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Objective: Coronavirus disease 2019 (COVID‐19) outbreak is a major challenge all over the world, without acknowledged treatment. Intravenous immunoglobulin (IVIG) has been recommended to treat critical coronavirus disease 2019 (COVID‐19) patients in a few reviews, but the clinical study evidence on its efficacy in COVID‐19 patients was lacking. Methods: 325 patients with laboratory‐confirmed critical COVID‐19 were enrolled from 4 government‐designated COVID‐19 treatment centres in southern China from December 2019 to March 2020. The primary outcomes were 28‐ and 60‐day mortality, and the secondary outcomes were the total length of in‐hospital and the total duration of the disease. Subgroup analysis was carried out according to clinical classification of COVID‐19, IVIG dosage and timing. Results: In the enrolled 325 patients, 174 cases used IVIG and 151 cases did not. The 28‐day mortality was improved with IVIG after adjusting confounding in overall cohort (P = 0.0014), and the in‐hospital and the total duration of disease were longer in the IVIG group (P < 0.001). Subgroup analysis showed that only in patients with critical type, IVIG could significantly reduce the 28‐day mortality, decrease the inflammatory response and improve some organ functions (all P < 0.05); the application of IVIG in the early stage (admission ≤ 7 days) with a high dose (> 15 g per day) exhibited significant reduction in 60‐day mortality in the critical‐type patients. Conclusion: Early administration of IVIG with high dose improves the prognosis of critical‐type patients with COVID‐19. This study provides important information on clinical application of IVIG in the treatment of SARS‐CoV‐2 infection, including patient selection and administration dosage and timing. [ABSTRACT FROM AUTHOR]

Summary: In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users' privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently. [ABSTRACT FROM AUTHOR]

This paper proposes a novel management scheme, namely, the distribution system operator (DSO) as a key player participating in China's electricity markets owing to its high value-added capability, high customer loyalty and robust risk resistance. In light of this, this paper discusses the maximal marginal revenue for DSO in uncertain electricity markets with vehicle-to-grid operation incorporated. Initially, considering different benefit expectations of electric vehicles (EVs) owners, DSO could provide preferential contracts, including the discount price contract (DPC) and revenue-sharing contract (RSC), in exchange for the management rights of EVs. Then, the stochastic optimization model considering the spatiotemporal uncertainties of EVs and electricity markets is built up, and the optimal bidding and coordinating strategy are proposed to maximize the marginal revenue of DSO. Comprehensive case studies are conducted in a 31-node distribution system of a university campus under the electricity markets in Guangdong, China. Results reveal that different distribution of EVs parking lots has a significant impact on the profit of DSO, and the revenue for DSO is 13,477 yuan more than that for EVA by scheduling 1400 EVs' charging/discharging behavior properly. Additionally, the DSO with RSCs is exposed to 25% less market volatility risk than that with DPCs. • Comparing EV aggregator(EVA) and distribution system operator(DSO) characteristics. • Discount price contracts and revenue-sharing contracts are compared. • The advantages of DSO over EVA in market participation are analyzed. [ABSTRACT FROM AUTHOR]

Coordinated charging can utilize the properties of electric vehicles (EVs) to obtain various benefits. However, there are two major challenges, viz. the uncertainty of EV charging behaviors and the overlong solving time for the optimal solutions in the charging scheduling problem of large-scale EVs. It is almost infeasible to precisely predict the charging information of EVs due to the uncertainty of their mobility as transportation tools. In order to tackle this issue, the real-time charging scheduling method is employed in this paper. Even so, the computational complexity is crucially important in the real-time scheduling methods since the charging strategies of large-scale EVs must be acquired in a short time. Hence, a high efficient methodology is proposed for EV real-time scheduling based on the definition of capacity margin index and charging priority index. Finally, the simulation results show that the proposed scheduling method has a significant superiority over the uncoordinated charging in the regard of relieving the demand stress on the power system. Moreover, the complexity analysis demonstrates that the proposed method has near-linear complexity so that it can acquire the optimal real-time charging scheme in a rather shorter time than other methods. [ABSTRACT FROM AUTHOR]

