The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation h...The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation has grown more vibrant,thus a novel approach called safe deep reinforcement learning is proposed in this paper.Herein,the real-time ACOPF problem is modeled as a constrained Markov decision process,and primal-dual optimization(PDO)based proximal policy optimization(PPO)is used to learn the optimal generator outputs in the primal domain and security constraints in the dual domain,which avoids manually selecting a trade-off between penalties for constraint violations and rewards for the economy.Before training,behavior cloning clones the expert experience into the initial weights of neural networks.Moreover,multiprocessing training is utilized to accelerate the training speed.Case studies are conducted on the IEEE 118-bus system and the modified IEEE 118-bus system.Compared with other methods,the experimental results show that the proposed method can achieve security and near-optimal economic goals by fast calculating the real-time ACOPF problem.展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po...With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.展开更多
An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performanc...An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.展开更多
In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model ave...In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.展开更多
In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In additi...In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.展开更多
According to the dynamic interaction process between cyber flow and power flow in grid cyber-physical systems(GCPS),attackers could gradually trigger large-scale power failures through cooperative cyber-attacks,subseq...According to the dynamic interaction process between cyber flow and power flow in grid cyber-physical systems(GCPS),attackers could gradually trigger large-scale power failures through cooperative cyber-attacks,subsequently forming cross-domain cascading failures(CDCF)that cross cyber-domain and power-domain and endanger the stable running of GCPS.To reveal the evolutionary mechanism of CDCF,an optimal attack scheme evaluation method is proposed,considering the spatiotemporal synergy of multiple attack-event-chains.First,in accordance with the spatiotemporal synergy of multiple attack-event-chains,the CDCF evolutionary mechanism is analyzed from the attackers'perspective,and a CDCF mathematical model is established.Furthermore,an attack graph model of CDCF evolution and its hazard calculation method are proposed.Then,the attackers'decision-making process for the optimal attack scheme of CDCF is deduced based on the attack graph model.Finally,both the evaluation and implementation processes of the optimal attack scheme are simulated in the GCPS experimental system based on IEEE-39 bus systems.展开更多
In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperati...In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperative behavior of players in the system and introduce the concept of positive-incentive noise in CAID.We design a global cost function to ensure convergence of the system can be achieved and strive to improve the final level of cooperation.An optimal CAID control method is proposed to derive the deterministic optimal learning rate in analytical form,avoiding the variability and uncertainty brought about by neural network fitting or parameter adjustment.On this basis,the convergence of the dynamic model is further analyzed by using the Lyapunov function instead of the Jacobian matrix.Additionally,an adaptive filtering mechanism is designed to dynamically ensure that only positive-incentive noise affects the system,effectively reducing the impact of negative noise and enhancing system stability.The framework is validated through simulations involving triple classical game models,including the hawk-dove game,the stag hunt game,the chicken game on networks,and a straightforward illustrative example.展开更多
Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing riboso...Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing ribosome binding.Escherichia coli(E.coli)counters this bottleneck by inducing the cold-shock protein A(CspA),an RNA chaperone that remodels inhibitory structures.However,synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism,creating a fundamental resource-allocation trade-off.In this work,we develop a dynamical model capturing the interplay between metabolic precursors,ribosomes,and CspA,and use it to examine how growth and allocation patterns shift with temperature.Steady-state analysis shows that each temperature produces a distinct,locally stable equilibrium,illustrating how cold environments reshape cellular priorities.We then formulate growth maximization as an optimal control problem,solved using Pontryagin’s Maximum Principle,to identify allocation strategies that balance translation maintenance and biomass production.The resulting optimal strategies exhibit bang-bang and singular structures,highlighting periods of extreme and intermediate allocation that reflect how bacteria might dynamically prioritize competing cellular functions.These control patterns converge to their corresponding steady state allocations and provide quantitative insight into optimal resource management under cold stress.These results provide a quantitative optimal-control framework linking RNA-level cold-shock adaptation to proteome allocation and growth,yielding testable predictions for how bacteria balance translational maintenance and biomass production at suboptimal temperatures.展开更多
In mixture experiments,the observed response is determined by the relative proportions of the components,consequently rendering the experimental region a simplex.This paper focuses primarily on the optimal designs of ...In mixture experiments,the observed response is determined by the relative proportions of the components,consequently rendering the experimental region a simplex.This paper focuses primarily on the optimal designs of mixture experiments that involve process variables.Prior research has extensively delved into optimal orthogonal block designs for some classic mixture models with process variables.Based on the framework of general blending models,this paper proposes a class of symmetric linear mixture models,which can be regarded as a generalization of many existing ones.Under the orthogonal blocking conditions,orthogonal block designs are devised through Latin squares in the presence of process variables.TheD-,A-,and E-optimality criteria are utilized to obtain optimal designs at the boundary of the simplex in the case of 3 components.As the values of the exponents change,numerically derived optimal design points are presented to illustrate the pattern of their variations,and to verify the consistency of the results with previous research on some specific symmetric general blending models.展开更多
