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Three mole percent yttria-stabilized tetragonal zirconia polycrystal (3Y-TZP) ceramics are exemplary materials for dental restoration due to their high mechanical strength, fracture toughness, chemical endurance, and biocompatibility. Nevertheless, 3Y-TZP ceramics are opaque and their CAD/CAM manufacturing process may cause micro-cracks in conventional clinical practice. In this study, 3Y-TZP ceramic samples were prepared using vat photopolymerization, pre-sintering, external staining, and final high-temperature sintering. The microstructures, mechanical properties, optical properties, and cytotoxicity of the 3Y-TZP ceramic samples were investigated. The results indicate that with increasing Fe3+ concentration of staining solution from 0.1 mol/L to 0.3 mol/L and increasing staining time from 5 s to 30 min, the 3Y-TZP ceramic samples showed a tetragonal crystal structure of zirconia with densely packed grains and a slight increase in grain size. The flexural strength, Vickers hardness, and fracture toughness of 3Y-TZP ceramic samples stained in 0.1–0.3 mol/L Fe3+ solution for 5 s to 3 min were all greater than 665 MPa, 11.9 GPa, and 5 MPa m1/2, respectively, meeting the mechanical requirements for clinical application. Colorimetric analysis revealed a decrease in L* (black-white index) from 90.4 to 81.3, an increase in a* (green-red index) from −1.5 to 3.2, and an increase in b* (blue-yellow index) from 11.6 to 20.3, approximating the commercial VITA 3D-Master Shade Guide chromaticity. Furthermore, the 3Y-TZP ceramic samples exhibited a cell viability of 90% or higher toward L929 cells.

Abstract The occurrence of NAD+ as a non-canonical RNA cap has been demonstrated in diverse organisms. TIR domain-containing proteins present in all kingdoms of life act in defense responses and can have NADase activity that hydrolyzes NAD+. Here, we show that TIR domain-containing proteins from several bacterial and one archaeal species can remove the NAM moiety from NAD-capped RNAs (NAD-RNAs). We demonstrate that the deNAMing activity of AbTir (from Acinetobacter baumannii) on NAD-RNA specifically produces a cyclic ADPR-RNA, which can be further decapped in vitro by known decapping enzymes. Heterologous expression of the wild-type but not a catalytic mutant AbTir in E. coli suppressed cell propagation and reduced the levels of NAD-RNAs from a subset of genes before cellular NAD+ levels are impacted. Collectively, the in vitro and in vivo analyses demonstrate that TIR domain-containing proteins can function as a deNAMing enzyme of NAD-RNAs, raising the possibility of TIR domain proteins acting in gene expression regulation.

In this paper, the integrated demand response (IDR) optimization problem is studied under the consideration of herd mentality. The contradiction between power supply and demand is becoming increasingly prominent. IDR can maintain the balance between power supply and demand, ensuring the safe operation of the system. The establishment of an IDR model that takes into account the herd mentality of consumers and the renewable energy supply, fully enhances consumers’ enthusiasm for participating in demand response and input renewable energy into the comprehensive energy system to improve energy conversion rate and supply, which is closer to the actual IDR process. Using the Genetic Simulated Annealing Algorithm (GSA) to solve multi-objective optimization problems with comprehensive energy efficiency, operating costs and peak-to-average ratio as optimization objectives has significant advantages in terms of iteration speed and solution accuracy. The analysis of the simulation shows that the IDR optimization model can not only improve the stability and comprehensive energy efficiency of the system but also reduce the operating cost of the system, which proves the effectiveness of the proposed method.

Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods based on peak shaving demand forecasts. However, due to the randomness of supply and demand, fluctuations in peak shaving demand occur, making it a significant technical challenge to meet peak shaving needs under limited budget allocations. To address this issue, this paper first conducts a clustering analysis of various adjustable load characteristics to derive typical electricity consumption curves, and then proposes a differentiated calculation method for the value of multi-time-segment peak shaving. Subsequently, an optimization model for LA scheduling and compensation pricing is established based on the limited peak shaving budget and time-segment peak shaving value. While ensuring the economic benefits of LAs, the model also analyzes the impact of different peak shaving budget allocations on the scale of peak shaving that can be achieved. Finally, case studies demonstrate that, compared to traditional fixed compensation pricing, the proposed pricing method reduces scheduling costs by an average of 16.5%, while significantly improving the overall satisfaction of adjustable users.

