Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.

Multi-Physics Coupled Acoustic-Mechanics Analysis and Synergetic Optimization for a Twin-Fluid Atomization Nozzle

Fine particulate matter produced during the rapid industrialization over the past decades can cause significant harm to human health.Twin-fluid atomization technology is an effective means of controlling fine particulate matter pollution.In this paper,the influences of the main parameters on the droplet size,effective atomization range and sound pressure level(SPL)of a twin-fluid nozzle(TFN)are investigated,and in order to improve the atomization performance,a multi-objective synergetic optimization algorithm is presented.A multi-physics coupled acousticmechanics model based on the discrete phase model(DPM),large eddy simulation(LES)model,and Ffowcs Williams-Hawkings(FW-H)model is established,and the numerical simulation results of the multi-physics coupled acoustic-mechanics method are verified via experimental comparison.Based on the analysis of the multi-physics coupled acoustic-mechanics numerical simulation results,the effects of the water flow on the characteristics of the atomization flow distribution were obtained.A multi-physics coupled acoustic-mechanics numerical simulation result was employed to establish an orthogonal test database,and a multi-objective synergetic optimization algorithm was adopted to optimize the key parameters of the TFN.The optimal parameters are as follows:A gas flow of 0.94 m^(3)/h,water flow of 0.0237 m^(3)/h,orifice diameter of the self-excited vibrating cavity(SVC)of 1.19 mm,SVC orifice depth of 0.53 mm,distance between SVC and the outlet of nozzle of 5.11 mm,and a nozzle outlet diameter of 3.15 mm.The droplet particle size in the atomization flow field was significantly reduced,the spray distance improved by 71.56%,and the SPL data at each corresponding measurement point decreased by an average of 38.96%.The conclusions of this study offer a references for future TFN research.

Optimal Operation Strategy of Electricity-Hydrogen Regional Energy System under Carbon-Electricity Market Trading

Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors.However,further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen(P2H)technology,focusing on participating in combined carbon-electricity market transactions.This study introduces an innovative Electro-Hydrogen Regional Energy System(EHRES)in this context.This system integrates renewable energy sources,a P2H system,cogeneration units,and energy storage devices.The core purpose of this integration is to optimize renewable energy utilization and minimize carbon emissions.This study aims to formulate an optimal operational strategy for EHRES,enabling its dynamic engagement in carbon-electricity market transactions.The initial phase entails establishing the technological framework of the electricity-hydrogen coupling system integrated with P2H.Subsequently,an analysis is conducted to examine the operational mode of EHRES as it participates in carbon-electricity market transactions.Additionally,the system scheduling model includes a stepped carbon trading price mechanism,considering the combined heat and power generation characteristics of the Hydrogen Fuel Cell(HFC).This facilitates the establishment of an optimal operational model for EHRES,aiming to minimize the overall operating cost.The simulation example illustrates that the coordinated operation of EHRES in carbon-electricity market transactions holds the potential to improve renewable energy utilization and reduce the overall system cost.This result carries significant implications for attaining advantages in both low-carbon and economic aspects.

Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight Particle Swarm Algorithm

Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm（EPSA）, is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.

Optimal control of attitude for coupled-rigid-body spacecraft via Chebyshev-Gauss pseudospectral method

The attitude optimal control problem （OCP） of a two-rigid-body space- craft with two rigid bodies coupled by a ball-in-socket joint is considered. Based on conservation of angular momentum of the system without the external torque, a dynamic equation of three-dimensional attitude motion of the system is formulated. The attitude motion planning problem of the coupled-rigid-body spacecraft can be converted to a dis- crete nonlinear programming （NLP） problem using the Chebyshev-Gauss pseudospectral method （CGPM）. Solutions of the NLP problem can be obtained using the sequential quadratic programming （SQP） algorithm. Since the collocation points of the CGPM are Chebyshev-Gauss （CG） points, the integration of cost function can be approximated by the Clenshaw-Curtis quadrature, and the corresponding quadrature weights can be calculated efficiently using the fast Fourier transform （FFT）. To improve computational efficiency and numerical stability, the barycentric Lagrange interpolation is presented to substitute for the classic Lagrange interpolation in the approximation of state and con- trol variables. Furthermore, numerical float errors of the state differential matrix and barycentric weights can be alleviated using trigonometric identity especially when the number of CG points is large. A simple yet efficient method is used to avoid sensitivity to the initial values for the SQP algorithm using a layered optimization strategy from a feasible solution to an optimal solution. Effectiveness of the proposed algorithm is perfect for attitude motion planning of a two-rigid-body spacecraft coupled by a ball-in-socket joint through numerical simulation.

Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

Optimal dispatch approach for rural multi-energy supply systems considering virtual energy storage

In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China,this study proposes an optimal dispatch approach for a rural multi-energy supply system(RMESS)considering virtual energy storage(VES).First,to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes,this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint.Subsequently,an RMESS model encompassing these two types of VES was formulated.This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations.Based on the above models,a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation.Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.