The augmented evolution equation is established under the framework of the Variation Evolving Method(VEM)that seeks optimal solutions by solving the transformed Initial-Value Problems(IVPs).To improve the numerical pe...The augmented evolution equation is established under the framework of the Variation Evolving Method(VEM)that seeks optimal solutions by solving the transformed Initial-Value Problems(IVPs).To improve the numerical performance,its compact form is developed herein.Through replacing the states and costates variation evolution with that of the controls,the dimension-reduced Evolution Partial Differential Equation(EPDE)only solves the control variables along the variation time to get the optimal solution,and the initial conditions for the definite solution may be arbitrary.With this equation,the scale of the resulting IVPs,obtained via the semi-discrete method,is significantly reduced and they may be solved with common Ordinary Differential Equation(ODE)integration methods conveniently.Meanwhile,the state and the costate dynamics share consistent stability in the numerical computation and this avoids the intrinsic numerical difficulty as in the indirect methods.Numerical examples are solved and it is shown that the compact form evolution equation outperforms the primary form in the precision,and the efficiency may be higher for the dense discretization.Actually,it is uncovered that the compact form of the augmented evolution equation is a continuous realization of the Newton type iteration mechanism.展开更多
Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic effici...Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic efficiency.In this paper,aiming at the uncertainty of distributed wind power and photovoltaic output,considering the coupling relationship between power,carbon trading,and green cardmarket,the optimal operationmodel and bidding scheme of VPP in spot market,carbon trading market,and green card market are established.On this basis,through the Shapley value and independent risk contribution theory in cooperative game theory,the quantitative analysis of the total income and risk contribution of various distributed resources in the virtual power plant is realized.Moreover,the scheduling strategies of virtual power plants under different risk preferences are systematically compared,and the feasibility and accuracy of the combination of Shapley value and independent risk contribution theory in ensuring fair income distribution and reasonable risk assessment are emphasized.A comprehensive solution for virtual power plants in the multi-market environment is constructed,which integrates operation strategy,income distribution mechanism,and risk control system into a unified analysis framework.Through the simulation of multi-scenario examples,the CPLEXsolver inMATLAB software is used to optimize themodel.The proposed joint optimization scheme can increase the profit of VPP participating in carbon trading and green certificate market by 29%.The total revenue of distributed resources managed by VPP is 9%higher than that of individual participation.展开更多
Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are o...Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are often effective for stabilization but may not directly optimize long-term performance.To address this limitation,this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator.The proposed scheme adopts an actor–critic structure,where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation,and the actor network generates near-optimal control signals in real time.This dual adaptation enables the controller to refine its policy online without explicit system knowledge.Stability of the closed-loop system is analyzed through Lyapunov theory,ensuring boundedness of the tracking error.Numerical simulations on the single-link manipulator demonstrate that themethod achieves accurate trajectory followingwhile maintaining lowcontrol effort.The results further showthat the actor–critic learning mechanism accelerates convergence of the control policy compared with conventional optimization-based strategies.This work highlights the potential of reinforcement learning integrated with optimal control for robotic manipulators and provides a foundation for future extensions to more complex multi-degree-of-freedom systems.The proposed controller is further validated in a physics-based virtual Gazebo environment,demonstrating stable adaptation and real-time feasibility.展开更多
Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft conti...Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.展开更多
In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a ...In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.展开更多
In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along...In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.展开更多
Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energ...Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.展开更多
Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power fl...With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.展开更多
Dear Editor,In this letter,we focus on the algebraic relationship between the coefficient matrices and the solution of the stochastic algebraic Riccati equation.It is revealed that,if the coefficient matrices are in a...Dear Editor,In this letter,we focus on the algebraic relationship between the coefficient matrices and the solution of the stochastic algebraic Riccati equation.It is revealed that,if the coefficient matrices are in an algebra,then the solution(and also the control gain in many cases)is also in the same algebra.The main result is verified by a numerical simulation.展开更多
基金supported by the National Natural Science Foundation of China(52007173 and U22B2098).