With the rapid development of electric vehicles (EVs), the impacts on power grids arising from EV charging have drawn increasing attention worldwide. Uncoordinated charging of large-scale EVs will inevitably elevate load peaks at rush hours, therefore, poses serious challenges to the stability and security of power grid. Coordinated charging is expected to alleviate these negative impacts by utilizing the surplus power in the lower-demand hours with the help of the so-called valley filling algorithm. Nevertheless, when the amount of EVs involved reaches 1 million or above, the complexity of the scheduling method becomes a critical issue. In this paper, a very high efficient valley-filling strategy is proposed. Two key indexes, viz., capacity margin index and charging priority index are defined, the former one is used to select the target time slots on which the power grid has abundant surplus power for EV charging, and the latter one is used to determine the charging priority of EVs on each time slot. The simulation results demonstrate that the coordinated charging scheme with the proposed valley-filling strategy significantly outperforms the uncoordinated charging in the aspect of suppressing the elevated peak loads of power grid. Moreover, the complexity analysis shows that the proposed algorithm is much high-efficient than its existing counterparts. [ABSTRACT FROM AUTHOR]

Constitutive overactivation of TGFβ signaling is a common event in human cancer progression and acts as a major inducer of epithelial-mesenchymal transition (EMT). In pre-metastatic colorectal cancer (CRC) cells, however, this cascade is tightly controlled and the underlying mechanism in TGFβ stimulated hyperactivation of downstream Smad pathway remains elusive. In this study, expression of miR-187 was downregulated in colorectal cancer (CRC) compared with adjacent normal tissues. miR-187 could suppress the formation of aggressive phenotype in CRC and inactivate Smad pathway, thus preventing EMT. TGFβ stimulation significantly suppressed the expression of miR-187, and overexpressed miR-187 counteracted the influence of TGFβ on cell phenotype and downstream pathway. Furthermore, we found that miR-187 directly suppressed the expression of SOX4, NT5E and PTK6, which were identified as essential upstream effectors of Smad pathway. Together with the fact that high SOX4 or NT5E levels were associated with poor prognosis, we also demonstrated that downregulation of miR-187 was closely related to tumor metastasis and poor prognosis in CRC. These findings revealed a plausible mechanism for sustained TGFβ activation in cancer progression and might have suggested a novel miR-187-based clinical intervention target for patients with advanced CRC. [ABSTRACT FROM AUTHOR]

Highlights: [•] A scenario of vehicle-to-grid implementation within regional smart grid is discussed and mathematically formulated. [•] A double-layer optimal charging strategy for plug-in electric vehicles is proposed. [•] The proposed double-layer optimal charging algorithm aims to minimize power grid’s load variance. [•] The performance of proposed double-layer optimal charging algorithm is evaluated through comparative study. [Copyright &y& Elsevier]

• A scalable and reconfigurable hybrid microgrid clustering architecture is proposed. • The proposed ENU features multiplexed structure and flexible functionalities. • The ENUs could realize mutual AC/DC interconnection by same converter modules. • The autonomous power coordination within and between microgrids is achieved. • The utilization and local consumption of RESs with system reliability is improved. Microgrid clusters are effective to increase utilization of renewable energy resources (RESs), and improve reliability and stability of power systems. Facilitating flexible configurations, grid networking and coordinated operation among multiple microgrids are essential for the microgrid cluster. In view of this, this paper presents a scalable and reconfigurable hybrid microgrid clustering architecture and a corresponding decentralized control method. Initially, a new interlinking converter referred as energy networking unit (ENU) is proposed. The ENU is used to interface the AC and DC subgrid in single hybrid microgrid, and also facilitates the connection with the external power grid. Then the ENU-based hybrid microgrid clustering architecture is established, which is characterized by scalability, reconfigurability and modularity. Thanks to the reconfigurable topology of ENUs, the architecture could realize flexible AC/DC interconnection between microgrids by the same converter modules with less power conversion units. Finally, a proposed decentralized control strategy for this architecture could realize autonomous power exchange between subgrids in single microgrid with considering energy storage restrictions and smooth mode switch. And an adaptive and autonomous power interaction among neighboring microgrids is also achieved with full utilization of the compensation capacity of clusters, thus improving the system reliability, as well as the utilization and local consumption of RESs. The effectiveness of the proposed clustering architecture and decentralized control strategy is verified by comprehensive cases. [ABSTRACT FROM AUTHOR]

