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Resource Allocation for Space-Air-Ground Integrated Networks:A Comprehensive Review

Journal of Communications and Information Networks 2024年第1期9卷 1-23页

作者： Hui Liang Zhiqing Yang Guobin Zhang Hanxu Hou school of electrical engineering and intelligentization Dongguan University of TechnologyDongguan 523808China Peng Cheng Laboratory Shenzhen 518055China school of Electronics and Information engineering Shenzhen UniversityShenzhen 518061China school of electrical engineering and intelligentization Dongguan University of TechonologyDongguan 523808China school of Information and Communication engineering Guangzhou Maritime UniversityGuangzhou 510725China

The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.

关键词： space-air-ground integrated networks resource allocation 6G optimization theory game theory artificial intelligence

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Statistical QoS Provisioning Architecture for 6G Satellite-Terrestrial Integrated Networks

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Journal of Communications and Information Networks 2024年第1期9卷 34-42页

作者： Jingqing Wang Wenchi Cheng Wei Zhang Hui Liang school of Telecommunications engineering Xidian UniversityXi’an 710071China school of electrical engineering and Telecommunications the University of New South WalesSydneyAustralia school of electrical engineering and intelligentization Dongguan University of TechnologyDongguan 523808China Peng Cheng Laboratory Shenzhen 518055China.

The emergence of massive ultra-reliable and low latency communications (mURLLC) as a category of age/time/reliability-sensitive service over 6G wireless networks has received considerable research attention, which has presented unprecedented challenges. As one of the key enablers for 6G,satellite-terrestrial integrated networks (STIN) have been developed to offer more expansive connectivity and comprehensive 3D coverage in space-aerial-terrestrial domains for supporting 6G mission-critical mURLLC applications while fulfilling diverse and rigorous quality of service (QoS) requirements. In the context of these mURLLC-driven satellite services, data freshness assumes paramount importance, as outdated data may engender unpredictable or catastrophic outcomes.To effectively measure data freshness in satellite-terrestrial integrated communications,age of information(AoI)has recently surfaced as a new dimension of QoS metric to support time-sensitive applications. It is crucial to design new analytical models that ensure stringent and diverse QoS metrics bounded by different key parameters,including AoI,delay,and reliability,over 6G satellite-terrestrial integrated networks. However,due to the complicated and dynamic nature of satellite-terrestrial integrated network environments, the research on efficiently defining new statistical QoS provisioning schemes while taking into account varying degrees of freedom has still been in their infancy. To remedy these deficiencies, in this paper we develop statistical QoS provisioning schemes over 6G satellite-terrestrial integrated networks in the finite blocklength regime. Particularly, we firstly introduce and review key technologies for supporting mURLLC.Secondly,we formulate a number of novel fundamental statistical-QoS metrics in the finite blocklength regime.Finally,we conduct a set of simulations to validate and evaluate our developed statistical QoS provisioning schemes over satellite-terrestrial integrated networks.

关键词： statistical QoS provisioning mURLLC peak AoI satellite-terrestrial integrated networks QoS exponents hybrid automatic repeat request finite blocklength coding

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Vortex Quantum Droplets under Competing Nonlinearities

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Chinese Physics Letters 2024年第2期41卷 1-9页

作者：陈桂华王红成邓海明 Boris A.Malomed school of Electronic engineering&intelligentization Dongguan University of TechnologyDongguan 523808China school of Physics and Electronic-electrical engineering Xiangnan UniversityChenzhou 423000China Instituto de Alta Investigación Universidad de TarapacáCasilla 7DAricaChile

