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Oxalicine B (1) is an α -pyrone meroterpenoid with a unique bispirocyclic ring system derived from Penicillium oxalicum. The biosynthetic pathway of 15-deoxyoxalicine B (4) was preliminarily reported in Penicillium canescens , however, the genetic base and biochemical characterization of tailoring reactions for oxalicine B (1) has remained enigmatic. In this study, we characterized three oxygenases from the metabolic pathway of oxalicine B (1), including a cytochrome P450 hydroxylase OxaL, a hydroxylating Fe(II)/ α -KG-dependent dioxygenase OxaK, and a multifunctional cytochrome P450 OxaB. Intriguingly, OxaK can catalyze various multicyclic intermediates or shunt products of oxalicines with impressive substrate promiscuity. OxaB was further proven via biochemical assays to have the ability to convert 15-hydroxdecaturin A (3) to 1 with a spiro-lactone core skeleton through oxidative rearrangement. We also solved the mystery of OxaL that controls C-15 hydroxylation. Chemical investigation of the wild-type strain and deletants enabled us to identify 10 metabolites including three new compounds, and the isolated compounds displayed potent anti-influenza A virus bioactivities exhibiting IC 50 values in the range of 4.0−19.9 μmol/L. Our studies have allowed us to propose a late-stage biosynthetic pathway for oxalicine B (1) and create downstream derivatizations of oxalicines by employing enzymatic strategies. The late-stage biosynthetic pathway of meroterpenoid oxalicine B was proposed, and three oxygenases, including a cytochrome P450 hydroxylase OxaL, a hydroxylating Fe(II)/ α -KG-dependent dioxygenase OxaK, and a multifunctional cytochrome P450 monooxygenase OxaB, were biochemically characterized. [Display omitted] [ABSTRACT FROM AUTHOR]

• The similarities and differences of inertia response between SG and DFIG are analyzed and compared. • An analytical model of DFIG with inertia control is developed for studying frequency dynamics considering aerodynamics and rotor side power of wind turbine. • The virtual inertia function is derived accounting for the differences between SG and DFIG, and the key factors affecting virtual inertia are analyzed. • An optimal design approach of virtual inertia is constructed and solved for improving system frequency trajectory. • The effectiveness and advantages of proposed model are verified through a generic two machine power system and modified IEEE-9 bus test system. Modern power systems are gradually evolving into low-inertia systems due to the integration of high penetration of renewable energy and power electronic equipment. For the natural inertia on the rotor, doubly-fed induction generator (DFIG) based wind farms are expected to provide inertia support for systems. The paper proposed an analytical model of DFIG with df / dt inertia control for studying frequency dynamics, and investigates the optimal design of virtual inertia from wind farms, as well as its equivalent inertia contribution to system. The inertia response differences between DFIG and synchronous generator (SG) are discussed and analyzed for the first time, and then to accurately describe the frequency response with low cost, an analytical model of DFIG with inertia control is developed considering wind turbine aerodynamic characteristics. The explicit expression of virtual inertia provided by DFIG is derived according to the kinetic energy equilibrium, and the key factors that dominate the magnitude of virtual inertia are illustrated. Based on the system frequency response model including wind farms, taking the system frequency nadir (SFN) and the time of reaching nadir (ToN) as targets, a multi-objective optimal configuration model of virtual inertia is constructed and solved through MOPSO. The time domain simulations of a generic two machine four bus system and modified IEEE 9-bus test system are both performed in MATLAB/Simulink, the accuracy and efficiency of our proposed DFIG model on system frequency dynamics research are demonstrated, the pareto frontiers of virtual inertia configuration under different working conditions are optimized and analyzed as well as the impacts of different parameters on inertia evaluation. [ABSTRACT FROM AUTHOR]

• A novel hybrid model and data-driven voltage stability online assessment approach is proposed. • The parameters in the model-driven method are modified based on the response data. • To improve accuracy, impedance difference is included in the data-driven input data. • The proposed method combines the clear mechanism of the model-driven and high accuracy of the data-driven. With the continuously increasing integration of renewable energy sources into power grids, the dynamic response of a power system is becoming more complex. For example, the interaction between the dynamic loads and low-voltage ride-through of renewable energy generators makes the voltage response more rapid and unpredictable. Ensuring the accuracy and speed of traditional voltage stability assessment methods is difficult. This study developed a novel hybrid model and data-driven voltage stability assessment approach. First, the equivalent parameters of a power system were calculated based on the measured data, and the parameters were constantly modified based on the response data. To further improve the accuracy of the approach, a data-driven method was introduced to correct the assessment results using a Thevenin equivalent-based assessment. The difference between the Thevenin and system impedances, which better reflects the system stability, was included in the data-driven input data. Finally, by combining the clear physical mechanism of the model-driven method and high accuracy of the data-driven method, the final the assessment process was a serial combination of the model- and data-driven methods. The effectiveness of the method was verified using an IEEE New England 10-generator 39-bus test system and a 100-bus actual system in China. The results showed that the method developed was more accurate and had higher robustness under data loss and noise conditions than other methods. [ABSTRACT FROM AUTHOR]