Towards Production and Energy Coupling System Modeling and Simulation for Energy Optimization in the Process Industry

The production and energy coupling system is used to mainly present energy flow, material flow, information flow, and their coupling interaction. Through the modeling and simulation of this system, the performance of energy flow can be analyzed and optimized in the process industry. In order to study this system, the component based hybrid Petri net methodology (CpnHPN) is proposed, synthesizing a number of extended Petri net methods and using the concept of energy place, material place, and information place. Through the interface place in CpnHPN, the component based encapsulation is established, which enables the production and energy coupling system to be built, analyzed, and optimized on the multi-level framework. Considering the block and brief simulation for hybrid system, the CpnHPN model is simulated with Simulink/Stateflow. To illustrate the use of the proposed methodology, the application of CpnHPN in the energy optimization of chlorine balance system is provided.

STUDY OF TOPOLOGY OPTIMIZATION FOR THERMO-STRUCTURAL COUPLING FIELD

A number of critical problems of topology optimization concerning the thermostructural coupling field axe studied at length. The governing equations and topology optimization model for the thermal-structural coupling field axe derived, with an adjoint method for sensitivity analysis of the thermo-structural coupling field proposed. The optimization algorithm for coupling field topology optimization is investigated and a flowchart of coupling field topology optimization presented. The theory and algorithms axe implemented and verified by two numerical examples.

An Expanded Optimal Control Policy for a Coupled Tanks System with Random Disturbance

In this paper, an expanded optimal control policy is proposed to study the coupled tanks system, where the random disturbance is added into the system. Since the dynamics of the coupled tanks system can be formulated as a nonlinear system, determination of the optimal water level in the tanks is useful for the operation decision. On this point of view, the coupled tanks system dynamics is usually linearized to give the steady state operating height. In our approach, a model-based optimal control problem, which is adding with adjusted parameters, is considered to obtain the true operating height of the real coupled tanks system. During the computation procedure, the differences between the real plant and the model used are measured repeatedly, where the optimal solution of the model used is updated. On this basis, system optimization and parameter estimation are integrated. At the end of the iteration, the iterative solution approximates to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. In conclusion, the efficiency of the approach proposed is shown.

Peak shaving operation optimization of high proportion new energypower generation considering wind-solar complementationand source-load coupling

To optimize peaking operation when high proportion new energy accesses to power grid,evaluation indexes are proposed which simultaneously consider wind-solar complementation and source-load coupling.A typical wind-solar power output scene model based on peaking demand is established which has anti-peaking characteristic.This model uses balancing scenes and key scenes with probability distribution based on improved Latin hypercube sampling(LHS)algorithm and scene reduction technology to illustrate the influence of wind-solar on peaking demand.Based on this,a peak shaving operation optimization model of high proportion new energy power generation is established.The various operating indexes after optimization in multi-scene peaking are calculated,and the ability of power grid peaking operation is compared whth that considering wind-solar complementation and source-load coupling.Finally,a case of high proportion new energy verifies the feasibility and validity of the proposed operation strategy.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

Optimization method of refracturing timing for old shale gas wells

Based on the elastic theory of porous media,embedded discrete fracture model and finite volume method,and considering the micro-seepage mechanism of shale gas,a fully coupled seepage-geomechanical model suitable for fractured shale gas reservoirs is established,the optimization method of refracturing timing is proposed,and the influencing factors of refracturing timing are analyzed based on the data from shale gas well in Fuling of Sichuan Basin.The results show that due to the depletion of formation pressure,the percentage of the maximum horizontal principal stress reversal area in the total area increases and then decreases with time.The closer the area is to the hydraulic fracture,the shorter the time for the peak of the stress reversal area percentage curve to appear,and the shorter the time for the final zero return(to the initial state).The optimum time of refracturing is affected by matrix permeability,initial stress difference and natural fracture approach angle.The larger the matrix permeability and initial stress difference is,the shorter the time for stress reversal area percentage curve to reach peak and return to the initial state,and the earlier the time to take refracturing measures.The larger the natural fracture approach angle is,the more difficult it is for stress reversal to occur near the fracture,and the earlier the optimum refracturing time is.The more likely the stress reversal occurs at the far end of the artificial fracture,the later the optimal time of refracturing is.Reservoirs with low matrix permeability have a rapid decrease in single well productivity.To ensure economic efficiency,measures such as shut-in or gas injection can be taken to restore the stress,and refracturing can be implemented in advance.