This study investigates the tracking of underwater cables using autonomous underwater vehicles (AUVs) equipped with side-scan sonar (SSS). AUV motion stability is crucial for effective SSS imaging, which is essential for continuous cable tracking. Traditional methods that derive AUV guidance rates directly from measured cable states often cause unnecessary jitter when imaging, complicating accurate detection. To address this, we propose a non-myopic receding-horizon optimization (RHO) strategy designed to maximize cable imaging quality while considering AUV maneuvering constraints. This strategy identifies the optimal heading decision sequence over a future horizon, ensuring stable and efficient cable tracking. We also employ a long short-term memory (LSTM) network to predict future cable states, further minimizing AUV motion instability during abrupt path changes. Given the computational limitations of AUVs, we have developed an efficient decision-making framework that can execute resource-intensive algorithms in real time. Finally, the robustness and effectiveness of the proposed algorithm were validated through comparative experiments. The results demonstrate that the proposed method outperforms existing methods in key metrics such as cable-tracking accuracy and AUV motion stability. This ensures that the AUV can acquire high-quality acoustic images of the submarine cable in an optimal state, enhancing the continuity and reliability of cable-tracking tasks.

Abstract Bioenergetic therapy is emerging as a promising therapeutic approach. However, its therapeutic effectiveness is restricted by metabolic plasticity, as tumor cells switch metabolic phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) to compensate for energy. Herein, Metformin (MET) and BAY‐876 (BAY) co‐loaded CuFe2O4 (CF) nanoplatform (CFMB) is developed to boost energy deprivation by synchronous interventions of glycolysis and OXPHOS for bioenergetic therapy synergetic with chemodynamic/photothermal therapy (CDT/PTT). The MET can simultaneously restrain glycolysis and OXPHOS by inhibiting hexokinase 2 (HK2) activity and damaging mitochondrial function to deprive energy, respectively. Besides, BAY blocks glucose uptake by inhibiting glucose transporter 1 (GLUT1) expression, further potentiating the glycolysis repression and thus achieving much more depletion of tumorigenic energy sources. Interestingly, the upregulated antioxidant glutathione (GSH) in cancer cells triggers CFMB degradation to release Cu+/Fe2+ catalyzing tumor‐overexpressed H2O2 to hydroxyl radical (∙OH), both impairing OXPHOS and achieving GSH‐depletion amplified CDT. Furthermore, upon near‐infrared (NIR) light irradiation, CFMB has a photothermal conversion capacity to kill cancer cells for PTT and improve ∙OH production for enhanced CDT. In vivo experiments have manifested that CFMB remarkably suppressed tumor growth in mice without systemic toxicity. This study provides a new therapeutic modality paradigm to boost bioenergetic‐related therapies.

The efficient and stable application of periodic forcing for drag-reduction can help underwater vehicles operate at high speed for long durations and improve their energy-utilization efficiency. This study considers flow control around a body-of-revolution model subjected to periodic blowing or suction through annular slots. The focus is on the boundary-layer structure, properties, and drag of the control fluid under a wide range of body variables (size, free-flow velocity, slot area, and blowing/suction velocity) and control parameters (normalized periodic-forcing amplitude and relative slot sizes). Body variables differ in their effects on the drag-reduction rate, with the surface pressure pushing the model vehicle when S and v are higher than S0 and v0. In particular, the lowest pressure drag was −26.4 N with v increasing, and the maximum drag-reduction rate of total drag exceeded 135%. At a fixed Reynolds number, increasing the values of the control parameters leads to larger-scale unstable vortex rings downstream from the slots; the surface-velocity gradient is reduced, effectively lowering the drag. A simple model relating the periodic fluctuation of pressure drag to the body variables is developed through quantitative analysis and used to determine navigational stability.