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy. • A pseudo-local reserve market is established considering EV's reserve potential. • Optimal bidding strategy for DSO in China's electricity markets is constructed. • A framework for EV coordinated V2G operation is developed. • The proposed method can limit load deviation cost for more profits. [ABSTRACT FROM AUTHOR]

• The day-ahead bidding strategy of electric vehicle (EV) aggregator is explored. • Two typical agent modes between EV users and EV aggregator are introduced. • The market uncertainty is handled by analyzing potential market clearing scenarios. • The Guangdong electricity market is first investigated for case study. The electric vehicle (EV) aggregator is a new entity that coordinates the charging of an EV fleet to exploit the business opportunities in the electricity market. This paper explores the optimal bidding strategy for the EV aggregator participating in day-ahead markets. Two typical agent modes between the EV aggregator and the EV user are introduced based on the reality need of the EV user, viz. the centralized protocol management mode (CPMM) and the decentralized demand response mode (DDRM). After that, the stochastic optimization model is developed to maximize the expected profits of EV aggregator. The uncertainties from the electricity market and the EV charging are incorporated into the stochastic optimization model. The Guangdong electricity market, as the pioneer of China's power reform, is first investigated as the target market to verify the rationality of the proposed approach. The possible real-time clearing scenarios are extracted from the historical market clearing data using the multi-class support vector machine (MSVM). Results reveal that the EV aggregator can make an optimal bidding strategy for the potential clearing scenarios when multiple agent modes are involved. Moreover, the comparison of two agent modes indicates that the EVs with the CPMM are more sensitive to the electricity price than those with the DDRM. [ABSTRACT FROM AUTHOR]

• An V2G scheme concerning on edge computing is proposed. • The architecture of scheme is scalable, reliable and information secure. • The algorithm of scheme aims at PV self-consumption and load flattening. • The proposed scheme provides very high computational performance. • Case studies are verified in terms of the short-time scale and the long-time scale. Plug-in electric vehicles (PEVs) and renewable energy sources (RESs) can relief the stress on air pollution. Particularly, using RES for PEV energy requirement can integrate more RESs on the grid. In this paper, a vehicle-to-grid (V2G) scheme concerning on RES and edge computing, i.e. the internet of smart charging points with photovoltaics integration, is presented. Within the architecture of the scheme, each charging point equips computing and storage units, so as to store PEV sensitive information locally and conduct "burn after scheduling". Besides, this architecture can transform the traditional large-scale V2G problem into several sub-problems, which are small enough to optimize. Based on the architecture of the scheme, an associated high-efficiency algorithm is designed. Six typical scenarios of PEV charging are elaborated and two indexes are presented to facilitate 1) the self-consumption of photovoltaics energy by PEV charging and 2) the peak-shaving and valley-filling of net load. Additionally, voltage regulation and real-time control are applied to ensure the security of the distribution grid and mitigate the uncertain conditions. Finally, compared with uncoordinated charging, the short-time scale simulation realizes the peak-shaving and valley-filling by 17.54% and 12.42%, respectively; and the amount of self-consumption of photovoltaics energy increases by 258.74%. Furthermore, the long-time scale simulations also present a satisfying performance for the grid energy saving and the load factor. Particularly, the proposed scheme offers high computational efficiency compared with different architecture and algorithm, and the execution time for scheduling one PEV at one-time interval shows a microsecond basis. [ABSTRACT FROM AUTHOR]

With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods. [ABSTRACT FROM AUTHOR]