Effect of coil and chamber structure on plasma radial uniformity in radio frequency inductively coupled plasma

Enhancing plasma uniformity can be achieved by modifying coil and chamber structures in radio frequency inductively coupled plasma(ICP)to meet the demand for large-area and uniformly distributed plasma in industrial manufacturing.This study utilized a two-dimensional self-consistent fluid model to investigate how different coil configurations and chamber aspect ratios affect the radial uniformity of plasma in radio frequency ICP.The findings indicate that optimizing the radial spacing of the coil enhances plasma uniformity but with a reduction in electron density.Furthermore,optimizing the coil within the ICP reactor,using the interior point method in the Interior Point Optimizer significantly enhances plasma uniformity,elevating it from 56%to 96%within the range of the model sizes.Additionally,when the chamber aspect ratio k changes from 2.8 to 4.7,the plasma distribution changes from a center-high to a saddleshaped distribution.Moreover,the plasma uniformity becomes worse.Finally,adjusting process parameters,such as increasing source power and gas pressure,can enhance plasma uniformity.These findings contribute to optimizing the etching process by improving plasma radial uniformity.

The Optimal Precursors for ENSO Events Depicted Using the Gradientdefinition-based Method in an Intermediate Coupled Model

The predictability of El Ni-o-Southern Oscillation (ENSO) has been an important area of study for years. Searching for the optimal precursor (OPR) of ENSO occurrence is an effective way to understand its predictability. The CNOP (conditional nonlinear optimal perturbation), one of the most effective ways to depict the predictability of ENSO, is adopted to study the optimal sea surface temperature (SST) precursors (SST-OPRs) of ENSO in the IOCAS ICM (intermediate coupled model developed at the Institute of Oceanology, Chinese Academy of Sciences). To seek the SST-OPRs of ENSO in the ICM, non-ENSO events simulated by the ICM are chosen as the basic state. Then, the gradient-definition-based method (GD method) is employed to solve the CNOP for different initial months of the basic years to obtain the SSTOPRs. The experimental results show that the obtained SST-OPRs present a positive anomaly signal in the western-central equatorial Pacific, and obvious differences exist in the patterns between the different seasonal SST-OPRs along the equatorial western-central Pacific, showing seasonal dependence to some extent. Furthermore, the non-El Ni-o events can eventually evolve into El Ni-o events when the SST-OPRs are superimposed on the corresponding seasons;the peaks of the Ni-o3.4 index occur at the ends of the years, which is consistent with the evolution of the real El Ni-o. These results show that the GD method is an effective way to obtain SST-OPRs for ENSO events in the ICM. Moreover, the OPRs for ENSO depicted using the GD method provide useful information for finding the early signal of ENSO in the ICM.

Optimization of water-urban-agricultural-ecological land use pattern:A case study of Guanzhong Basin in the southern Loess Plateau of Shaanxi Province,China

Extensive land use will cause many environmental problems.It is an urgent task to improve land use efficiency and optimize land use patterns.In recent years,due to the flow decrease,the Guanzhong Basin in Shaanxi Province is confronted with the problem of insufficient water resources reserve.Based on the Coupled Ground-Water and Surface-Water Flow Model(GSFLOW),this paper evaluates the response of water resources in the basin to changes in land use patterns,optimizes the land use pattern,improves the ecological and economic benefits,and the efficiency of various spatial development,providing a reference for ecological protection and high-quality development of the Yellow River Basin.The research shows that the land use pattern in the Guanzhong Basin should be further optimized.Under the condition of considering ecological and economic development,the percentage change of the optimum area of farmland,forest,grassland,water area,and urban area compared with the current land use area ratio is+2.3,+2.4,-6.1,+0.2,and+1.6,respectively.The economic and ecological value of land increases by14.1%and 3.1%,respectively,and the number of water resources can increase by 2.5%.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

Potential Symmetries, One-Dimensional Optimal System and Invariant Solutions of the Coupled Burgers’ Equations

In this paper, we discuss one-dimensional optimal system and the invariant solutions of Coupled Burgers’ equations. By using Wu-differential characteristic set algorithm with the aid of Mathematica software, the classical symmetries of the Coupled Burgers’ equations are calculated, and the one-dimensional optimal system of Lie algebra is constructed. And we obtain the invariant solution of the Coupled Burgers’ equations corresponding to one element in one dimensional optimal system by using the invariant method. The results generalize the exact solutions of the Coupled Burgers’ equations.

Optimization of Impedance Plane Reducing Coupling between Antennas

This paper provides a solution for the design optimization of two-dimensional impedance structures for a given elec-tromagnetic field distribution. These structures must provide electromagnetic compatibility between antennas located on a plane. The optimization problem is solved for a given attenuation of the complete field. Since the design optimiza-tion gives a complex law of impedance distribution with a large real part, we employ the method of pointwise synthesis for the optimization of the structure. We also consider the design optimization case where the structure has zero im-pedance on its leading and trailing edges. The method of moments is used to solve the integral equations and the nu-merical solution is presented. The calculated impedance distribution provides the required level of antenna decoupling. The designs are based on the concept of soft and hard surfaces in electromagnetics.