• Build a framework for the identification and localization of frequency oscillations. • Investigation of the PFR oscillation mechanism under environmental excitation. • Combined Morlet Wavelet Filtering and SDMD for parameter identification. • Proposed dissipation area difference ratio index to localize oscillatory sources. Frequency oscillation results in all units within the system oscillating in the same phase and persistently, leading to significant damage to the system. It is important to remove the threat of frequency oscillation. However, existing methods rely on extracting and localizing oscillation parameters from transient oscillation data, failing to effectively identify potential oscillation risks within the system for proper corrective control. To fill this gap, this paper proposes a method for identifying and localizing frequency oscillations within random environmental data. Firstly, signal processing employs Morlet wavelet filtering to extract electrical quantity data in a single mode. Subsequently, a subspace dynamic mode decomposition algorithm is applied for parameter identification, enabling the detection and warning of weakly damped modes. In order to accurately locate the source of oscillations, a locating index is improved based on the energy flow theory, known as the dissipation area difference ratio, which enables the online evaluation of dissipation energy variation in generators within power systems operating under different modes. Through an arithmetic analysis conducted on both a 16-machine 5-zone system and an actual grid system, the effectiveness and accuracy of the proposed identification and localization method are demonstrated. [ABSTRACT FROM AUTHOR]

Ultralow-frequency oscillations (ULFOs) have frequently occurred in hydro-dominant power systems in recent years. These phenomena seriously threaten the safe and stable operation of power systems. Therefore, measures that can effectively suppress ULFO need to be taken. Currently, the hydropower unit governor can enhance the dynamic stability and dampening oscillations of a system through adjusting the proportional–integral–derivative parameters. However, modifying these parameters can affect the primary frequency regulation performance, which compromises the assurance of grid frequency quality. This study proposes a switching type of hydro generator governor based on the local damping contribution rate to address the aforementioned problems. The approach incorporates the energy structure into the classical governor model, calculates the local damping contribution rate, and enables automatic switching of the hydro generator governor parameters based on positive and negative local damping contributions. This way allows for segmented control with different parameter settings, which enables the system to effectively consider the damping characteristics of ULFO while maintaining primary frequency regulation performance. It can also accurately and efficiently suppress the ULFO. The proposed method is verified through arithmetic analysis conducted on a 16-machine 5-zone system and an actual grid system. Simulation results demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

• A robust data-driven predictive load frequency control (LFC) scheme for system with rich wind generation is proposed. • A data-driven LFC scheme is accomplished through learning recursively from the input–output trajectory by employing matrix zonotopes. • The process uncertainties is well considered in generating a robustly optimal control law, and the robustness to the uncertainties caused by renewables could be guaranteed. • The performance of the proposed LFC scheme is verified through comparison with the model-based predictive control scheme, AI-based controller and traditional PI controller. The uncertainties regarding the generations and key electromechanical parameters due to the high penetration of renewable energy bring significant challenges to frequency control. To address this problem, a robust data-driven predictive load frequency control (LFC) scheme for system with rich wind generation is proposed in this paper. To weaken the effects of the uncertainty of the key electromechanical parameters, the data-driven LFC scheme is accomplished through learning recursively from the input–output trajectory by employing matrix zonotopes, instead of accurate predictive model. Since the process uncertainties is well considered in generating a robustly optimal control law, the robustness to the uncertainties caused by renewables could be guaranteed in proposed methodology at each time step. The performance of the proposed LFC scheme is verified through comparison with the model-based predictive control scheme, AI-based controller and traditional PI controller. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy into power systems and expansion of interconnected power grid frequently and uncontrollably result in low-frequency oscillation. Low-frequency oscillation is potentially dangerous for power systems. However, acquiring dominated oscillation paths and generator damping properties are quite helpful for system operators when taking appropriate measures to suppress these oscillations. In this study, a method for the online tracking of dominated oscillation paths and generator damping contribution during low-frequency oscillations is proposed on the basis of the damping loss factor (DLF) and two improved forms of power system energy structure (PSES). DLF in elasticity mechanics is introduced to quantitatively establish the relationship between dissipation energy and damping ratio for each mode. On the basis of the PSES framework, the dissipation energy composition during low-frequency oscillation is analyzed. Then, based on the dominance of the aperiodic component in the dissipation energy, two improved forms of PSES are derived with different distributions of dissipation energy. Subsequently, based on the DLF and improved forms of PSES, two dissipation energy-based indexes are proposed to realize the online tracking of dominated oscillation paths and evaluate the generator damping contribution (including renewable energy generators) during low-frequency oscillations. Finally, the accuracy of the proposed method is verified by simulating the “16-machine, 5-area” system as a practical power system in China. [ABSTRACT FROM AUTHOR]

• A general model of regulating characteristics for Energy-intensive load is given. • A multi-objective scheduling approach is developed for wind utilization. • The scheduling benefits both power system and Energy-intensive load. • The production demand of load are taken as the most precondition of regulation. • Cases study of multi-scenario and multi-mode in regulating. Energy-intensive load (EIL) has excellent regulating potential in the utilization of wind power: it not only offers high flexibility and large power capacity but also features significant price sensitivity. Without considering the production tasks and the regulating characteristic of energy-intensive load, the current scheduling approach is difficult to implement. A novel multi-objective coordinative scheduling approach is proposed in this paper for wind-power utilization considering the regulating models of energy-intensive load to guarantee the benefits of both power system and energy-intensive load, which can greatly improve the willingness of energy-intensive load to participate in scheduling. Firstly, A general model of regulating characteristics for energy-intensive load is given based on production process. Next, based on this modeling, a coordinative scheduling model is established with three layer. Layer A determines the total tasks of load side and generating side with the goal of minimizing the wind curtailment. Layer B and layer C make decomposition optimization to thermal power units and load control units with the goal of minimizing the generation cost and load-adjustable cost, respectively. Finally, a case study to verify the modeling is presented. The study shows that the proposed method can significantly improve the utilization of wind power at lower cost on the basis of ensuring the production requirements and benefits of energy-intensive load. [ABSTRACT FROM AUTHOR]