文摘The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation has grown more vibrant,thus a novel approach called safe deep reinforcement learning is proposed in this paper.Herein,the real-time ACOPF problem is modeled as a constrained Markov decision process,and primal-dual optimization(PDO)based proximal policy optimization(PPO)is used to learn the optimal generator outputs in the primal domain and security constraints in the dual domain,which avoids manually selecting a trade-off between penalties for constraint violations and rewards for the economy.Before training,behavior cloning clones the expert experience into the initial weights of neural networks.Moreover,multiprocessing training is utilized to accelerate the training speed.Case studies are conducted on the IEEE 118-bus system and the modified IEEE 118-bus system.Compared with other methods,the experimental results show that the proposed method can achieve security and near-optimal economic goals by fast calculating the real-time ACOPF problem.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金Supported by the Technology Project of State Grid Corporation Headquarters(No.5100-202322029A-1-1-ZN)the 2024 Youth Science Foundation Project of China (No.62303006)。
文摘With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.
基金supported in part by National Key R&D Program of China(2022YFF0610600).
文摘An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.
基金supported by Youth Academic Innocation Team Construction project of Capital University of Economics and Business under Grant No.QNTD202303supported by the Beijing Outstanding Young Scientist Program under Grant No.JWZQ20240101027the National Natural Science Foundation of China under Grant Nos.12031016,12531012 and 12426308。
文摘In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.
基金supported by the 2024 Research Fund of University of Ulsan.
文摘In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.
基金supported by National Natural Science Foundation of China(51977155 and 61833008).
文摘According to the dynamic interaction process between cyber flow and power flow in grid cyber-physical systems(GCPS),attackers could gradually trigger large-scale power failures through cooperative cyber-attacks,subsequently forming cross-domain cascading failures(CDCF)that cross cyber-domain and power-domain and endanger the stable running of GCPS.To reveal the evolutionary mechanism of CDCF,an optimal attack scheme evaluation method is proposed,considering the spatiotemporal synergy of multiple attack-event-chains.First,in accordance with the spatiotemporal synergy of multiple attack-event-chains,the CDCF evolutionary mechanism is analyzed from the attackers'perspective,and a CDCF mathematical model is established.Furthermore,an attack graph model of CDCF evolution and its hazard calculation method are proposed.Then,the attackers'decision-making process for the optimal attack scheme of CDCF is deduced based on the attack graph model.Finally,both the evaluation and implementation processes of the optimal attack scheme are simulated in the GCPS experimental system based on IEEE-39 bus systems.
