期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到13,003篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Adaptive distributed optimization of high-order nonlinear multi-agent systems with predefined accuracy under state observer 认领 引用
1
作者 Xiao-Wen Zhao Tong Shu +2 位作者 Deng-Hao Pang Tao Li Mei Yao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第3期236-247,共12页
This paper discusses adaptive distributed optimization with predefined accuracy for high-order nonlinear multi-agent systems(MASs)that are subject to disturbances and nonlinear uncertainties.To estimate the global opt... This paper discusses adaptive distributed optimization with predefined accuracy for high-order nonlinear multi-agent systems(MASs)that are subject to disturbances and nonlinear uncertainties.To estimate the global optimal solution in realtime,a distributed proportional-integral optimization technique is used to generate a virtual system for each agent.For the unknown control gain of the controller,the Nussbaum function is employed.Then,a fuzzy adaptive observer is designed to estimate the unmeasured state by leveraging the general approximation capabilities of fuzzy logic systems.Using the Lyapunov stability method and backstepping technique,we develop the adaptive law and a new distributed controller.This ensures that the outputs of multi-agent systems converge to optimal values.Finally,a simulation example is used to confirm the viability of the presented control mechanism. 展开更多
关键词 distributed optimization nonlinear multi-agent systems(MASs) fuzzy state observer predefined accuracy control
暂未订购 下载PDF
Distributed Load-Sharing and Loss Optimization Within Voltage Safety Constraints for Meshed DC Microgrid 认领 引用
2
作者 Yu Zhang Yan-Wu Wang Xiao-Kang Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1518-1520,共3页
Dear Editor,This letter presents anε-exact penalty-based scalarization method to solve the constrained multi-objective optimization problem of load sharing and transmission loss minimization within voltage safety con... Dear Editor,This letter presents anε-exact penalty-based scalarization method to solve the constrained multi-objective optimization problem of load sharing and transmission loss minimization within voltage safety constraints in a meshed direct current(DC)microgrid.A distributed predefined-time optimization algorithm is designed and implemented by deploying consensus-based observers to obtain an optimal solution.The proposed algorithm is verified by simulations and hardware-in-the-loop experiments in cases of load variation,plugand-play,grid change,and by comparative study. 展开更多
关键词 consensus based observers predefined time optimization transmission loss minimization load sharing distributed load sharing voltage safety constraints exact penalty based scalarization meshed DC microgrid
暂未订购 下载PDF
Multi-Agent Swarm Optimization Method With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association 认领 引用
3
作者 Taiyou Chen Xiaomin Hu +1 位作者 Qiuzhen Lin Weineng Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1673-1688,共16页
With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration abi... With the development of communication and computation capabilities on terminal hardware,it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors.In this work,we study distributed multi-target localization problem with measurement-to-measurement association(DM2M),where each sensor only accesses its own measurement data without the association of measurements from other sensors.We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors.Then,we propose a multiagent swarm optimization method with contribution-based cooperation(MASTER).In MASTER,each sensor maintains a particle swarm to represent candidate solutions of target positions.Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively.To address the bilevel local objective function,we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization.To promote sensors to optimize the global objective,we design a contribution-based cooperation method to guide sensors to learn from their neighbors.Through localization experiments for different target numbers and localization dimensions,the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms. 展开更多
关键词 Distributed optimization evolutionary computation measurement-to-measurement association particle swarm optimization(PSO) wireless sensor networks(WSNs)
暂未订购 下载PDF
Distributed continuous-time aggregative optimization and its applications to power generation systems 认领 引用
4
作者 XIAN Chengxin ZHAO Yu LIU Yongfang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期1-8,共8页
This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to t... This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems. 展开更多
关键词 distributed continuous-time aggregative optimization distributed average tracking(DAT) time-base generator(TBG)
暂未订购 下载PDF
Uncertainty-Aware Distributed Optimization for IoEV Smart Charging and Battery Health Management in Cyber-Physical Smart Grids 认领 引用
5
作者 Supriya Wadekar Shailendra Mittal +3 位作者 Ganesh Wakte Mrunali Kite Aditya Ghonmode Riya Devkate 《Energy Engineering》 EI 2026年第9期157-192,共36页