This concise review summarizes recent advancements in theoretical studies of vortex quantum droplets(VQDs)in matter-wave fields.These are robust self-trapped vortical states in two-and three-dimensional(2D and 3D)Bose–Einstein condensates(BECs)with intrinsic nonlinearity.Stability of VQDs is provided by additional nonlinearities resulting from quantum fluctuations around mean-field states,often referred to as the Lee–Huang–Yang(LHY)corrections.The basic models are presented,with emphasis on the interplay between the mean-field nonlinearity,LHY correction,and spatial dimension,which determines the structure and stability of VQDs.We embark by delineating fundamental properties of VQDs in the 3D free space,followed by consideration of their counterparts in the 2D setting.Additionally,we address stabilization of matter-wave VQDs by optical potentials.Finally,we summarize results for the study of VQDs in the single-component BEC of atoms carrying magnetic moments.In that case,the anisotropy of the long-range dipole-dipole interactions endows the VQDs with unique characteristics.The results produced by the theoretical studies in this area directly propose experiments for the observation of novel physical effects in the realm of quantum matter,and suggest potential applications to the design of new schemes for processing classical and quantum information.

关键词： quantum correction dipole

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Analysis of China's Electricity Price and Electricity Burden of Basic Industries under the Carbon Peak Target before 2030

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 481-491页

作者： Yue Qiu Suyang Zhou Wei Gu Xiao-Ping Zhang school of electrical engineering Southeast UniversityNanjingChina school of Electronic Electrical and System EngineeringUniversity of BirminghamU.K.

The Chinese government is deepening reformation of electricity prices during the 14th Five Year Plan period and has set a carbon emission reduction target of reaching carbon peak before 2030.In this context,will the carbon emission target influence electricity pricing and will electricity price influence competitiveness of Chinese main industries are two questions needing to be answered.This paper compares China's electricity price level with the selected major countries in the world,and four typical industries are selected to evaluate their electricity burden respectively.Then,the correlation between residential electricity price and industrial electricity price and the influencing factors is analyzed,from the perspectives of scale,structure and technology.According to the model obtained by regression analysis,the electricity price level and corresponding residential and industrial electricity burden in 2025 and 2030 are forecasted.Index Terms-Electricity burden,industrial electricity price,regression analysis,residential electricity price.

关键词： Electricity burden industrial electricity price regression analysis residential electricity price

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A Projection and Decomposition Approach for Multi-agent Coordinated Scheduling in Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 991-996页

作者： Haifeng Qiu Zhigang Li Hongjun Gao Hung Dinh Nguyen Veerapandiyan Veerasamy Hoay Beng Gooi IEEE the school of electrical and Electronic engineering Nanyang Technological University school of Electric Power engineering South China University of Technology the College of electrical engineering Sichuan University

Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty, a generic projection and decomposition(P&D) approach is proposed in this letter. The canonical min-max-min two-stage robust optimization(TSRO) model with coupling constraints is equivalent to a concise robust optimization(RO) model in the version of mixed-integer linear programming(MILP) via feasible region projection. The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm, which ensures the fast convergence to a high-quality solution in the distributed optimization. Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

关键词： Uncertainty Couplings Power systems Optimization Computational modeling Convergence Processor scheduling

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Coordinated Dispatch Based on Distributed Robust Optimization for Interconnected Urban Integrated Energy and Transmission Systems

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 840-851页

作者： Wei Xu Yufeng Guo Tianhui Meng Yingwei Wang Jilai Yu the school of electrical engineering and Automation Harbin Institute of Technology

To improve the economic efficiency of urban integrated energy systems(UIESs) and mitigate day-ahead dispatch uncertainty, this paper presents an interconnected UIES and transmission system(TS) model based on distributed robust optimization. First, interconnections are established between a TS and multiple UIESs, as well as among different UIESs, each incorporating multiple energy forms. The Bregman alternating direction method with multipliers(BADMM) is then applied to multi-block problems, ensuring the privacy of each energy system operator(ESO). Second, robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty. The column and constraint generation(C&CG) algorithm is then employed to solve the robust model. Third, to tackle the convergence and practicability issues overlooked in the existing studies, an external C&CG with an internal BADMM and corresponding acceleration strategy is devised. Finally, numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.

