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With the large-scale integration of distributed generators (DGs), the relationships between transmission network (TN) and distribution networks (DNs) are becoming more closely, especially in the aspects of reactive power and voltage. Optimal reactive power flow (ORPF) problems for TN and DNs are not suitable to be solved independently. Due to the fact that TN and DNs are operated by different control centers, a heterogeneous decomposition (HGD) based distributed ORPF method for global transmission and distribution (T&D) networks is presented in this paper. Considering the characteristics of the master–slave structure, the global T&D-ORPF problem can be decomposed into the TN-ORPF master sub-problem, the DN-ORPF slave sub-problems and the boundary consistency coordination sub-problem. Any of those optimization algorithms based on duality and gradient theory can be adopted to solve the TN-ORPF or DN-ORPF sub-problems. By incorporating a penalty function into optimization algorithm, the discrete control variables can successively discretized and therefore the augmented Lagrangian functions are differentiable. Boundary sensitivities constructed by the boundary dual multipliers are used to decouple the global ORPF problem of T&D networks. The same solution as the centralized optimization is achieved by transferring the boundary variables and sensitivities between the TN control center and DN control centers. The simulation results of IEEE 30-bus (TN) and modified IEEE 33-bus (DN) test systems containing multiple DGs show that the HGD algorithm is effective. [ABSTRACT FROM AUTHOR]

Contralateral subdural effusion (CSE) after decompressive craniectomy (CSEDC) is occasionally observed. Cranioplasty is routinely performed for reconstruction and has recently been associated with improving contralateral subdural effusion. We sought to systematically review all available literature and evaluate the effectiveness of cranioplasty for CSE. A PubMed, Web of Science, and Google Scholar search was conducted for preferred reporting items following the guidelines of systematic review and meta-analysis, including studies reporting patients who underwent cranioplasty because of CSEDC. The search yielded 8 articles. A total of 56 patients ranging in age from 21 to 71 years developed CSEDC. Of them, 32 patients underwent cranioplasty. Eighteen cases with symptomatic CSE underwent cranioplasty alone, 2 cases received Ommaya drainage later because of a recurrence of CDC, and 1 case underwent a ventriculoperitoneal shunt because the CSE did not resolve completely and the ventricle was dilated again. The symptoms of 14 cases lessened without recurrence after simultaneous cranioplasty and drainage or a shunt. The total success rate (CSE disappeared without recurrence) was 90.6% for patients who underwent cranioplasty; however, the total incidence of hydrocephalus was 40.1%. This review suggests that cranioplasty is effective for the treatment of CSEDC, particularly intractable cases, but early cranioplasty may be more effective. In addition, hydrocephalus is fairly common after cranioplasty and requires further treatment. [ABSTRACT FROM AUTHOR]

The development of smart substations plays a crucial role in the development of smart grid. The major difference between a smart substation and an existing conventional substation lies in the process bus. Based on a thorough study over the network schemes of sampled values for process bus in smart substations, this paper summarizes and analyses major characteristics of existing network schemes. In order to overcome shortcomings of existing network schemes in time synchronization, the paper proposes a novel network scheme for process bus in smart substations without using external synchronizing clock. Compared to conventional network schemes, the proposed scheme does not need an external global synchronizing clock, thus can significantly improve the reliability of the process bus network. As a major research output of the key technological project, this work has been successfully applied to the 110 kV Shenxiang Substation in Suzhou, China. [ABSTRACT FROM AUTHOR]

Industrial energy conservation and CO 2 emission reduction (ECCER) management is a multi-objective optimization problem with multiple uncertainty factors. However, most studies have used deterministic optimization approaches, and neglected the uncertainty factors that affect the effectiveness of the management strategies. This study adopts a multi-objective optimization model under uncertainty to solve the ECCER management problem in China's iron and steel industry. Three objectives: minimum energy intensity, maximum CO 2 emission reduction, and minimum cost, are optimized simultaneously. This study simulates the perturbation of the uncertainty parameters within their fluctuation ranges via Latin Hypercube Sampling, adopts the mean objective function value mechanism to calculate the objective value, and obtains the optimized results using the second generation of the Non-dominated Sorting Genetic Algorithm (NSGA-II). Lastly, this study sets three types of preferences to generate final decision strategies via a Vague set-based approach. Results show: (1) The algorithm is reliable as per the verification of Hypervolume indicator and Spacing Metric; (2) The average values of energy intensity and CO 2 emission reduction amount in optimal solutions are 524.00 kgce and 125.03 kg per ton steel respectively, which are 6.3% and 7.6% lower than the deterministic optimal ones; (3) The decision strategies encourage the wider application of large-sized process equipment, identify 8-9 advanced technology and eight reutilization approaches as key measures, but find the use of renewable energy will still be in low level. This study aims to solve the industrial ECCER optimization problem under uncertainty, and put forward policy suggestions in sustainable manufacturing in this industry. • Energy conservation, CO 2 emission reduction, and cost control are set as objectives. • Random sampling is used to simulate the fluctuation of uncertain parameters. • This study adopts NSGA-II to search for optimal solutions. • Final decision strategies are generated via a Vague set based approach. [ABSTRACT FROM AUTHOR]

