In this paper, a post-event restoration method is proposed for power distribution systems with Internet data centers (IDCs) as critical loads. The proposed method is formulated as a mixed integer ...linear programming-based optimization model which considers emergency operation decisions in IDCs and restoration decisions in power distribution systems, and can minimize the utility loss of IDCs and other loads during the restoration process. Since the original form of the proposed method may face some problems in practical application, such as confidential information sharing and long computing time, a decoupling method is proposed to convert the integrated model into several sequential parts which are operated by power distribution systems or IDCs independently, so that the confidential information sharing issue is solved, and the computing time is significantly reduced. The proposed restoration method is tested on the IEEE 123-Bus Feeder with multiple IDCs to illustrate its feasibility and efficiency.
Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily energy consumption behaviors. It helps manage flexible demand response resources on peak load. ...However, it is insufficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mechanism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers energy consumption behaviors under different prices. Then, to avoid unfair evaluation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumers time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superiority of the proposed methods, meanwhile price risks from the price change can be hedged effectively.
The grinding process has become widely used to improve the fineness and performance of fly ash. However, most studies focus on the particle size distribution of ground fly ash, while the particle ...morphology is also an important factor to affect the performance of cement paste. This article aims at three different kinds of ground fly ash from the ball mill and vertical mill, and the particle morphology is observed by scanning electron microscopy (SEM) to calculate the spherical destruction (the ratio of spherical particles broken into irregular particles in the grinding process of fly ash), which provides a quantification of the morphology change in the grinding process. The fluidity of cement paste and the strength of cement mortar are tested to study the relation of spherical destruction and fluidity and strength. The results show that the spherical destruction of ground fly ash in a ball mill is more than 80% and that in a vertical mill with a separation system is only 11.9%. Spherical destruction shows a significant relation with the fluidity. To different addition of ground fly ash, the fluidity of cement paste decreases with the increase of spherical destruction. To the strength of cement paste, particle size distribution and spherical destruction are both the key factors. Therefore, spherical destruction is an important measurement index to evaluate the grinding effect of the fly ash mill.
Virtual power plants (VPPs) are a critical technology for distribution systems that can integrate various renewable energy resourcescontrollable loads and energy storage systems into one specific ...power plant through a distributed energy management system. This paper proposes a coordinated dispatch optimization model between the main grid and VPPs aiming to minimize both the power generation cost and total system active loss. When the time of the equivalent dispatching model is not divisible due to the existence of a time coupling constraint inside the VPPs, this model can obtain the global optimal solution through iteration between the main grid and the VPPs. By employing multi-parametric quadratic programming to obtain accurate critical domains and optimal cost functions, the convergence speed and stability are significantly improved. Additionally, a reactive power and voltage optimization technique leveraging the generalized Benders decomposition is presented for the coordination of the main grid and the VPPs. Moreover, the impact of distributed energy resource (DER) clusters on the main grid was studied, from which we proved that the proposed approach can expeditiously abate energy production expenditure and system active dissipation whilst enhancing the system equilibrium.
Background
Deep learning based unsupervised registration utilizes the intensity information to align images. To avoid the influence of intensity variation and improve the registration accuracy, ...unsupervised and weakly‐supervised registration are combined, namely, dually‐supervised registration. However, the estimated dense deformation fields (DDFs) will focus on the edges among adjacent tissues when the segmentation labels are directly used to drive the registration progress, which will decrease the plausibility of brain MRI registration.
Purpose
In order to increase the accuracy of registration and ensure the plausibility of registration at the same time, we combine the local‐signed‐distance fields (LSDFs) and intensity images to dually supervise the registration progress. The proposed method not only uses the intensity and segmentation information but also uses the voxelwise geometric distance information to the edges. Hence, the accurate voxelwise correspondence relationships are guaranteed both inside and outside the edges.
Methods
The proposed dually‐supervised registration method mainly includes three enhancement strategies. Firstly, we leverage the segmentation labels to construct their LSDFs to provide more geometrical information for guiding the registration process. Secondly, to calculate LSDFs, we construct an LSDF‐Net, which is composed of 3D dilation layers and erosion layers. Finally, we design the dually‐supervised registration network (VMLSDF) by combining the unsupervised VoxelMorph (VM) registration network and the weakly‐supervised LSDF‐Net, to utilize intensity and LSDF information, respectively.
Results
In this paper, experiments were then carried out on four public brain image datasets: LPBA40, HBN, OASIS1, and OASIS3. The experimental results show that the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD) of VMLSDF are higher than those of the original unsupervised VM and the dually‐supervised registration network (VMseg) using intensity images and segmentation labels. At the same time, the percentage of negative Jacobian determinant (NJD) of VMLSDF is lower than VMseg. Our code is freely available at https://github.com/1209684549/LSDF.
Conclusions
The experimental results show that LSDFs can improve the registration accuracy compared with VM and VMseg, and enhance the plausibility of the DDFs compared with VMseg.
Renewable energy sources play a key role in the transition towards clean and affordable energy. However, grid integration of renewable energy sources faces many challenges due to its intermittent ...nature. The controllability of aggregated regenerative electric heating load provides a method for the consumption of renewable energy sources. Based on the concept of a virtual power plant (VPP), this paper considers the cooperative energy management of aggregated residential regenerative electric heating systems. First, considering physical constraints, network constraints, and user comfort, comprehensive modeling of a VPP is given to maximize its social benefits. In addition, this VPP is investigated as a participant in day-ahead energy and reserve markets. Then, to solve this problem, a distributed coordination approach based on an alternating direction method of multipliers (ADMM) is proposed, which can respect the independence of users and preserve their privacy. Finally, the simulation results illustrate the effectiveness of our algorithm.