Abstract: In this work, we focus on the design and implementation of a robust flight control system for an unmanned helicopter. A comprehensive nonlinear model for an unmanned helicopter system, which is built by our research team at the National University of Singapore, is first presented. A three-layer control architecture is then adopted to construct an automatic flight control system for the aircraft, which includes (1) an inner-loop controller designed using the H ∞ control technique to internally stabilize the aircraft and at the same time yield good robustness properties with respect to external disturbances, (2) a nonlinear outer-loop controller to effectively control the helicopter position and yaw angle in the overall flight envelope, and lastly, (3) a flight-scheduling layer for coordinating flight missions. Design specifications for military rotorcraft set for the US army aviation are utilized throughout the whole process to guarantee a top level performance. The result of actual flight tests shows our design is very successful. The unmanned helicopter system is capable of achieving the desired performance in accordance with the military standard under examination. [Copyright &y& Elsevier]

Abstract: We present in the paper the design of a hardware-in-the-loop simulation framework and its actual implementation on our custom constructed unmanned-aerial-vehicle (UAV) helicopter systems. Real-time hardware-in-the-loop simulation is one of the most effective methods for the verification of the overall control performance and safety of the UAVs before conducting actual flight tests. In our proposed framework, four modules, which include onboard hardware, flight control, ground station and software, are integrated together to realize the hardware-in-the-loop simulation. This design is successfully utilized for simulating several flight tests including basic flight motions, full-envelope flight and multiple UAV formation flight. Results obtained show that the constructed hardware-in-the-loop simulation system is highly effective and useful. [Copyright &y& Elsevier]

Abstract: In this paper, we present the design and implementation of an autonomous flight control law for a small-scale unmanned aerial vehicle (UAV) helicopter. The approach is decentralized in nature by incorporating a newly developed nonlinear control technique, namely the composite nonlinear feedback control, together with dynamic inversion. The overall control law consists of three hierarchical layers, namely, the kernel control, command generator and flight scheduling, and is implemented and verified in flight tests on the actual UAV helicopter. The flight test results demonstrate that the UAV helicopter is capable of carrying out complicated flight missions autonomously. [Copyright &y& Elsevier]

Abstract: In this paper, we present a comprehensive design methodology for constructing small-scale UAV helicopters. The systematic design procedure, which includes hardware component selection, design and integration, as well as experimental evaluation, is utilized to construct a fully functional UAV helicopter, named SheLion. Various ground and flight tests have been performed to verify the feasibility and reliability of SheLion. This simple, systematic and effective methodology can be easily followed and used for building small-scale UAV helicopters for general research purposes. [Copyright &y& Elsevier]

The clean transformation of building energy consumption is a critical pathway for achieving deep decarbonization. In light of the diversity of building energy needs, this paper proposes an integrated zero-carbon energy supply building architecture that involves the coupling of electrical, hydrogen, heat, and cooling energy sources. To address challenges in real-time supply-demand balancing and reliability of supply under extreme or boundary conditions due to complex energy couplings, the paper introduces an energy management framework with two control layers comprising mixed-integer linear programming (MILP) and model predictive control (MPC). This framework conducts hierarchical optimization to synergize economic and technical objectives. At the economic optimization layer, optimal economic parameters are formulated based on MILP; while at the MPC optimization layer, these parameters serve as reference states. By incorporating state relaxation method, the framework accommodates the uncertainties associated with system operation. Simulation analyses of typical weeks during summer and winter seasons demonstrate that the energy optimization management approach proposed in this paper can achieve highly reliable and economically stable operation of the building energy supply system. • A zero-carbon energy supply building energy structure is constructed. • Dynamic evolution model of electricity-hydrogen-heat-cooling multi-energy coupling system is established. • A double-layer energy management method including economic optimization and MPC optimization is proposed. [ABSTRACT FROM AUTHOR]

• The dynamic interaction mechanism between the grid-tied VSCs and the AC power grid in the electromechanical bandwidth is revealed. • A local measurement signal selection strategy for monitoring inter-area oscillations is proposed. • A dominant inter-area oscillation mode extraction method based on modal energy is presented. • The excellent effectiveness and adaptability performance of the local measurement signal selection strategy are verified by using IEEE 16-generator 5-area test system. Appropriate phasor measurement unit (PMU) signals must be selected to monitor electromechanical oscillations for large-scale renewable energy connected power system. Considering the response mechanism of the active and reactive power of grid-tied voltage source converters (VSCs) in the electromechanical time scale, a local measurement signal selection strategy for monitoring inter-area oscillations (0.1–0.7 Hz) based on the inherent electromechanical characteristics of the synchronous generators (SGs) is proposed. Moreover, optimized dynamic mode decomposition (OpDMD) algorithm is used to extract dominant modal parameters (e.g., oscillation frequency, damping ratio, and mode shape). First, from the perspective of frequency response characteristics, the dynamic interaction mechanism between grid-tied VSCs and the AC grid is revealed in the electromechanical time scale. Second, considering the intrinsic relationship between the inherent inertia constant of the SG and observability of response after a disturbance, a local measurement signal selection strategy for monitoring the inter-area electromechanical oscillation of power systems with high grid-tied VSCs penetration is proposed. On the basis of the selected local measurement signals, the OpDMD algorithm identifies dominant inter-area oscillation parameters. Results of the IEEE 16-generator 5-area system show that the proposed method can accurately extract electromechanical modal parameters from the limited PMU measurements, verifying the effectiveness of the local measurement signal selection strategy for inter-area oscillation monitoring of power systems with high grid-tied VSCs penetration. [ABSTRACT FROM AUTHOR]