The vehicle-to-grid (V2G) technology is an effective and economic solution to enable the integration of electric vehicles (EVs) into power grids. As an effort to present the state of the art in relevant fields, the power interaction mode between EVs and power grids, and the scheduling methodology for the V2G implementation are overviewed comprehensively in this paper. To be specific, technical requirements, grid impacts and battery degradations regarding each power interaction mode are discussed; the operating processes of centralized and decentralized scheduling approaches are introduced and compared. More importantly, it is noted that few attentions are given to the solving algorithms of programming models used for optimizing the scheduling strategy of EVs and few works examine their feasibilities to acquire the optimal charging strategies especially for a large number of EVs. In this context, this paper also illuminates the mathematical foundation on optimization techniques for the optimal V2G strategy, and provides new insights for evaluating these models. • A review in the aspect of the EV-grid integration is made including power interaction mode and mainstream scheduling method. • Mathematical foundation on optimization techniques for the V2G strategy is given. • New insights are provided to make an overall evaluation on the existing methods. [ABSTRACT FROM AUTHOR]

• An easy-to-implement hybrid microgrid reconstruction scheme is proposed for old residential communities. • The existing transformers and power lines could be reused in the proposed hybrid microgrid architecture. • A significant maximum 297% of EV penetration could be obtained with the proposed architecture and operation strategy. • Real-time voltage regulation, desirable operation efficiency, and improved load ratio are to be implemented. The widespread popularity of electric vehicles (EVs) imposes great challenges for distribution grids, such as feeder congestion, voltage deviation, and so forth. Although building new electrical facilities is feasible to release more capacity for EV integration, this is not a wise scheme to be applied in old residential communities owing to huge costs and limited spaces. In light of this, the paper proposes an easy-to-implement and cost-effective hybrid AC/DC microgrid reconstruction scheme for low-voltage distribution grids of old residential communities. The advanced power electronic device called smart interlinking unit (SIU) is incorporated in prior power distribution rooms of residential communities, featuring integrated structure, dedicated interfaces, and flexible control capability. The SIU-based architecture reuses existing redundant transformers and power lines to form AC/DC subgrids. An outstanding transfer capacity improvement and power loss reduction via the DC renovation are quantified. Considering the varied efficiency characteristics of multiple power converters and transformers, a hierarchical coordinated operation strategy is proposed for this architecture to realize the overall energy-saving operation. The optimal operation point tracking and AC/DC voltage regulation are included in the strategy design. Regarding the target scenario in the case study, the adopted reconstruction scheme would achieve a significant 297% of the maximum EV penetration. The real-time voltage regulation for AC and DC feeders is also realized to avoid possible voltage violations. Additionally, the proposed architecture and operation strategy could obtain about 10% of load ratio improvement and 1.5% of average efficiency promotion compared to the traditional AC architecture. [ABSTRACT FROM AUTHOR]

The Wnt/β-catenin pathway is known to contribute to colorectal cancer (CRC) progression, although little is known about the contribution of β-catenin on this process. We investigated the role of miR-490-3p, which was recently reported to suppress tumorigenesis through its effect on Wnt/β-catenin signaling. We found that hypermethylation of the miR-490-3p promoter down-regulates miR-490-3p expression in CRC tissue. Gain- and loss-of-function assays in vitro and in vivo reveal that miR-490-3p suppresses cancer cell proliferation by inducing apoptosis and inhibits cell invasiveness by repressing the initiation of epithelial-to-mesenchymal transition (EMT), a key mechanism in cancer cell invasiveness and metastasis. The frequently rearranged in advanced T-cell lymphomas (FRAT1) protein was identified as a direct target of miR-490-3p and contributes to its tumor-suppressing effects. miR-490-3p appears to have an inhibitory effect on β-catenin expression in nuclear fractions of CRC cells, whereas FRAT1 expression is associated with the accumulation of β-catenin in the nucleus of cells, which could be weakened by transfection with miR-490-3p. Our findings suggest that the miR-490-3p/FRAT1/β-catenin axis is important in CRC progression and provides new insight into the molecular mechanisms underlying CRC. They may help to confirm the pathway driving CRC aggressiveness and serve for the development of a novel miRNA-targeting anticancer therapy. [ABSTRACT FROM AUTHOR]

