surrogate algorithm is applied to solve this complex bi-level programming problem. Case studies show that the proposed method can increase the economic and environmental benefits and improve energy efficiency. Sensitivity analysis is performed to investigate the influence of investment cost and carbon emission price on the results. The correctness and effectiveness of the proposed algorithm are verified by comparison with the particle swarm optimization algorithm.

To increase distributed energy resource (DER) utilization while reducing their negative influence, this article proposes an increment-exchange-based decentralized multiobjective optimal power flow (MO-OPF) algorithm for distribution grids with high-DER integration. In the MO-OPF model, the network operator and DER owners pursue their own operational and economic objectives while ensuring that voltages and branch power limits are satisfied throughout the network. The proposed algorithm enables the solution of the MO-OPF problem in a decentralized manner, without exposing any DER's private information to the network operator. Its core idea is to use the quadratic functions of coupling variables’ increments to describe the impact of DER owners on the network operator; then the network operator computes variables’ increments by solving accumulated quadratic programming problems. The resulting method can provide the same evenly distributed Pareto-optimal solutions as the centralized model, and it has plug-and-play features and scalability properties. Case studies on an IEEE 33-bus and a real 266-bus distribution system are carried out to demonstrate the merits of the proposed algorithm.

Temporal trends and geographical variations in disease burden for diabetes mellitus (DM) and cardiovascular disease (CVD) attributable to high body mass index (BMI) in China have not been fully elucidated.

Background: The present study was designed to evaluate the efficacy and safety of a combination of helical tomotherapy (HT) or intensity-modulated radiotherapy (IMRT) and EGFR (epidermal growth factor receptor) inhibitor (Cetuximab or Nimotuzumab) with or without chemotherapy in patients with locally advanced hypopharyngeal carcinoma. Patients and Methods: The retrospective study included forty-six patients (12 stage III and 34 stage IV) with locally advanced hypopharyngeal cancer. Among them, 20 were treated with induction chemotherapy with docetaxel and cisplatin (TP) followed by concurrent chemoradiotherapy with cisplatin and EGFR inhibitor, 13 received concurrent chemoradiotherapy with cisplatin and EGFR inhibitor, and 13 were treated with concurrent radiotherapy plus EGFR inhibitor. HT and IMRT were performed in 33 and 13 patients, respectively. Side effects were evaluated with the established CTCAE (Common Terminology Criteria for Adverse Events) 3.0 criteria. Results: The median follow-up time was 39.4 months (range 3-69 months). All patients completed the planned RT without any treatment breaks. The 3-year local control survival, disease-free survival, overall survival, and laryngeal preservation survival rates were 66.8%, 59.0%, 68.9%, and 86.7%, respectively. The most common grade 3 or higher side effect was oropharyngeal mucositis. One patient required dilatation of a pharyngeal stricture 18 months after treatment. No patient required percutaneous gastrostomy and tracheostomy tube. Conclusion: The treatment with EGFR inhibitor in combination with non-surgical combined modality in patients with hypopharyngeal carcinoma was well tolerated and resulted in encouraging laryngeal preservation survival rate. HT or IMRT, EGFR inhibitor, and effective management of severe oropharyngeal mucositis contributed to the positive outcomes.

In viticulture, grafting has been practiced widely and influences grape development as well as berry and wine quality. However, there is limited understanding of the effects of rootstocks on grape phenolic compounds, which are located primarily in the berry skin and contribute to certain sensory attributes of wine. In this study, scion–rootstock interactions were investigated at the green-berry stage and the veraison stage when grapevines were hetero-grafted with three commonly used rootstock genotypes (5BB, 101-14MG, and SO4). Physiological investigations showed that hetero-grafts, especially CS/5BB, contained higher concentrations of total proanthocyanidins (PAs) and various PA components in berry skins compared with the auto-grafted grapevines. Further metabolomics analysis identified 105 differentially accumulated flavonoid compounds, the majority of which, including anthocyanins, PAs, and flavonols, were significantly increased in the berry skins of hetero-grafted grapevines compared with auto-grafted controls. In addition, transcriptomic analysis of the same samples identified several thousand differentially expressed genes between hetero-grafted and auto-grafted vines. The three rootstocks not only increased the transcript levels of stilbene, anthocyanin, PA, and flavonol synthesis genes but also affected the expression of numerous transcription factor genes. Taken together, our results suggest that hetero-grafting can promote phenolic compound accumulation in grape berry skin during development. These findings provide new insights for improving the application value of grafting by enhancing the accumulation of nutritious phenolic components in grape.

Grapevine leafroll-associated virus 3 (GLRaV-3) is one of the causal agents of grapevine leafroll disease (GLD), which severely impacts grapevine production in most viticultural regions of the world. The development of virus-resistant plants is a desirable strategy for the efficient control of viral diseases. However, natural resistant resources have not been reported in the genus Vitis,and anti-GLRaV-3 research has been quite limited in grapevine. In this study, by expressing FnCas9 and LshCas13a, we established a highly effective transgenic construct screening system via an optimized Agrobacterium-mediated transient delivery system in grapevine plantlets. Our study indicated that CRISPR/FnCas9 and LshCas13a caused GLRaV-3 inhibition. Moreover, three vectors—pCR01-CP, pCR11-Hsp70h and pCR11-CP—exhibited the most robust inhibition efficiency compared to those targeting other sites and could be further engineered to generate GLRaV-3-resistant grapevine. In addition, the viral interference efficiency of FnCas9 was dependent on its RNA binding activity. The efficiency of virus inhibition was positively correlated with the level of Cas gene expression. Importantly, we demonstrated that LshCas13a had better interference efficiency against viruses than FnCas9. In summary, this study confirmed that these two RNA-targeting CRISPR mechanisms can confer immunity against viruses in grapevine, providing new avenues to control GLRaV-3 or other RNA viruses in fruit crops.

Distributed optimal power flow (OPF) is of great importance and challenge to AC/DC interconnected power grid with different dispatching centres, considering the security and privacy of information transmission. In this paper, a fully distributed algorithm for OPF problem of AC/DC interconnected power grid called synchronous ADMM is proposed, and it requires no form of central controller. The algorithm is based on the fundamental alternating direction multiplier method (ADMM), by using the average value of boundary variables of adjacent regions obtained from current iteration as the reference values of both regions for next iteration, which realizes the parallel computation among different regions. The algorithm is tested with the IEEE 11-bus AC/DC interconnected power grid, and by comparing the results with centralized algorithm, we find it nearly no differences, and its correctness and effectiveness can be validated.