With the increasing proportion of renewable energy (mainly wind power and photovoltaic) connected to the grid, the fluctuation of renewable energy power brings great challenges to the safe and reliable operation of power grid. As a clean, low-carbon secondary energy, hydrogen energy is applied in renewable energy (mainly wind power and photovoltaic) grid-connected power smoothing, which opens up a new way of coupling hydrogen storage energy with renewable energy. This paper focuses on the optimization of capacity of electrolyzers and fuel cells and the analysis of system economy in the process of power output smoothing of wind/photovoltaic coupled hydrogen energy grid-connected system. Based on the complementary characteristics of particle swarm optimization (PSO) and chemical reaction optimization algorithm (CROA), a particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) are proposed. Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are constrained by wind power fluctuation, and considering environmental benefits, government subsidies and time value of funds, the objective function and its constraints are established. According to the simulation analysis, by comparing the calculated results with PSO and CROA, it shows that PSO-CROA effectively evaluates the economy of the system, and optimizes the optimal capacity of the electrolyzers and fuel cells. The conclusion of this paper is of great significance for the application of hydrogen energy storage in the evaluation of power smoothness and economy of renewable energy grid connection and the calculation of economic allocation of hydrogen energy storage capacity. • The particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) is proposed. • Based on PSO-CROA, the techno-economic of coupled renewable energy and hydrogen system is analyzed. • Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are optimized. • PSO-CROA effectively evaluates the economy of system and the optimal capacity of the electrolyzers and fuel cells. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) should be designed appropriately with an adequate combination of different renewable sources and various energy storage methods to overcome the problem of intermittency of renewable energy resources. Focusing on the inevitable impact on the grid caused by strong randomicity and apparent intermittency of photovoltaic (PV) generation system, modeling and control strategy of pure green and grid-friendly hybrid power generation system based on hydrogen energy storage and supercapacitor (SC) is proposed in this paper. Aiming at smoothing grid-connected power fluctuations of PV and meeting load demand, the alkaline electrolyzer (AE) and proton exchange membrane fuel cell (PEMFC) and SC are connected to DC bus of photovoltaic grid-connected generation system. Through coordinated control and power management of PV, AE, PEMFC and SC, hybrid power generation system friendliness and active grid-connection are realized. The validity and correctness of modeling and control strategies referred in this paper are verified through simulation results based on PSCAD/EMTDC software platform. • The AE, PEMFC and SC are integrated to DC bus of PV grid-connection system. • A model and control strategy of hybrid power generation system is proposed. • Friendly and active grid-connection of the hybrid system is realized. • Validity of hybrid system is verified by simulation based on PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

This study proposes an algorithm based on adaptive local iterative filtering decomposition (ALIFD), which is applicable for the feature extraction of a power oscillating signal in a power system. The ALIFD algorithm uses the Fokker–Planck equation to construct the filter function as well as filter sifting to obtain the intrinsic mode function (IMF) with stable features. This algorithm has a solid mathematical foundation and can effectively avoid the mode-mixing problems in the empirical mode decomposition (EMD) algorithm. In this study, the ALIFD algorithm is initially used to obtain the oscillation component. Subsequently, Hilbert Transformation (HT) of each component is performed, and oscillation characteristic parameters are extracted. Analysis results of the test signal, the simulation signal, and the measured data verify the effectiveness of the proposed algorithm. Meanwhile, the comparative results of the EMD algorithm prove that the proposed method is highly adaptive to extracting the characteristics of power oscillation in a power system. [ABSTRACT FROM AUTHOR]

• Givens rotation is employed to enhance the computational efficiency of Rsub-DMD while ensuring the extraction accuracy. • The Rsub-DMD based participation factor extraction method is ambient data-oriented, which releases the requirement of system model. • The proposed PF can effectively quantify the participation degree of generation output in an electromechanical mode. • A novel generation rescheduling strategy that relies solely on ambient data for interconnected power system was proposed. Participation factor (PF) is widely used in assisting the selection of critical rescheduled generators in re-dispatching issues. The conventional PF relies heavily on the detailed system model and is commonly limited to quantifying the participation degree of system state variables in electromechanical mode. In this paper, the PF with respect to generator active power is presented by employing LQ -decomposition based recursive sub-DMD (Rsub-DMD) from synchrophasor ambient data. The proposed participation factor directly quantifies the relationship between the mode damping and generator outputs with the ability to track system operation conditions, thereby identifying the most effective re-dispatching generators online. Then a general online generation rescheduling strategy integrated with the proposed PF is applied for enhancing critical damping. The proposed PF-assisted rescheduling strategy is ambient data oriented, and the developed scheme enables online damping enhancement of critical mode. The effectiveness of the PF-assisted selection of rescheduled generators and the proposed rescheduling strategy has been demonstrated via numerical simulation and simplified China Northern system model cases. [ABSTRACT FROM AUTHOR]