The flux-switching permanent-magnet (FSPM) machine attracts increasing attention recently due to its high power density, robust mechanical structure, good flux-weakening capability, and essential sinusoidal back-electromotive-force waveforms. In previous studies, the approach for analyzing FSPM machines is either by using the lumped parameter magnetic circuit models or from the “generator-oriented” perspective. In this paper, the operating principle of FSPM machines is reiterated from a new perspective, viz., the “motor-oriented” perspective. Some interesting findings and essential principles can be unveiled, which include how the stable electromagnetic torque can be developed, how the pole-pair number (PPN) of armature windings, the PPN of PMs, and the synchronous speed of the armature field should be defined, and how to determine the connection of coils for the sake of developing stable electromagnetic torques. This new perspective is more consistent with the classical theory on electric machines. There is no need to consider the polarity of coils when determining the connection of windings. Three typical FSPM machines with different combinations of stator and rotor poles, viz., 12/11-pole, 12/13-pole, and 12/26-pole, are investigated to verify the validity of the proposed analysis approach. Experimental verification concerning the latter two sample machines is also conducted. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs) are believed as efficient solutions to reduce carbon emissions and fossil fuel reliance in transportation sectors. Yet, the ever-increasing penetration of EVs also poses great challenges for distribution grid planning and operation. As a research hotspot, the vehicle-to-grid (V2G) technology could not only relieve the adverse effects of large-scale uncoordinated EV charging but also offer varied auxiliary services for the utility grid via the proper charging/discharging schedule. In this paper, a comprehensive review of distribution grid architectures, grid connection infrastructures and standards, and typical applications is conducted from the perspective of EV-grid integration and V2G operation for the first time. The nanogrid, microgrid, and cluster architectures that are critical components in active distribution grids are all involved in the discussion. Considering EV's triple roles played in grid interaction, the corresponding infrastructure and grid-connection standards, as well as bidirectional/unidirectional charger topologies, are overviewed. Then, four-type nanogrid and microgrid architectures are reviewed and fully evaluated in six aspects, viz., charging demand compatibility, power quality severity, V2G availability, architecture scalability, local control complexity, and technology maturity. The assessment result reveals that the hybrid AC/DC-coupled architecture based on advanced interlinking converters has flexible power flow control and power quality compensation capability, thus facilitating the EV-grid integration and V2G operation effectively. The assessment for three-type cluster architectures indicates that the hybrid parallel-series microgrid cluster has balanced performance in complexity, cost, redundancy, and reliability. Finally, the typical applications of the nanogrid, microgrid, and cluster with EVs integration are exemplified and discussed. • The emerging DC-powered converter topologies to interface EVs are identified and discussed. • Extensive nanogrid, microgrid, and cluster are contained in the architecture discussion. • Extensive six indexes are developed to evaluate four-type nanogrid and microgrid architectures. • Comprehensive assessment of varied power architectures is completed in view of the EV-grid integration and V2G operation. [ABSTRACT FROM AUTHOR]