基金supported by the National Science Fund for Distinguished Young Scholars(62025602)the National Natural Science Foundation of China(62373302,U22B2036)+2 种基金the National Key Research and Development Program of China(2024YFF0509600)the Fundamental Research Funds for the Central Universities(G2024WD0151,D5000240309)the Tencent Foundation and XPLORER PRIZE。
文摘In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperative behavior of players in the system and introduce the concept of positive-incentive noise in CAID.We design a global cost function to ensure convergence of the system can be achieved and strive to improve the final level of cooperation.An optimal CAID control method is proposed to derive the deterministic optimal learning rate in analytical form,avoiding the variability and uncertainty brought about by neural network fitting or parameter adjustment.On this basis,the convergence of the dynamic model is further analyzed by using the Lyapunov function instead of the Jacobian matrix.Additionally,an adaptive filtering mechanism is designed to dynamically ensure that only positive-incentive noise affects the system,effectively reducing the impact of negative noise and enhancing system stability.The framework is validated through simulations involving triple classical game models,including the hawk-dove game,the stag hunt game,the chicken game on networks,and a straightforward illustrative example.
基金supported by NASA Oklahoma Established Program to Stimulate Competitive Research(EPSCoR)Infrastructure Development,“Machine Learning Ocean World Biosignature Detection from Mass Spec,”(PI:Brett McKinney),Grant No.80NSSC24M0109Tandy School of Computer Science,The University of Tulsa.
文摘Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing ribosome binding.Escherichia coli(E.coli)counters this bottleneck by inducing the cold-shock protein A(CspA),an RNA chaperone that remodels inhibitory structures.However,synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism,creating a fundamental resource-allocation trade-off.In this work,we develop a dynamical model capturing the interplay between metabolic precursors,ribosomes,and CspA,and use it to examine how growth and allocation patterns shift with temperature.Steady-state analysis shows that each temperature produces a distinct,locally stable equilibrium,illustrating how cold environments reshape cellular priorities.We then formulate growth maximization as an optimal control problem,solved using Pontryagin’s Maximum Principle,to identify allocation strategies that balance translation maintenance and biomass production.The resulting optimal strategies exhibit bang-bang and singular structures,highlighting periods of extreme and intermediate allocation that reflect how bacteria might dynamically prioritize competing cellular functions.These control patterns converge to their corresponding steady state allocations and provide quantitative insight into optimal resource management under cold stress.These results provide a quantitative optimal-control framework linking RNA-level cold-shock adaptation to proteome allocation and growth,yielding testable predictions for how bacteria balance translational maintenance and biomass production at suboptimal temperatures.
基金supported by the National Natural Science Foundation of China[grant numbers 12071329,12471246].
文摘In mixture experiments,the observed response is determined by the relative proportions of the components,consequently rendering the experimental region a simplex.This paper focuses primarily on the optimal designs of mixture experiments that involve process variables.Prior research has extensively delved into optimal orthogonal block designs for some classic mixture models with process variables.Based on the framework of general blending models,this paper proposes a class of symmetric linear mixture models,which can be regarded as a generalization of many existing ones.Under the orthogonal blocking conditions,orthogonal block designs are devised through Latin squares in the presence of process variables.TheD-,A-,and E-optimality criteria are utilized to obtain optimal designs at the boundary of the simplex in the case of 3 components.As the values of the exponents change,numerically derived optimal design points are presented to illustrate the pattern of their variations,and to verify the consistency of the results with previous research on some specific symmetric general blending models.
基金supported by the National Nature Science Foundation of China under Grant No.11902332。
文摘The augmented evolution equation is established under the framework of the Variation Evolving Method(VEM)that seeks optimal solutions by solving the transformed Initial-Value Problems(IVPs).To improve the numerical performance,its compact form is developed herein.Through replacing the states and costates variation evolution with that of the controls,the dimension-reduced Evolution Partial Differential Equation(EPDE)only solves the control variables along the variation time to get the optimal solution,and the initial conditions for the definite solution may be arbitrary.With this equation,the scale of the resulting IVPs,obtained via the semi-discrete method,is significantly reduced and they may be solved with common Ordinary Differential Equation(ODE)integration methods conveniently.Meanwhile,the state and the costate dynamics share consistent stability in the numerical computation and this avoids the intrinsic numerical difficulty as in the indirect methods.Numerical examples are solved and it is shown that the compact form evolution equation outperforms the primary form in the precision,and the efficiency may be higher for the dense discretization.Actually,it is uncovered that the compact form of the augmented evolution equation is a continuous realization of the Newton type iteration mechanism.