To improve the sensitivity of surface-enhanced Raman spectroscopy (SERS) detection, we propose a three-dimensional (3D) SERS chip based on an inverted pyramid micro-reflector (IPMR) that converges ...Raman scattering light signals to improve the signal collection efficiency. The influence of the geometric parameters of the inverted pyramid structure on the Raman signal collection efficiency was analyzed by simulation for the determination of the optimal design parameters. The inverted pyramid through-hole structure was prepared on the silicon wafer through an anisotropic wet etching process, followed by the sputtering of a gold film to form the IPMR. The 3D SERS chip was constructed by bonding the IPMR and the active substrate that assembled with silver nanoparticles. Using Rhodamine 6G molecules, the Raman intensity measured with the 3D SERS chip was threefold greater than that of the silicon-based SERS substrate under the same test conditions. These experimental results show that the 3D SERS chip can significantly improve the SERS signal intensity. Its 3D structure is convenient for integration with microfluidic devices and has great potential in biochemical detection applications.
Spatial relationships between forest and PM2.5 concentrations are of great policy implications in regional afforestation layout and air pollution control. This paper investigates the transboundary ...externality of a city’s forest on the concentrations of PM2.5 in different city segments. Employing a mixed-regressive spatial panel model with data for 255 Chinese cities over 2000 to 2015, we find that the concentrations of PM2.5 tend to be substantially lower in cities with larger forest area and the depositing effect of forest spills over significantly to neighboring cities. A one percentage increase in forest area reduces the average annual concentrations of PM2.5 by 2.53%, of which 76% is contributed to the spillover effect. Moreover, the average marginal effect of forest on PM2.5 concentrations exhibits an inverted-U relationship with wind speed and the depositing effect minimizes (in magnitude) as the average annual speed of wind approaches to 23 kilometers per hour. These findings suggest that severe hazing cities with mild wind speed are priority afforestation areas for transboundary air pollution control.
•We quantified the own and spillover effects of forest on PM2.5 concentrations with a panel dataset of 255 Chinese cities over 2000 to 2015.•The concentrations of PM2.5 tend to be substantially lower in cities with larger forest area and the effect spills over significantly to neighboring cities.•PM2.5 concentrations in a city are more heavily affected by an average increase in forest area of its neighbors.•The depositing effect of forest on PM2.5 minimizes (in magnitude) as the average annual wind speed approaches to 23 km per hour.
In this work, we present a simple and novel digital surface-enhanced Raman spectroscopy (SERS)-microfluidic chip designed for the rapid and accurate quantitative detection of microorganisms. The chip ...employs a high-density inverted pyramid microcavity (IPM) array to separate and isolate microbial samples. The presence or absence of target microorganisms is determined by scanning the IPM array using SERS and identifying the characteristic Raman bands. This approach allows for the “digitization” of the SERS response of each IPM, enabling quantification through the application of mathematical statistical techniques. Significantly, precise quantitative detection of yeast was achieved within a concentration range of 106–109 cells/mL, with the maximum relative standard deviation from the concentration calibrated by the cultivation method being 5.6%. This innovative approach efficiently addresses the issue of irregularities in SERS quantitative detection, which arises due to fluctuations in SERS intensity and poor reproducibility. We strongly believe that this digital SERS-microfluidic chip holds immense potential for diverse applications in the rapid detection of various microorganisms, including pathogenic bacteria and viruses.
To gain a deep understanding of the interaction between underground mining and mountain deformation, based on historical deformation and the UAV video, we analyzed the evolution process of ...deformation and failure in detail and comprehensively evaluated the slope deformation and fracture network under the action of underground mining via the bottom friction physical simulation test, DPDM technology, fractal theory, and percolation theory. We simulated the whole process of mining, deformation, and failure of the Pusa collapse. DPDM technology was employed to obtain the evolution process of the total displacement, maximum shear strain (
γ
max
), and volumetric strain
ε
v
of the Pusa collapse and establish a relationship between the fractal dimension and settlement. Simultaneously, the fractal dimension, fracture number, fracture rate, and percolation probability of the fracture network were calculated in MATLAB software. The research results of the bottom friction physical simulation test and DPDM technology indicated that after the M10 coal seam was mined, the maximum total displacement and maximum shear strain
γ
max
were mainly located in the direct roof, resulting in volume expansion due to the direct roof collapse. After the M14 coal seam was mined, the maximum total displacement and volume strain
ε
v
developed towards the slope top, and the maximum shear strain was located in the middle and lower parts of the model surface and the leading and trailing edges of the slope top, respectively. The research results of fractal dimension and percolation probability indicated that after the M10 coal seam was mined, the development form of the fracture network at this stage mainly entailed the formation of new fractures. After the M14 coal seam was mined, the fracture network developed from beyond this stage mainly included fracture expansion and opening. The test results are consistent with the historical change process and the UAV video showing the method and signs of deformation. These research results help to better explain the deformation evolution process of a given slope under the action of underground mining and provide a technical reference for accurate assessment and proper mitigation of similar landslide disasters.
PDF