• The so-called pseudo hierarchical energy management architecture built upon the smart EV charging point is firstly introduced. • The proposed energy management architecture is a compact, cost-effective, and easy-to-build scheme for nanogrid. • The introduced nanogrid operation strategy enables autonomous power coordination with V2G incorporated and efficient power dispatching simultaneously. • Comprehensive nanogrid operation scenarios and modes are analyzed via system simulation and hardware-in-loop experiment. Most recently, DC nanogrid incorporating effective energy management has attracted widespread attention. Due to its favorability to integrate renewable energy sources and emerging power electronic loads, such as photovoltaics and electric vehicles (EVs), DC nanogrid is believed to be able to improve the energy utilization efficiency and mitigate the carbon footprint in the coming decades. Towards a compact, cost-effective, and easy-to-build energy management scheme for nanogrid, this paper presents a pseudo hierarchical management architecture built upon the smart charging point. The proposed architecture incorporates the upper-level central controller with the local power role creatively and comprises two timescale management levels with corresponding operation strategies. In the short-timescale local management level, a state-triggered droop strategy based on the decentralized control mechanism is introduced to realize the autonomous power coordination without extensive communication links. The autonomous vehicle-to-grid (V2G) operation is also implemented with providing real-time power balance capability to unpredicted and short-timescale load variation in peak periods. In the power dispatching level, a multi-mode power dispatching strategy involving six operation modes is introduced to realize the efficient power scheduling for the nanogrid. The effectiveness of the proposed architecture and operation strategy is verified in the detailed simulation model and hardware-in-loop experiment platform. The results show that the real-time, autonomous, and stabilized power coordination in nanogrid could be realized along with a self-regulated V2G operation. Additionally, the peak-shaving and valley-filling of load curve, the satisfaction of EV charging demand, and an improved operation economy are achieved under the proposed architecture and operation strategy. [ABSTRACT FROM AUTHOR]

• A cyber-physical co-modeling is developed to fulfill the essential insights of ISCP. • A hierarchical distributed V2G coordination apt to parallel computing is proposed. • The cyber topology for ISCP is fully discussed in terms of communication efficiency. Vehicle-to-grid (V2G) technology plays an important part in achieving carbon neutrality. Hence, reducing the execution time under the real-time application becomes an urgent issue. In this paper, we develop a cyber-physical co-modeling to fulfill the fundamental insights into the internet of smart charging points (ISCP), wherein the local controllers are designed near the plug-in electric vehicles (PEVs), and are coordinated with each other. In perspective of energy dispatching, a hierarchical V2G scheduling is implemented in a distributed way to decompose the optimization problem into several sub-problems. Besides, the parallel computing is applied in the V2G problem to accelerate the speed of obtaining results. Moreover, the voltage regulation is applied near the energy coordinator with high-performance computer rather than by the local controller. In perspective of network communication, the small-world network is applied to ensure the communication efficiency and decrease the wiring costs. Besides, the privacy-preserving of both the energy coordinator and the PEV users is guaranteed by processing and storing the sensitive information of the two participants nearby. Finally, the cyber-physical co-modeling is performed in Matlab and Network Simulator 2. Results show load flatting, self-consumption of photovoltaic output, voltage regulation, and up/down regulation are achieved. Moreover, the delay of small-world network is 90.94 times faster than that of lattice network, and the cost of small-world network is nearly 500 times less than that of full mesh network. Particularly, the execution time for V2G operation at one-time interval is less than 1 s. [ABSTRACT FROM AUTHOR]

This paper presents a novel transverse flux permanent magnet disk generator (TFPMDG) for wind power generation. The main features of its structure are the modular H-shaped stator cores and two simple rotor disks. What is different from the structures introduced in the references is that each H-shaped stator core is formed by two T-shaped iron cores and a permanent magnet (PM) rather than a complete H-shaped core, which makes the manufacturing simpler and easier. Each rotor disk consists of a rotor holder and several rotor bars, resulting in high robustness and reliability. Moreover, two circular coils in the H-shaped stator cores together with the stator disk are sandwiched by the two rotor disks, which improves the utilization of PMs. In this paper, the proposed TFPMDG is investigated in detail. Firstly, the structure and operating principle are introduced. Then, the magnetic circuit method is used to analyze the TFPMDG. Next, the three-dimensional (3D) finite element method (FEM) is employed to compute the magnetic field distribution and EMF at no load. According to the calculation result, the other three TFPMDGs with different shapes of rotor cores are proposed and analyzed for better back EMF, and then a generator with good performance is selected for load analysis. Finally, a prototype is fabricated and tested, and the simulated results are compared with the measured ones, which proves the rationality of the simulated results. [ABSTRACT FROM AUTHOR]