关键词： Heating systems Convex functions Optimization Uncertainty Pipelines Distributed algorithms Convergence

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Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 719-729页

作者： Qing Ma Changhong Deng IEEE the school of electrical engineering and Automation Wuhan University

Volt-var control(VVC) is essentially a non-convex optimization problem due to the non-convexity of power flow(PF) constraints, resulting in the difficulty in obtaining the optimum without convexity conversion. The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables, which in turn increases the optimization difficulty or even leads to optimization failure. This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF). This method uses the input convex neural network(ICNN) to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment, which can ensure the global optimum with extremely fast computation speed. To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC, this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF), which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval. Combining DLIPF with DLPF, this method decreases the modeling and optimization difficulty of robust VVC significantly. Test results on 30-bus, 118-bus, and 200-bus systems prove the correctness and rapidity of the proposed methods.

关键词： Optimization Reactive power Mathematical models Deep learning Uncertainty Load flow Programming

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Day-ahead Voltage-stability-constrained Network Topology Optimization with Uncertainties

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 730-741页

作者： Dingli Guo Lei Wang Ticao Jiao Ke Wu Wenjing Yang the school of electrical and Electronic engineering Shandong University of Technology

A day-ahead voltage-stability-constrained network topology optimization(DVNTO) problem is proposed to find the day-ahead topology schemes with the minimum number of operations(including line switching and bus-bar splitting) while ensuring the sufficient hourly voltage stability margin and the engineering operation requirement of power systems. The AC continuation power flow and the uncertainty from both renewable energy sources and loads are incorporated into the formulation. The proposed DVNTO problem is a stochastic, largescale, nonlinear integer programming problem. To solve it tractably, a tailored three-stage solution methodology, including a scenario generation and reduction stage, a dynamic period partition stage, and a topology identification stage, is presented. First, to address the challenges posed by uncertainties, a novel problem-specified scenario reduction process is proposed to obtain the representative scenarios. Then, to obtain the minimum number of necessary operations to alter the network topologies for the next 24-hour horizon, a dynamic period partition strategy is presented to partition the hours into several periods according to the hourly voltage information based on the voltage stability problem. Finally, a topology identification stage is performed to identify the final network topology scheme. The effectiveness and robustness of the proposed three-stage solution methodology under different loading conditions and the effectiveness of the proposed partition strategy are evaluated on the IEEE 118-bus and 3120-bus power systems.

关键词： Power system stability Switches Uncertainty Stability criteria Network topology Topology Renewable energy sources

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Optimal Operation Strategy Analysis with Scenario Generation Method Based on Principal Component Analysis,Density Canopy,and K-medoids for Integrated Energy Systems

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 89-100页

作者： Bingtuan Gao Yunyu Zhu Yuanmei Li school of electrical engineering Southeast UniversityNanjingChina

The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accuracy.The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.

关键词： Scenario generation principal component analysis(PCA) density canopy K-medoids integrated energy system

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Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS

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Global Energy Interconnection 2024年第1期7卷 14-24页

作者： Bin Zhang Yihui Xia Xiaotao Peng school of electrical engineering Naval University of EngineeringWuhan 430000P.R.China school of electrical engineering Wuhan UniversityWuhan 430072P.R.China

Integrated energy systems(IESs)can improve energy efficiency and reduce carbon emissions,essential for achieving peak carbon emissions and carbon neutrality.This study investigated the characteristics of the CHP model considering P2G and carbon capture systems,and a two-stage robust optimization model of the electricity-heat-gascold integrated energy system was developed.First,a CHP model considering the P2G and carbon capture system was established,and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived,which proved that the model could weaken the electric-thermal coupling characteristics,increase the electric power regulation range,and reduce carbon emissions.Subsequently,a two-stage robust optimal scheduling model of an IES was constructed,in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs.The objective function in the real-time scheduling stage was to minimize the equipment operating costs,carbon emission costs,wind curtailment,and solar curtailment costs,considering multiple uncertainties.Finally,after the objective function is linearized with a ψ-piecewise method,the model is solved based on the C&CG algorithm.Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

关键词： Combined heat and power Power-to-gas Carbon capture system Integrated energy system Robust optimization

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