The rapid expansion of electric vehicles(EVs)and the emergence of the Internet of Electric Vehicles(IoEV)have created considerable operational challenges for modern power systems.Large-scale EV charging can cause peak... The rapid expansion of electric vehicles(EVs)and the emergence of the Internet of Electric Vehicles(IoEV)have created considerable operational challenges for modern power systems.Large-scale EV charging can cause peak demand surges,voltage instability,and inefficient utilization of renewable energy resources when charging activities are not effectively coordinated.This study proposes an uncertainty-aware distributed optimization framework for smart EV charging in cyber-physical smart grids,in which charging schedules are coordinated while simultaneously considering grid capacity constraints,stochastic EV arrival patterns,renewable energy variability,and battery degradation effects.A multi-objective optimization model is formulated to minimize peak grid load,charging cost,and battery degradation.The optimization problem is solved using a distributed algorithm based on the Alternating Direction Method of Multipliers,enabling scalable coordination among multiple charging stations.Simulation studies were carried out in the MATLAB-Simulink environment with EV fleet sizes ranging from 100 to 500 vehicles integrated with solar photovoltaic generation.The results indicate a 39.2%reduction in peak feeder load,a 24%decrease in total charging cost,and an improvement in renewable energy utilization to 76.9%.In addition,the proposed framework reduces annual battery capacity loss from 6.7%to 3.8%compared with uncontrolled charging.The primary contribution of this work is the integration of uncertainty modeling,distributed optimization,and battery health prediction within a unified IoEV charging management framework. 展开更多
关键词 Battery degradation distributed optimization electric vehicle charging internet of electric vehicles(IoEV) smart grid uncertainty-aware optimization
暂未订购 下载PDF
Concurrent topology and fiber distribution optimization of continuous fiber-reinforced polymer(CFRP)structures under thermal-mechanical coupling 认领 引用
6
作者 Yongjia Dong Hongling Ye +1 位作者 Jicheng Li Sujun Wang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期326-346,共21页
Continuous fiber-reinforced polymers(CFRPs)have been extensively utilized in aerospace industries,making it imperative for CFRP structural optimization to consider the effects of extreme service environments.In this p... Continuous fiber-reinforced polymers(CFRPs)have been extensively utilized in aerospace industries,making it imperative for CFRP structural optimization to consider the effects of extreme service environments.In this paper,a thermalmechanical coupling concurrent topology and fiber distribution optimization(TM-CTFDO)method is proposed,specifically tailored for CFRP structures subjected to extreme environmental conditions.The mapping relationships between fiber design variables and material properties are deduced based on the rule of mixture to realize the analysis of thermoelastic CFRP structures.The integrated optimization model for CFRP structures is established with minimizing structural compliance,adhering to the volume constraints of structural and fiber under mechanical and temperature loads.Sensitivity analysis and optimization solution are realized by adopting the adjoint method and the method of moving asymptotes,respectively.This approach culminates in the determination of the optimal topology,fiber orientation,and content.In the post-processing phase,a fiber path planning algorithm is investigated to achieve the continuous fiber path based on the optimization results,which also effectively controls the distribution of dense and sparse fiber.Several examples under uniform and varying temperature fields are provided to verify the effectiveness of the TM-CTFDO method.The influence of temperature and mechanical loads on the optimization results is discussed,which will provide guidance on CFRP structural design and fiber path planning under thermal-mechanical coupling. 展开更多
关键词 Continuous fiber-reinforced polymers Topology optimization Fiber distribution optimization Thermalmechanical coupling Continuous fiber path planning
暂未订购 下载PDF
Optimal Allocation of Distributed Generation and Energy Storage Considering Line Vulnerability under Extreme Weather in Distribution Networks 认领 引用
7
作者 Yangjun Zhou Chenying Yi +10 位作者 Wei Zhang Juntao Pan Ke Zhou Weixiang Huang Like Gao Shan Li Yuanchao Zhou Ling Li Liwen Qin Hongwen Wu Lijuan Yan 《Energy Engineering》 EI 2026年第6期567-583,共17页