• The optimal scheduling model for VPP to participate in carbon trading and green certificate trading is established. • The reverse search strategy and dimension mutation strategy of the proposed SCV-PSO algorithm can significantly improve the performance of the algorithm. • The proposed optimal scheduling model can significantly improve the economy and environmental friendliness of VPP operation. The grid connection of large-scale clean energy provides the possibility for the establishment of a clean energy system. The urgent problem that needs to be solved is how to improve the utilization efficiency of clean energy to reduce carbon emissions. First, the virtual power plant (VPP) operation mode under the carbon trading and green certificate trading mechanism is analyzed. Secondly, this paper incorporates carbon trading mechanism and green certificate trading mechanism into the optimal dispatch model of VPP including wind power generation, photovoltaic power generation, gas turbines and energy storage device. Taking the net profit of VPP as the optimization goal, which can take into account both economic and environmental protection. Based on whether VPP participates in carbon trading and green certificate trading, three schemes are established and compared and analyzed. In addition, to cope with the volatility of renewable energy, the utilization rates of the renewable energy output scenarios for four typical days under the three schemes were compared and analyzed. To solve this problem, this paper proposes the Self-Conclusion and Variational Particle Swarm Optimization (SCV-PSO) algorithm. The simulation results show that the VPP optimal scheduling model and solving algorithm proposed can effectively improve the utilization rate of renewable energy and reduce the carbon emission under the premise of ensuring economic efficiency. It can provide a useful reference for the low-carbon economic operation of the power system in the future. [ABSTRACT FROM AUTHOR]

As the uncertainty in energy supply increases, engaging various flexible resources in power systems has emerged as an effective strategy to address wind curtailment issues. Existing research insufficiently explores how EAFs can participate in reducing wind curtailment or optimizing flexible power system resources to decrease CO 2 emissions from TTPs and enhance wind energy absorption across various timescales. This study introduces a dual timescale, dual-tier scheduling methodology incorporating EAF regulation and wind power modal decomposition. The day-ahead model integrates EAF demand response to decrease wind curtailment, a comprehensive wind power allocation, and a TTP carbon minimization model. The intra-day model employs wind power modal decomposition for optimizing BESSs within WFs and schedules TTPs to minimize CO 2 emissions. Implemented through iterative genetic algorithms and CPLEX solver techniques, simulation results from a real-case scenario indicate that incorporating EAF loads reduces wind curtailment by 40.49 % and cuts CO 2 emissions by 2.5 % in the day-ahead phase. Furthermore, by applying modal decomposition, TTPs and BESSs absorb fluctuating wind power components, ensuring maximal wind utilization and substantial CO 2 reduction at TTPs. This approach offers vast potential to enhance power system flexibility, advance energy-intensive industries' transition, and foster low-carbon initiatives at TTPs. • An energy-intensive model for EAFs is devised based on operational traits. • TPP scheduling achieves minimal CO 2 emissions. • VMD decomposes day-ahead wind curves, integrating thermal and storage for optimal absorption. • A dual timescale, dual-tier optimization scheduling approach is introduced. [ABSTRACT FROM AUTHOR]

• Hankel block-enhanced dynamic mode decomposition (HeDMD), which is dramatically time-saving, is proposed to extract the inter-area modes from ambient data in near real time. • One of the contributions of this work is to prove the benefits of the use of HeDMD in the extraction of ambient modes. • The use of a Hankel block in the proposed method significantly improves the DMD-based extraction of ambient modes in near real time when applied to simulated data and a real measurement case study. Ambient data-driven algorithms for the extraction of electromechanical modes serve as useful and practical methods for assessing the small signal stability of power systems in real time. This paper proposes a Hankel block-enhanced dynamic mode decomposition (HeDMD) based approach for the online extraction of modes from synchrophasor ambient data. To improve the ability of the dynamic mode decomposition (DMD) algorithm to capture modes from ambient data, a Hankel matrix is introduced to rearrange the measured data. With the extension function of the Hankel block, the mode frequency and damping ratio can be estimated by using only a short data window, which can effectively reduce the computation time. The performance of the proposed HeDMD method for the extraction of modes is investigated using simulated data from both the IEEE 4-generator system and the IEEE 16-generator system and by using ambient measurement data from a real power system. [ABSTRACT FROM AUTHOR]

The significant expansion of wind power has presented challenging obstacles to power system operation. The inherent stochasticity and variability of wind power have emerged as critical issues that impede the advancement of wind energy, affecting its integration and volatility control. Moreover, with the momentum of carbon peaking and carbon neutrality objectives, reducing CO 2 emissions has become urgent. This study proposes a low-carbon dispatching methodology for power systems to address these challenges. It considers regulating energy-intensive loads of electrically fused magnesium and mitigating wind power fluctuations. The approach involves integrating electrically Fused magnesium loads with tight regulation characteristics on the demand side alongside thermal power plants to optimize the power grid and dispatch collectively. And battery energy storage devices are introduced into various wind power stations on the supply side to smooth out wind power fluctuations. The objective function aims to minimize the sum of the average variance of power fluctuations at all wind power busbars while adhering to constraints of maximum wind power consumption and minimum carbon emissions from thermal power plants. The problem is solved using genetic algorithms. Ultimately, we show how effective our proposed low-carbon dispatching method for power systems is, which deals with regulating energy-intensive loads of electrically fused magnesium and reducing wind power fluctuations. We demonstrate this through a case study of wind curtailment, CO 2 emissions, wind power fluctuations, and other related parameters under different conditions. The approach can enhance renewable energy consumption, reduce carbon emissions, and mitigate the power fluctuations of renewable energy. • A model of regulating energy-intensive load characteristics of electrofused magnesium is proposed. • The fluctuation of wind power is smoothed out by utilizing energy storage devices within wind farms. • A multi-objective scheduling approach is proposed that considers source-load coordination. • A case study on double-scheme regulation for the upper and lower level models. [ABSTRACT FROM AUTHOR]