Abstract Iron deficiencies are the most common nonenteric syndromes observed in patients with inflammatory bowel disease, but little is known about their impacts on immune tolerance. Here we show that homeostasis of regulatory T cells in the intestine was dependent on high cellular iron levels, which were fostered by pentanoate, a short-chain fatty acid produced by intestinal microbiota. Iron deficiencies in Treg caused by the depletion of Transferrin receptor 1, a major iron transporter, result in the abrogation of Treg in the intestine and lethal autoimmune disease. Transferrin receptor 1 is required for differentiation of c-Maf+ Treg, major constituents of intestinal Treg. Mechanistically, iron enhances the translation of HIF-2α mRNA, and HIF-2α in turn induces c-Maf expression. Importantly, microbiota-produced pentanoate promotes iron uptake and Treg differentiation in the intestine. This subsequently restores immune tolerance and ameliorated iron deficiencies in mice with colitis. Our results thus reveal an association between nutrient uptake and immune tolerance in the intestine.

Wheat production is threatened by wheat yellow mosaic virus (WYMV). Here, via genome-wide association study, the authors report that a putative methyltransferase B positively regulates WYMV infection through enhancing viral genomic RNA stability by N6-methyladenosine RNA modification.

Enhanced turbulence triggered by near-inertial wave (NIW) trapping by a mesoscale anticyclone and typhoon “Kompasu” was observed in the northern South China Sea. The observations provide evidence for the trapping of NIW packets of amplitude ~0.2 m/s near the base of an anticyclonic eddy, and of ~0.3 m/s after the passage of typhoon “Kompasu”. The wave energy was amplified in a layer located near the base of the anticyclonic eddy, between 150 and 300 m, while stronger NIWs triggered by the typhoon extended to depths > 500 m. Diffusivity was calculated by a fine-scale parameterization. A diffusivity elevated by one order of magnitude, the occurrence of high near-inertial velocity shears, and the low (≤1) Richardson numbers were consistent with turbulence production and mixing from the base of the anticyclonic eddy and following the passage of the typhoon, and were associated with the trapped NIWs. This study showed that, by serving as a bridge between mesoscale eddies and small-scale motion, NIWs are an important pathway for ocean energy transmission. Mesoscale-NIW interactions represent a significant source of NIWs as well as a sink of mesoscale energy.

Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-time data assimilation from flow sensing, and the optimization of AMVs’ sensing strategies, culminating in a closed-loop dynamic data-driven application system (DDDAS). This article presents a novel DDDAS that systematically integrates flow modeling, data assimilation, and adaptive flow sensing using networked AMVs. It features a hybrid data-driven flow model, uniting a neural network for trend prediction and a Gaussian process model for residual fitting. The neural network architecture is designed using knowledge extracted from historic flow data through tidal harmonic analysis, enhancing its capability in flow prediction. The Kriged ensemble transform Kalman filter is introduced to assimilate spatially correlated flow-sensing data from AMVs, enabling effective model learning and accurate spatiotemporal flow prediction, while forming the basis for optimizing AMVs’ flow-sensing paths. A receding horizon strategy is proposed to implement non-myopic optimal path planning, and a distributed strategy of implementing Monte Carlo tree search is proposed to solve the resulting large-scale tree searching-based optimization problem. Computer simulations, employing underwater gliders as sensing networks, demonstrate the effectiveness of the proposed DDDAS in predicting depth-averaged flow in nearshore ocean environments.

With increasing global shortage of fresh water resources, many countries are prioritizing desalination as a means of utilizing abundantly available seawater resources. Integrated energy efficiency evaluation is a scientific method for the quantitative analysis of energy efficiency based on multiple indicators and is very useful for investment, construction, and scientific decision-making for desalination projects. In this paper, the energy efficiency evaluation of the micro energy network (MEN) of desalination for multi-source and multi-load is studied, and the basic idea of comprehensive energy efficiency evaluation is analyzed. The process includes the use of a MEN model to establish an integrated energy efficiency evaluation index system, taking into consideration energy, equipment, economic, environmental, and social factors. A combined evaluation method considering subjective and objective comprehensive weights for multi-source multi-load desalination MENs is proposed to evaluate the energy efficiency of desalination and from multiple perspectives.