The increasing integration of distributed generation(DG)and energy storage systems(ESS)has significantly enhanced the flexibility and efficiency of distribution networks.However,the growing frequency of extreme weathe... The increasing integration of distributed generation(DG)and energy storage systems(ESS)has significantly enhanced the flexibility and efficiency of distribution networks.However,the growing frequency of extreme weather events has exposed the vulnerability of distribution lines,posing serious challenges to the reliability and resilience of such systems.Existing DG and ESS planning models often neglect this vulnerability dimension,leading to suboptimal siting decisions and reduced system robustness.To address this issue,this paper proposes a comprehensive multi-objective optimization framework that coordinates the allocation of DG and ESS and explicitly incorporates line vulnerability under extreme weather conditions.The vulnerability index of each distribution line is first evaluated through Monte Carlo simulations that capture the probabilistic influence of micro-climatic and terrain factors.This assessment serves as a pre-processing stage that screens out high-risk lines and thereby constrains the optimization decision space to more reliable nodes for DG and ESS deployment.Building upon this filtered network,a multi-objective optimization model is established to determine the optimal siting and capacities of DG and ESS.The optimization simultaneously minimizes the total annual cost,which includes investment,operation,and maintenance expenses,as well as network power losses,while improving overall system resilience.A case study on a modified IEEE 33-bus distribution system verifies the effectiveness of the proposed method.The results demonstrate that vulnerability-aware planning achieves a better balance between cost and reliability compared with conventional approaches.Specifically,the proposed strategy reduces annual network losses and outage durations while maintaining voltage stability with respect to climate-adjusted line failure rates.Furthermore,the integration of ESS enables effective peak shaving and valley filling,improving system efficiency and operational flexibility.These findings confirm that incorporating line vulnerability into DG and ESS planning provides a practical and scalable pathway for enhancing the resilience and economy of distribution networks. 展开更多
关键词 Distributed generation energy storage systems distribution networks extreme weather line vulnerability multi-objective optimization
暂未订购 下载PDF
Bi-Objective Optimization of Distribution Network Reliability Enhancement Using Quantitative Decomposition 认领 引用
8
作者 Chenying Yi Yangjun Zhou +8 位作者 Wei Zhang Like Gao Hongwen Wu Yuanchao Zhou Ke Zhou Weixiang Huang Juntao Pan Shan Li Bin Feng 《Energy Engineering》 EI 2026年第8期262-276,共15页
Ensuring reliability in distribution networks is essential under increasing operational and economic constraints.Traditional planning models rely on power flow calculations,leading to high computational costs and poor... Ensuring reliability in distribution networks is essential under increasing operational and economic constraints.Traditional planning models rely on power flow calculations,leading to high computational costs and poor scalability.This study proposes a quantitative decomposition framework that establishes a direct linkage among reliability improvement measures,reliability parameters,and reliability indices,enabling fast and analytical reliability evaluation without power flow analysis.A bi-objective optimization model is developed to minimize both reliability indices(SAIDI)and investment costs,solved using Pareto-based multi-objective PSO combined with the TOPSIS method.Case studies on a 519-node distribution network demonstrate that the proposed approach achieves significant reliability improvement with superior computational efficiency,offering a practical and scalable tool for reliabilityoriented distribution planning. 展开更多
关键词 Distribution network reliability improvement measures quantitative analysis optimization decision
暂未订购 下载PDF
Dual Layer Source Grid Load Storage Collaborative Planning Model Based on Benders Decomposition: Distribution Network Optimization Considering Low-Carbon and Economy 认领 引用 被引量：1
9
作者 Jun Guo Maoyuan Chen +2 位作者 Yuyang Li Sibo Feng Guangyu Fu 《Energy Engineering》 EI 2026年第2期104-133,共30页
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ... Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability. 展开更多
关键词 Benders decomposition source grid load storage distribution network planning low-carbon economy optimization model
暂未订购 下载PDF
Gradient-free distributed online optimization in networks 认领 引用 被引量：1
10
作者 Yuhang Liu Wenxiao Zhao +2 位作者 Nan Zhang Dongdong Lv Shuai Zhang 《Control Theory and Technology》 EI CSCD 2025年第2期207-220,共14页
In this paper,we consider the distributed online optimization problem on a time-varying network,where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss ... In this paper,we consider the distributed online optimization problem on a time-varying network,where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss accumulated.Moreover,we focus on distributed algorithms which do not use gradient information and projection operators to improve the applicability and computational efficiency.By introducing the deterministic differences and the randomized differences to substitute the gradient information of the objective functions and removing the projection operator in the traditional algorithms,we design two kinds of gradient-free distributed online optimization algorithms without projection step,which can economize considerable computational resources as well as has less limitations on the applicability.We prove that both of two algorithms achieves consensus of the estimates and regrets of\(O\left(\log(T)ight)\)for local strongly convex objective,respectively.Finally,a simulation example is provided to verify the theoretical results. 展开更多