• The paper proposed an interval method based planning model for multi-energy microgrid. • Our optimal design includes optimal technology portfolio, placement, and operation. • Our model contains linearized electrical AC power flow and heat transfer equations. • Our model takes uncertainties of renewable energy and demands into account via intervals. • The feasibility and superiority of the model are verified via case studies and analyses. Creating an optimal design for multi-energy microgrids is a challenge owing to the complicated energy flows existing between sources and demands. In addition, the uncertainties of microgrids, in particular, the stochastic problem of wind, solar, and energy demands, are difficult to describe and overcome. To address these problems, this study proposes an interval method based planning model for MEMGs that determines the device to be installed, the optimal device capacity, the optimal device placement, and the associated optimal operation of multiple energy types with considering uncertainties, the electrical power flow and heat flow equations was also included in our model. These uncertainties of renewable energy and demand are described as intervals, and based on the interval linear programming theory, the corresponding uncertain constraints could be converted to deterministic ones. The developed model is formulated as mixed-integer linear programming, which renders the design model easy to understand and compute. The feasibility and superiority of the model are verified via case studies and analyses, although the results of proposed model turn to be more conservative which means low efficiency in economy, the planning scheme is able to adapt to different uncertain scenarios and maintain the reliability of operation, which means that the robustness of the result were enhanced. [ABSTRACT FROM AUTHOR]

• An optimal WADC controller considering time delay is proposed to suppress low frequency oscillations. • A USP provides compensation of time delay, thus mitigating the influence of time delay on the control system. • The ITAE criterion considers both the excitation system speed and the controller damping characteristics. • The proposed control approach improves the dynamic performance and the time-delay stability boundaries. Wide-area signals from wide-area damping controllers have non-negligible delays during acquisition and transmission in power systems. Such delays can undermine the damping performance of wide-area power system stabilizers. To handle this type of delay, we propose an optimal wide-area damping controller considering time delay. First, the open-loop power system is linearized, and the installation locations of the controller and feedback signal are selected from geometric measurements. Then, frequency-domain identification allows to obtain the system estimation model, and a unified Smith predictor provides compensation of time delay, thus mitigating the influence of time delay on the control system. Finally, particle swarm optimization is applied to further improve the controller. The proposed controller not only eliminates the influence of time delay but also ensures robustness by automatically performing particle swarm optimization and using collaborative search of individual local and group global information to speed up convergence. Taking the two-area four-machine and New England systems for testing, simulation results show that the proposed method enhances the ability of the closed-loop system to withstand time delay and effectively improves both the damping of low-frequency oscillations and the dynamic stability of the power systems. [ABSTRACT FROM AUTHOR]

Abstract In order to make full use of the built wind farm, it is one of the most basic work to analyze the output characteristics. In this paper, a thought of mining wind power output characteristics with the perspective of descriptive statistical analysis and correlation is put forward. Based on the measured data, the output characteristics of the wind farm are analyzed from two aspects of descriptive statistics: the digital feature and the distribution characteristics. Then, in view of the wind speed-power relationship, an envelope domain is constructed to characterize it, and its rationality is verified by the defined data density index of wind power output. The method proposed in this paper is helpful to effectively characterize the output characteristics of wind farm connected to the network, and lays a foundation for better operation and management of wind farms. Highlights • The characteristics of wind farms are investigated by descriptive statistical analysis. • Weibull distribution is validated by comparing the cumulative probability of output ratio of wind farms. • Envelope domain is constructed to describe the relations between wind speed and power. • A data density index is developed to verify the rationality of envelope domain. • The applicability of envelope domain in three intervals of wind speed is compared. [ABSTRACT FROM AUTHOR]

The large-scale access of a substantial proportion of the distributed photovoltaic (PV) power sources has introduced considerable source-side randomness and volatility to the distribution network in the development of PV power generation in the whole county of China. This paper proposes a cooling-heat-electric multi-energy coupled power distribution network expansion bi-level planning model to reduce the influence of uncertainty and improve the PV consumption rate (PVCR). It is based on the distributed hydrogen-thermal storage system (DHTSS) in a high-proportion PV scenario. First, an irradiation-load-temperature multi-grid joint scenario generation model is constructed based on each meteorological cluster to effectively describe the uncertainty of PV power generation and multi-energy consumption. Subsequently, a refined distributed PV system physical model chain is constructed to address the low accuracy of the traditional PV output model in the high PV output (HPVO) scenarios. Lastly, considering the power supply reliability objective of the low-probability with high-load (LPHL) scenarios, a multi-objective bi-level expansion planning model of the distribution network is constructed to accurately determine the capacity of each equipment in DHTSS, the distribution lines, and low-voltage transformers to be expanded in the distribution network. • To propose an irradiation-load-temperature multi-grids joint scenarios generation model. • To propose a method of refined distributed PV system physical model chain. • To propose an operating model of the distributed hydrogen-thermal storage system. • To propose a multi-objective bi-level distribution network expansion planning model. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive overview on the recent advances of miniature hybrid Unmanned Aerial Vehicles (UAVs). For now, two conventional types, i.e., fixed-wing UAV and Vertical Takeoff and Landing (VTOL) UAV, dominate the miniature UAVs. Each type has its own inherent limitations on flexibility, payload, flight range, cruising speed, takeoff and landing requirements and endurance. Enhanced popularity and interest are recently gained by the newer type, named hybrid UAV, that integrates the beneficial features of both conventional ones. In this survey paper, a systematic categorization method for the hybrid UAV's platform designs is introduced, first presenting the technical features and representative examples. Next, the hybrid UAV's flight dynamics model and flight control strategies are explained addressing several representative modeling and control work. In addition, key observations, existing challenges and conclusive remarks based on the conducted review are discussed accordingly. [ABSTRACT FROM AUTHOR]