基金funded by the Department of Education of Liaoning Province and was supported by the Basic Scientific Research Project of the Department of Education of Liaoning Province(Grant No.LJ222411632051)and(Grant No.LJKQZ2021085)Natural Science Foundation Project of Liaoning Province(Grant No.2022-BS-222).
文摘Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic efficiency.In this paper,aiming at the uncertainty of distributed wind power and photovoltaic output,considering the coupling relationship between power,carbon trading,and green cardmarket,the optimal operationmodel and bidding scheme of VPP in spot market,carbon trading market,and green card market are established.On this basis,through the Shapley value and independent risk contribution theory in cooperative game theory,the quantitative analysis of the total income and risk contribution of various distributed resources in the virtual power plant is realized.Moreover,the scheduling strategies of virtual power plants under different risk preferences are systematically compared,and the feasibility and accuracy of the combination of Shapley value and independent risk contribution theory in ensuring fair income distribution and reasonable risk assessment are emphasized.A comprehensive solution for virtual power plants in the multi-market environment is constructed,which integrates operation strategy,income distribution mechanism,and risk control system into a unified analysis framework.Through the simulation of multi-scenario examples,the CPLEXsolver inMATLAB software is used to optimize themodel.The proposed joint optimization scheme can increase the profit of VPP participating in carbon trading and green certificate market by 29%.The total revenue of distributed resources managed by VPP is 9%higher than that of individual participation.
基金supported in part by the National Science and Technology Council under Grant NSTC 114-2221-E-027-104.
文摘Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are often effective for stabilization but may not directly optimize long-term performance.To address this limitation,this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator.The proposed scheme adopts an actor–critic structure,where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation,and the actor network generates near-optimal control signals in real time.This dual adaptation enables the controller to refine its policy online without explicit system knowledge.Stability of the closed-loop system is analyzed through Lyapunov theory,ensuring boundedness of the tracking error.Numerical simulations on the single-link manipulator demonstrate that themethod achieves accurate trajectory followingwhile maintaining lowcontrol effort.The results further showthat the actor–critic learning mechanism accelerates convergence of the control policy compared with conventional optimization-based strategies.This work highlights the potential of reinforcement learning integrated with optimal control for robotic manipulators and provides a foundation for future extensions to more complex multi-degree-of-freedom systems.The proposed controller is further validated in a physics-based virtual Gazebo environment,demonstrating stable adaptation and real-time feasibility.
基金supported in part by the Innovation and Technology Commission of Hong Kong,China(ITS/136/20,ITS/234/21,MHP/096/22,ITS/235/22)Multi-Scale Medical Robotics Center,InnoHK,China(8312051)+1 种基金Research Grants Council(RGC) of Hong Kong,China(CUHK 14217822,CUHK14207823,AoE/E-407/24-N)The Chinese University of Hong Kong(CUHK) Direct Grant。
文摘Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.
基金supported by the National Natural Science Foundation of China under Grant 62472264the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13。
文摘In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.
文摘In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.
基金supported by Integrated Distribution Network Planning and Operational Enhancement Using Flexibility Domains Under Deep Human-Vehicle-Charger-Road-Grid Coupling(U22B20105).
文摘Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2024162).
文摘With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.
文摘Dear Editor,In this letter,we focus on the algebraic relationship between the coefficient matrices and the solution of the stochastic algebraic Riccati equation.It is revealed that,if the coefficient matrices are in an algebra,then the solution(and also the control gain in many cases)is also in the same algebra.The main result is verified by a numerical simulation.