关键词 Distributed optimization Online convex optimization Gradient-free algorithm Projection-free algorithm
Design and optimization of steam power systems in industrial parks based on the distributed steam turbine system 认领 引用 被引量：2
11
作者 Lingwei Zhang Ziyuan Cui Yufei Wang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第1期259-272,共14页
Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple ... Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×103USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines. 展开更多
关键词 Industrial parks Steam power systems Distributed steam turbine system Mixed-integer nonlinear programming Optimization Enthalpy
暂未订购 下载PDF
Distributed stochastic model predictive control for energy dispatch with distributionally robust optimization 认领 引用 被引量：1
12
作者 Mengting LIN Bin LI C.C.ECATI 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第2期323-340,共18页
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer... A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved. 展开更多
关键词 distributed stochastic model predictive control(DSMPC) distributionally robust optimization(DRO) islanded multi-microgrid energy dispatch strategy
暂未订购 下载PDF
Operation Optimization of Microgrid Clusters Coordinated with Distribution Systems with Limited Information Exchange 认领 引用
13
作者 Qianfeng Wu Dabo Xie +5 位作者 Wenhua Ni Junjie Zhou Xuantong Lu Chengying Ma Rongqiang Li Yang Li 《Energy Engineering》 EI 2026年第5期229-253,共25页
With the deepening of the power system reform,an increasing number of microgrids are being integrated into the distribution network.In traditional centralized optimization algorithms,the optimal power flow model of th... With the deepening of the power system reform,an increasing number of microgrids are being integrated into the distribution network.In traditional centralized optimization algorithms,the optimal power flow model of the distribution network and the optimal scheduling model of microgrid clusters are directly coupled and solved simultaneously.This process involves extensive information exchange between the upper distribution network system and the lower microgrid clusters,which not only increases the communication burden but also prolongs computation time and raises computational complexity.Moreover,it requires excessive information sharing,making it difficult to achieve limited information exchange between the upper and lower systems.In this paper,an optimization model and solution method based on the analytical target cascading approach are proposed.First,a typical microgrid model is constructed.On this basis,a collaborative optimization model for the active distribution network(ADN)and microgrid clusters is established.The distribution network and the microgrid clusters are treated as a unified entity of interest,with their interconnection power represented as virtual generators and virtual loads to achieve decoupling.Finally,simulations based on the IEEE-33 node standard system are conducted.Compared with the centralized algorithm,the effectiveness of the analytical target cascading method in coordinating the distribution network and microgrid clusters is verified.The proposed approach reduces computational complexity and enables optimized operation with limited information exchange. 展开更多
关键词 Distribution network microgrid clusters analysis target cascading collaborative optimization optimal scheduling limited information exchange
暂未订购 下载PDF
A Regional Distribution Network Coordinated Optimization Strategy for Electric Vehicle Clusters Based on Parametric Deep Reinforcement Learning 认领 引用
14
作者 Lei Su Wanli Feng +4 位作者 Cao Kan Mingjiang Wei Jihai Wang Pan Yu Lingxiao Yang 《Energy Engineering》 EI 2026年第3期195-214,共20页
To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s... To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates. 展开更多
关键词 Power system regional distributed energy electric vehicle deep reinforcement learning collaborative optimization
暂未订购 下载PDF
Optimal distributed control of Cahn-Hilliard-Brinkman system in three dimension 认领 引用
15
作者 XIAO Xiang-yu PU Zhi-lin 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2026年第2期430-454,共25页
In this paper,we consider the distributed optimal control of the three-dimensional Cahn-Hilliard-Brinkman system with a more general potential.We obtain the regularity results for the weak solution which are essential... In this paper,we consider the distributed optimal control of the three-dimensional Cahn-Hilliard-Brinkman system with a more general potential.We obtain the regularity results for the weak solution which are essential for an associated optimal control problem.We then show that the control-to-state operator is Frechet differentiable and we derive the first-order necessary optimality conditions in terms of a variational inequality involving the adjoint state variables. 展开更多