This paper proposes a new breed of solid-state transformer (SST) distribution tail system mainly based on hybrid cascaded multilevel converter and resonant DC-DC converters between medium-voltage distribution and low-voltage customer side. The proposed SST topology mainly combines per-phase cascaded H-bridge converters based on the DC transformers (DCTs) with the square-wave two-level VSC based on the assembled DCT together. The proposed system can offer the operational flexibility for active and reactive power control for medium-voltage side based on the simplified single-stage control system structure concept thanks to the characteristics of the resonant DC-DC converters working under the quasi-resonant mode. Additionally, compared to the existing SST systems, it offers better features such as smaller space volume, simpler control system, lower unit cost, etc. To illustrate the feasibility of the proposed SST distribution tail system, this paper assesses its dynamic performance during steady-state and network alterations, including its response to medium-voltage ac-side unbalance. [ABSTRACT FROM AUTHOR]

Fibroblast is believed to be the primary effector in idiopathic pulmonary fibrosis (IPF), a progressive lung disorder characterized by aberrant tissue remodeling and the formation of fibroblastic foci. Due to the complicated etiology and mechanism, there are few effective drugs for this fatal disease. Shikonin (SHI), which is the major ingredient isolated from the plant Lithospermum Erythrorhizon, has long been used as traditional medicine for many diseases including inflammation and cancer. The roles of SHI in attenuating skin scar and renal fibrosis by reducing TGFβ1-stimulated fibroblast activation are also reported. But whether SHI works on IPF which exhibits both inflammatory and carcinoma-like features remains unknown. In this study, using isolated pulmonary fibroblasts, we demonstrated that SHI inhibited the proliferation, migration of fibroblasts, enhanced cell apoptosis and led to cell cycle arrest at G1 and G2/M phase. Moreover, SHI reduced the production of α-SMA, fibronectin, collagen I and III in response to TGF-β induction in pulmonary fibroblasts, and all of these gene production is the key component of extracellular matrix for tissue remodeling for IPF. The phosphorylation of Akt was down-regulated, p53 increased, the mRNA levels of p21 and p27 enhanced after SHI treatments. The phosphorylation of both p38 MAPK and Akt stimulated by TGF-β was reduced after SHI treatments. Collectively, these data indicate that SHI has a strong cytotoxicity in pulmonary fibroblast via inhibiting Akt activation signaling pathway, and attenuates TGF-β induced extracellular matrix genes production in pulmonary fibroblasts via modulating the activities of p38 MAPK and Akt. SHI might serve as a therapeutically candidate for IPF patients. [ABSTRACT FROM AUTHOR]

• The constructed similar-weighted spatial-temporal graph can directly reflect the coupling relationship between loads of each bus and has certain interpretability. • Mining coupling associations between specific bus loads through SCL. And the spatial aggregation features are generated to realize the enhancement of full-domain node features. • The aggregated features are constructed as temporal series and input to GRUL to mine their temporal domain features. Comparative experiments show that the proposed method greatly reduces the worst load forecast evaluation metrics. Moreover, it has better robustness when containing abnormal load data. Existing short-term bus load forecasting methods mostly use temporal domain features, such as historical loads, to forecast and do not fully consider the influence of unstructured spatial–temporal coupling correlations among multiple bus loads in a wide spatial area on the forecasting results. In this paper, a wide-area multiple bus loads forecasting model is proposed. First, take the rapid computation of the maximal information coefficient (RapidMIC) value between bus loads as edge features, and combine the multiple node feature set to construct a similar-weighted spatial–temporal graph. The constructed similar-weighted spatial–temporal graph is not constrained by geography and grid topology, and can directly reflect the degree of spatial–temporal coupling correlation between bus loads. Second, the neighboring node features of each node in the graph are extracted by the spatial convolution layer (SCL) to achieve full-domain node feature enhancement. Finally, the features extracted were formed into temporal series and input to the gated recurrent unit layer (GRUL) to achieve a wide-area multiple buses short-term load forecast. The research was carried out by selecting the measured load data (15 busbars). The corresponding symmetric mean absolute percentage error (SMAPE) and mean absolute error (MAE) are 3.19 % to 5.89 % and 1.83 megawatts (MW) to 9.8 MW, respectively. Compared with the SMAPE of other methods, the worst evaluation metrics in the test set are improved by 1.82 % to 5.94 %, and it has better robustness in the scene with abnormal load data. [ABSTRACT FROM AUTHOR]

A renewable energy consumption method based on variational mode decomposition and combined regulation of thermal power and storage system is presented in this paper. By decomposing the equivalent load curve using variational mode decomposition, the equivalent load curve is divided into parts with different center frequencies and energy system constraints and solved separately. We then take the maximum wind power absorption rate as the upper model target and solve it. At the same time, through the operation curves of the thermal power unit and battery storage system, the lower optimization model is established to minimize the system's operating cost with carbon emission costs. The example shows that the regulation method with a storage system improves the consumption rate of wind power and reduces carbon dioxide emission compared with the regulation method without a storage system. Compared with the method without decomposition, the power output curve of the thermal power unit is smoother under the new method, effectively improving the solving speed. It reduces the operating cost of the system and its carbon dioxide emissions. • Variational mode decomposition of equivalent load curve and analysis of its characteristics. • Upper levels model improves wind power accommodation rate. • Lower levels model reduces carbon emissions and operating costs of thermal power units. • Frequency block adjustment makes output curve of thermal power unit smoother. [ABSTRACT FROM AUTHOR]