Multibody marine robots, representing a promising research and development area, have gained acute attention and response from civil and military fields. A multibody marine robot is a multibody system composed of multiple single-body marine robots (defined as a unit) in a certain topology. Such a system is generally characterized by underactuation, hyperredundancy, power distribution, modularization and so on. Compared with traditional single-body marine robots, multibody marine robots can carry greater loads and have more forms of motion, so they can meet more specialized functional requirements. At the same time, they also have more complex hydrodynamic characteristics due to their unique structures and motion forms. In this paper, the types, features, and hydrodynamic characteristics of typical multibody marine robots are reviewed and summarized based on the connection structures between units. In addition, the development trends of multibody marine robots are analyzed and discussed. This review work anticipates positive attention from future readers regarding multibody marine robots, which represent an important subarea of marine robots.

Using underwater gliders to measure underwater acoustic signals is a new measurement method emerging with the development of platform technology. Although underwater gliders are relatively quiet due to the absence of propellers, vehicle noise generated during motion is still inevitable, which will affect the recorded acoustic data. In this paper, we analyze the self-noise characteristics of underwater gliders based on simulated data by CFD technology for the hydrodynamic flow noise and experimental data acquired in an anechoic-water-tank experiment for the mechanical noise. The mechanical noise covers noises generated by buoyancy regulating (increasing and reducing), pitch regulating, rudder regulating and CTD pump working. According to the analysis results, the flow noise and CTD pump working noise could be ignored for the experimental data processing of sea trials. An experiment was conducted with an acoustic Sea-Wing underwater glider in the South China Sea from July 31 to September 4, 2018. Two kinds of noisy data were recorded, including target signals and ambient noise. All the target signals could be recognized after convolution filtering, except during the buoyancy regulating periods due to the high noise spectrum level. For the recorded ambient noise, in addition to the buoyancy regulating noise, the rudder and pitch regulating noises affected the recorded data. Then based on the acquired knowledge, a joint convolution filtering and thresholding method is proposed to remove the rudder and pitch noises from recorded noisy data. Kernels extracted from data acquired in the anechoic-water-tank experiment are used in the convolution filtering to localize each regulating action and energy thresholding is adopted to determine the duration of each regulation. All the rudder and pitch noises are removed from the recorded noisy data.

Wing-sailed autonomous sailing monohulls are promising platforms used in various scenarios to provide data for marine science research. These platforms need to operate long-term in changing seas; their general configurations (size matching between sail, hull, and keel) necessitate careful trade-offs to balance safety and efficiency. Since autonomous sailboats are often designed for different observation missions, scientific pay-loads and target areas, their design space is considerably large. It is also challenging to obtain prior performance estimation from historical designs. Therefore, traditional offline surrogate-based simulation-driven design frameworks suffer from a large amount of sampling required, the computational cost of which remains too expensive for such ad hoc design tasks. This paper proposes an innovative, generalised simulation-driven framework combining Bayesian optimisation and knowledge transfer. It allows for high-quality, low-cost optimisation of autonomous sailing monohulls’ general configuration without initial design and prior performance estimation. The proposed optimisation framework has been used to optimise the ‘Seagull’ prototype within the design constraints. The optimised design exhibits significant performance improvements. At the same time, the results show that the present method is significantly superior to traditional offline methods. The authors believe that the proposed framework promises to provide the autonomous sailing community with a solution for a general design methodology.

With the rapid development of the economy, the continuously increasing population, and ongoing climate change, the shortage of freshwater resources has become an increasingly important global problem. Seawater desalination technology can effectively alleviate the pressure on freshwater supplies and has been investigated in many countries. However, the majority of existing projects focus on the research and development of desalination equipment and the use of new technologies and pay less attention to the operation optimization of the desalination process. The micro energy network (MEN) designed in this study is an efficient distributed energy supply system that can be used to simultaneously supply electricity, cooling, heating, and freshwater as photovoltaic power, wind power, combined heat and power (CHP), electric cooling and heating, and a seawater desalination device are integrated into the MEN. In this study, a model for operation optimization of a MEN for seawater desalination was developed and the influences of the electric cooling and heating ratios and the operation optimization of the seawater desalination device were studied with the aim of minimizing the life cycle cost. Based on the results of this study, MENs can reduce the operation cost of desalination devices and improve the efficiency of renewable energy sources. Keywords: Seawater desalination, Micro energy network, Operation optimization, Life cycle cost