Flexible loads are important resources to help maintain the power balance of smart grid. However, the control center is not able to exactly sense and control them because of their large quantity and wide distribution. In order to get the flexible loads to participate in the dynamic power system look-ahead scheduling, this paper proposes a three-layer ‘centralized coordination, distributed control’ structure in which the load agents are introduced to perform the coordination based on the consensus control. Then, the optimal control strategy of the control center and the consensus control strategy of the load agents are designed. With the communication among different flexible loads, the distributed cooperative control is implemented. At last, the efficiency of the proposed mode and strategies is proven through the simulation carried out under the standard IEEE 9-bus system, and the effect of topology and communication is also analyzed. [ABSTRACT FROM AUTHOR]

• BDNF in the spinal dorsal horn are closely connected with neuropathic pain. • In physiological conditions, BDNF is an important regulator of neuronal development, synaptic transmission, and cellular and synaptic plasticity. • In pathological conditions, BDNF in the spinal dorsal horn may change the CNS from an adaptive state to an unadaptive state. • Elucidate the mechanism of BDNF in spinal dorsal horn and neuropathic pain. Neuropathic pain (NP) is caused by direct or indirect damage to the nervous system and is a common symptom of many diseases. The mechanisms underlying the onset and persistence of NP are unclear. Therefore, research concerning these mechanisms has become an important focus in the medical field. Brain-derived neurotrophic factor (BDNF) is a member of the neurotrophic factor family of signaling molecules. BDNF is an important regulator of neuronal development, synaptic transmission, and cellular and synaptic plasticity, which are essential for nerve maintenance and repair. However, BDNF is upregulated in the spinal dorsal horn and can promote NP by activating glial cells, reducing inhibitory functions and enhancing excitement after nociceptive stimulation. This review considers the relationship between NP and BDNF signaling in the spinal dorsal horn and discusses potentially related pathological mechanisms. [ABSTRACT FROM AUTHOR]

Power dispatch problems become more complex when the weight of uncertain renewable resources in the power system gradually increases in recent years. To make use of renewable energy, such as wind energy, more adequately, wisely and intelligently, higher requirements are placed on the level of inter-region power dispatch coordination. In the context, solving the problem of power dispatch on a large scale in near-real-time (5 min in this paper) becomes more important. In this paper, the power dispatch was treated as a sequential decision-making problem and Deep Reinforcement Learning (DRL) with continuous control was introduced to offer a smarter solution. In this way, we designed a novel interactive learning environment based on the economic power dispatch model for the DRL algorithm and we proposed two feasible implementations to handle the different application scenarios. As a result, DRL with a continuous control method has a great performance in our proposed implementations. Moreover, we found that dispatching data richness has a significant influence on the generalization of the learned policy. Image 1 • A novel learning method for ED within 5-min considering the injection of large-scale wind power is presented. • This paper designed an interactive parallel learning framework based on multiple source-lord scenarios. • It improves learning efficiency and brings effective utilization of historical data. • Data richness has a significant influence on the generalization of the learned policy. [ABSTRACT FROM AUTHOR]

• A novel nonintrusive load monitoring algorithm applicable to low-resolution data is proposed. • The algorithm improves the disaggregation accuracy and computational efficiency. • An analysis of how the data resolution affects the prediction performance is performed. • A cross-prediction approach is proposed that does not require a household's own historical data. • Four real-world datasets are used to validate the effectiveness of the methodology. Detailed information on individual appliance consumption is beneficial for improving energy efficiency and managing demand response. Nonintrusive load monitoring (NILM) aims to estimate the device-level energy consumption from the load data of an entire household. Because the majority of households can only provide load data at a normal smart-meter level, this paper introduces a novel similar time window (STW) algorithm to perform NILM with lower-resolution data. Derived from k-nearest neighbors (kNN), the proposed STW algorithm compares both the time and frequency domain similarities between windows of interest and historical data segments, and then selects the most similar time windows by instance-based learning to determine the device-level energy consumption. The desirable features of this algorithm include (1) reductions in the costs of and requirements for sensing equipment, (2) improvements in privacy preservation, and (3) a significant enhancement of the computational efficiency. To facilitate the selection of the data resolution and to satisfy the NILM application requirements in a cost-effective way, the paper also investigates the relationship among the input/output data resolution, time window length and prediction accuracy. To enable the generalizability of this algorithm, a cross-prediction approach is proposed to obtain the device-level consumption from a "library" of a group of households, without knowing each one's own historical data. Simulation results using four real-world public datasets demonstrate the competitive performance of the proposed STW algorithm with respect to traditionally used approaches for low-resolution NILM. [ABSTRACT FROM AUTHOR]