• To describe the relationship between the EV users' charging behavior, the analysis is carried out from the two dimensions of temporal and spatial. Comprehensively analyze the daytime similarity of the multinode joint charging scenario on the forecast day and the historical day (put the charging load data of each node on the same date in 32 rows in turn, and the charging load matrix is called the multinode joint charging scenario) and the similarity between the multinode charging loads in the scenario (i.e., the correlation between the charging loads in each row in the charging load matrix). • To deal with the strong spatial–temporal uncertainty of EV charging load and mining the potential spatial–temporal distribution of charging load, a data-driven scenario analysis method is proposed. This method uses the WGAN-GP model for scenario generation, which avoids assuming that the charging load data of different charging stations must obey a single probability distribution. • To more effectively forecast the spatial–temporal distribution of charging load in space, a new multinode charging load interval-forecasting method is proposed considering the spatial correlation of charging load. It avoids the problem that the total charging load obtained by each node forecasting method is much higher than the actual demand. The spatial–temporal distribution of electric vehicle (EV) charging load has strong randomness and is affected by battery capacity and user behavior. In addition, the multinode charging load in the distribution network has differential correlations. A multinode charging load joint adversarial generation interval-forecasting method considering the spatial correlation of the charging load between nodes is proposed to effectively forecast the spatial–temporal distribution of EV charging load. First, the multinode joint charging scenario is constructed. Under the spatial charging load matrix, the spatial–temporal correlation between multinode charging loads in the joint charging scenario of the forecast day and the historical day is analyzed. According to the strong-correlation historical-day multinode joint charging scenario of the forecasting day, the original multinode multiple-correlation-day joint charging scenario set, describing the charging behavior of multinode EVs, is determined. Second, a Wasserstein generative adversarial network with a gradient penalty is used to characterize the strong randomness of the spatial–temporal distribution of the charging load. A large number of joint charging scenarios with similar probability distributions but different timing distributions from the original scenario set are generated to obtain the potential spatial–temporal distribution of the multinode joint charging load. Then, based on the weighted two-dimensional correlation coefficient, the strong-correlation joint scenario set on the day to be forecast is selected from the generated multinode multiple-correlation-day joint charging scenario set. Finally, according to the strong-correlation joint scenario set on the day to be forecast, the interval-forecasting conclusion of the multinode EV charging load is obtained. To verify the effectiveness of the new multinode charging load interval-forecasting method, the simulation experiment uses the measured charging load data of 32 charging stations in a region of Zhejiang Province. The comparative experiment demonstrated that the proposed method has more-refined intervals and higher coverage than state-of-the-art interval forecasting models. Among the evaluation indexes of the charging load forecasting results of the new method, the PICP value is higher than 90.4%, and the PINAW and MAPE values are lower than 32.1% and 17.7%, respectively. The new method overcomes the limitation that the total charging load obtained by each node's charging load forecasting method is much higher than the actual demand. [ABSTRACT FROM AUTHOR]

To fulfill the commitment to carbon emission reduction, the grid penetration rate of renewable energy in China has increased rapidly. High penetration of renewable energy brings a significant challenge to the peaking ancillary services providers. In northern China, coal-fired units still play a significant role in peak-shaving, especially in areas where pumped hydropower, gas-fired power, and controllable load are limited. The current peaking ancillary service market in China cannot effectively mobilize the enthusiasm of peaking power plants. Fair calculation of the peak-shaving costs of coal-fired power plants is significant for formulating an ancillary service market mechanism. Current research on peak-shaving costs calculation of coal-fired power fleets is limited to the operating coal consumption costs, wear-and-tear costs, and the additional environmental costs (do not contain pollutant tax and ashes treatment). However, this costs calculation lacks consideration of the additional peaking coal consumption costs, the additional peaking environmental costs with pollution tax and ashes treatment, and the power output loss caused by the peak-shaving process. Therefore, this study proposes a novel calculating framework that includes all the potential peak-shaving costs of the coal-fired generating units mentioned above. To evaluate the proposed framework, a case study was conducted in a coal-fired power plant in Dalian City, Liaoning Province, China, in 2020. The results show that the total costs of peak-shaving in 2020 was only 4.96 million Yuan according to the existing method. However, according to the proposed costs calculation framework, the peak-shaving costs in 2020 should be 16.97 million Yuan. The power grid compensated 12.83 million Yuan to the power plant, and the real peaking net profits was -4.14 million Yuan, which means coal-fired power units are unprofitable under the current peaking compensation mechanism. Compared with the existing traditional costs calculation method, the proposed method could provide a more comprehensive and accurate costs accounting for the deep peak-shaving process of coal-fired units. This study may help formulate the benchmark and reasonable compensation in the peak-shaving ancillary service market. • A novel peak shaving cost calculation model is proposed for coal-fired power plants. • Minutes-level operational data are used to analyze peak shaving costs and profits. • Coal-fired power plants may not benefit under the current compensation mechanism. • The economic comparison between different coal prices for peak shaving. [ABSTRACT FROM AUTHOR]