This paper conducts comprehensive analysis about operational characteristics, market value, stakeholders, and operating modes of a megacity energy Internet. An innovating “one platform, two centers, and multiple nodes” operation management model is proposed. The study provides an in-depth explanation of the new operation and management model of the connotation and logical relationship. Building a comprehensive energy service platform, developing an integrated energy dispatch center, a market trading service center, communication, and other parts of the system needed to achieve benefits for all parties is including in the discussion. The proposed new model of operation and management has important guiding significance and reference for the development and construction of energy Internets in megacities. Keywords: Energy Internet, Mega city, Operation and management mode, Integrated energy service platform

In this paper, we propose time series prediction methods for depth-averaged current velocities (DACVs) of underwater gliders. Based on historical DACV data, these methods can predict the DACVs of future profiles with good performance. Regarding DACVs as time series, we use backpropagation neural network and least squares support vector machine (LSSVM) methods to predict the DACVs. To obtain better prediction performance, the features of DACVs are considered, and we use empirical mode decomposition (EMD) to decompose the time series into several sub-series. Then, the two methods are reused to predict each sub-series, and the results of all the sub-series with each method are added. Based on the real-time DACVs obtained from the simulation environment and the DACVs obtained from sea trials, we test and verify the four methods. The results demonstrate that all the methods exhibit a good prediction performance for conditions in which ocean currents are relatively regular; whereas in other cases, EMD-LSSVM shows inherent robustness and superiority compared with the other three methods.

An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended Kalman filter (EKF)-based method, combining a modified kinematic model of underwater gliders with the travel-time differences between signals received from a single beacon, is proposed for estimating the glider positions in a predict-update cycle. First, to accurately establish a motion model for underwater gliders moving in the ocean, we introduce two modification parameters, the attack and drift angles, into a kinematic model of underwater gliders, along with depth-averaged current velocities. The attack and drift angles are calculated based on the coefficients of hydrodynamic forces and the sensor-measured angle variation over time. Then, instead of satisfying synchronization requirements, the travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To further reduce the EKF estimation error, the Rauch-Tung-Striebel (RTS) smoothing method is merged into the EKF system. The proposed method is tested in a virtual spatiotemporal environment from an ocean model. The experimental results show that the performance of the RTS-EKF estimate is improved when compared with the motion model estimate, especially by 46% at the inflection point, at least in the particular study developed in this article.

The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results.

Background: GC--MS is a powerful tool for component analysis of unknown compounds, especially in the fields of analytical chemistry and detection of biological samples. To effectively identify compounds in GC--MS, one of the most important ways is to use a matching algorithm to compare the similarity between the reference spectrum and the query spectrum. Objective: To propose a novel way to improve compound identification accuracy. Methods: This article proposes a method based on an m/z set match. First of all, select the maximum m/z and the m/z corresponding to the highest peak intensity in a pre-search. Next, employ the space vector model to carry on a refined search in the remaining spectra after the pre-search. Then, distinguish stereoisomers according to the order of the G value. Results: Compared with the 10 peaks and the method based on an m/z number matching pre-search, the method based on m/z set matching showed higher accuracy and fewer remaining (missing) spectra. Furthermore, the refined search which is based on the m/z set matching method possesses shorter calculation time compared with no pre-search. Conclusions: The method can reduce the remaining spectra and speed up the identification of compounds. Highlights: The accuracy is higher, the number of remaining spectra is less, and the computational time is shorter. [ABSTRACT FROM AUTHOR]