关键词 distributed optimal control more general potential rst-order necessary optimality conditions the adjoint state variables
暂未订购 下载PDF
Distributed asynchronous double accelerated optimization for ethylene plant considering delays 认领 引用
16
作者 Ting Wang Zhongmei Li Wenli Du 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第2期245-250,共6页
Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the dela... Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm. 展开更多
关键词 Asynchronous distributed optimization Plant-wide optimization Heavy ball Nesterov Inequality constraints
暂未订购 下载PDF
Privacy Distributed Constrained Optimization Over Time-Varying Unbalanced Networks and Its Application in Federated Learning 认领 引用
17
作者 Mengli Wei Wenwu Yu +2 位作者 Duxin Chen Mingyu Kang Guang Cheng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第2期335-346,共12页
This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into accoun... This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs.We hereby propose a novel algorithm, termed the differential privacy(DP) distributed push-sum based zeroth-order constrained optimization algorithm(DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs,offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm(ZOCOA-FL) to address challenges stemming from the timevarying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares(DLS) and decentralized federated learning(DFL) tasks. 展开更多
关键词 Constrained distributed optimization decentralized federated learning(DFL) differential privacy(DP) time-varying unbalanced graphs zeroth-order gradient
暂未订购 下载PDF
Multi-Objective and Multi-Criteria Optimization of Energy Storage Planning in Renewable Distribution Networks 认领 引用
18
作者 Alireza Norouzpour Shahrbejari Nafiseh Pishbin +2 位作者 Mohammad Reza Maghami Mazlan Mohamed Mohammad Golmohammad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期581-619,共39页
This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting,sizing,and scenario-based operation of energy storage systems(ESSs)in renewable-integrated distribution network... This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting,sizing,and scenario-based operation of energy storage systems(ESSs)in renewable-integrated distribution networks.The proposed model concurrently addresses technical,economic,and reliability objectives—minimizing active power losses(PL),voltage deviation(VD),expected energy not supplied(EENS),and short-circuit level(SCL),while maximizing voltage sensitivity index(VSI)and power-loss sensitivity factor(PLSF).A Particle Swarm Optimization(PSO)algorithm with weighted-sum scalarization is employed to solve this complex,nonlinear optimization problem and effectively balance the conflicting operational goals.The framework is validated using IEEE 69-bus and IEEE 118-bus test systems under varying load conditions(20%,50%,100%,and 150%)with time-dependent photovoltaic(PV)and wind turbine(WT)generation profiles.Results demonstrate that the proposed approach achieves significant performance enhancements,reducing power losses by up to 54%,EENS by 88%,and operational cost by 22%while maintaining SCL values within protection limits.Furthermore,the inclusion of ESS units improves system reliability and voltage stability,ensuring smooth operation during load fluctuations and fault conditions.The findings confirm that the proposed weighted-sum multi-criteria optimization framework using PSO provides a scalable and protection-aware solution for integrating ESSs into renewable-rich distribution networks.It offers a robust planning and operational tool for next-generation smart grids,enabling a more efficient,resilient,and sustainable energy ecosystem. 展开更多
关键词 Energy storage systems multi-objective optimization optimal placements distribution networks renewable integration grid reliability这篇论文的主要研究内容是什么?这篇论文的作者单位都有哪些?这篇论文的研究成果有哪些应用?
暂未订购 下载PDF
A Comprehensive Review of Sizing and Allocation of Distributed Power Generation:Optimization Techniques,Global Insights,and Smart Grid Implications 认领 引用 被引量：1
19
作者 Abdullrahman A.Al-Shamma’a Hassan M.Hussein Farh +4 位作者 Ridwan Taiwo Al-Wesabi Ibrahim Abdulrhman Alshaabani Saad Mekhilef Mohamed A.Mohamed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期1303-1347,共45页
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ... Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems. 展开更多
关键词 Systematic and scientometric global trends distributed generation sizing and allocation multiobjectives modelling and algorithmic optimization
暂未订购 下载PDF
Predefined-Time Distributed Optimization for Resource Allocation Problems With Time-Varying Objective Function and Constraints 认领 引用
20
作者 Haotian Wu Yang Liu +1 位作者 Mahmoud Abdel-Aty Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2353-2355,共3页
Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,... Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach. 展开更多
关键词 resource allocation distributed optimization time varying objective function optimal resource allocationthe distributed control protocol time varying constraints predefined time convergence
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