Rationale: Ion mobility spectrometry (IMS) is a powerful analytical tool extensively applied in numerous domains. However, there still exists the phenomenon of peaks overlapping in the analysis of isomers with similar structures due to the limited resolution of IMS. In this paper, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm combined with second-order differentiation is proposed to separate the IMS overlapping peaks efficiently and precisely. Methods: It can identify the component number of overlapping peaks and limit those parameters (ion mobility, intensity, and full-width at half maximum of each single peak) of the peak model in a small range using second-order differentiation. Based on this, DIWPSO that has been set the best operating parameters is capable of accurately separating IMS overlapping peaks to identify the compound within a short time. Results: A comparison between the performance of DIWPSO and the improved particle swarm optimization (IPSO) found that DIWPSO with separation errors less than 2.34% overall outperforms IPSO whose maximum error is up to 5.58%. Moreover, the running time of DIWPSO is 30-80 times less than that of IPSO, and DIWPSO exhibits stronger robustness. Conclusions: This method can automatically identify the component number of IMS overlapping peaks and resolve them with muticomponents and different overlapped degrees rapidly and accurately, which further improves the structural resolution of IMS. [ABSTRACT FROM AUTHOR]

Rationale: Ion mobility spectrometry (IMS) is a powerful analytical tool that has been widely applied in many fields. However, the limited structural resolution of IMS results in peak overlapping in the analysis of samples with similar structures. We propose a novel method, improved particle swarm optimization (IPSO), for the separation of IMS overlapping peaks. Methods: This method, which prevents local optimization, is used to identify the peak model coefficients of IMS. Moreover, we use the half-peak width characteristics of IMS to determine the particle position range, which eliminates impossible combinations of single peaks and reduces the difficulty of identification of coefficients. Results: During a comparison in performance between IPSO and the genetic algorithm (GA), the results show that the maximum separation error of IPSO is only 1.45%, while the error of the GA is up to 17.43%. Moreover, the time consumed by IPSO is 95% less than that of the GA, and IPSO has a greater robustness under the same separation error conditions. Conclusions: The method proposed provides accurate analytical results in separating overlapping IMS peaks even in cases of severe overlaps, which greatly enhances the structural resolution of IMS. [ABSTRACT FROM AUTHOR]

Background: Gas chromatography–mass spectrometry (GC-MS) is one of the most widely used analytical techniques for analyzing chemical or biological samples in many fields. One of the most important approaches for the identification of compound in GCMS is to compare an experimental mass spectrum with a compound recorded in a reference spectral library through a spectrum-matching algorithm. Objective: To develop a novel method to speed up compound identification. Method: In this study, a method based on m/z matching is proposed. We selected the highest m/z values and m/z values corresponding to the largest peak intensities of a mass spectrum and stepwise modified the matching threshold (MTh) based on the principle of local optimum in the presearch process. The performance of the approach is evaluated using the mass spectral library maintained by the National Institute of Standards and Technology as a reference library and repetitive mass spectra as query spectra. Results: Compared with two-step spectral library pre-search and “ten peaks,” the method based on m/z matching has higher accuracy, smaller number of remaining (missing) spectra, and shorter computational time. Conclusions: Therefore, the method can effectively speed up compound identification. Highlights: A method based on m/z matching is proposed. The accuracy is higher, the number of remaining spectra is less, and the computational time is shorter. [ABSTRACT FROM AUTHOR]

The U.S. has emerged as the microgrid development leader with around 40% of worldwide capacity. Over the last decade, demonstrations have been executed by a mix of civilian federal, military, private, and local government entities. While their motivations are mixed, resilience became the focus following Superstorm Sandy in 2012, especially in the highly active northeast states. This paper describes U.S. microgrid demonstrations. Then it shows China’s effort to develop microgrids and compares the difference between U.S. and Chinese projects. Finally, based on U.S. experience, recommendations are provided for Chinese microgrids. [ABSTRACT FROM AUTHOR]

Soft open point (SOP) refers to a novel power electronic device installed in the distribution system to replace the traditional tie switch. The application of SOP will promote the flexibility and resilience of the distribution system in spatial scale. However, the energy storage system (ESS) is effective in energy transferring in time scale. SOP integrated with ESS (E-SOP) will form a new comprehensive energy conversion and storage device, which can significantly benefit the operation of active distributed network (ADN). First, a sequential optimization model of ADN with E-SOP is established, in which the model of E-SOP is developed fully considering the losses of converters and ESS. Then, the sequential optimization strategy of ADN with E-SOP is obtained. Finally, the modified IEEE 33-node test feeder is used to verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]