Spatial and Temporal Distribution Characteristics of Solar Energy Resources in Tibet

The Tibet Plateau is one of the regions with the richest solar energy resources in the world.In the process of achieving carbon neutrality in China,the development and utilization of solar energy resources in the region will play an important role.In this study,the gridded solar resource data with 1km resolution in Tibet were obtained by spatial correction and downscaling of SMARTS model.On this basis,the spatial and temporal distribution characteristics of solar energy resources in the region in the past 30 years(1991–2020)are finely evaluated,and the annual global horizontal radiation resource is calculated.The results show that:1)The average annual global horizontal radiation amount in Tibet is 1816 kWh/m^(2).More than 60%of the area belongs to the“Most abundant”(GHI≥1750 kWh/m^(2))area of China’s solar energy resources category A,and nearly 40%belongs to the“Quite abundant”(1400≤GHI<1750)area of China’s solar energy resource category B.2)In space,the solar energy resources in Tibet increased gradually from north to south and from east to west.Lhasa,Central and Eastern Shigatse,Shannan,and Southwestern Ali are the most abundant cities,with a maximum annual radiation level of 2189 kWh/m2.3)In terms of time,the total horizontal radiation in Tibet was the highest in May and the lowest in December.74%of the total area belongs to the“Very stable”(R_(w)≥0.47)area of solar resource stability category A,and 26%belongs to the“stable”(0.36≤R_(w)<0.47)area of solar resource stability category B.Solar energy resources in the region show the characteristics of both strong and stable.Average solar energy resources in the region have shown a fluctuating downward trend over the past 30 years,with an average decline of about 12.86(kWh/m2)per decade.4)In terms of solar radiation resources reaching the earth’s surface,the theoretical total amount of annual horizontal radiation in Tibet is about 240.07 billion tons of standard coal or 222.91 billion kilowatts on average.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Multi-agent Based Protection on Highly Dominated Distributed Energy Resources

Highly penetration of Distributed Energy Resources (DER) on the grid systems nowadays makes the systems grow dynamically. The system become more complex and the protection system become more complicated. The protection relay should accommodate the system changes according to the system conditions and topologies. As part of developmental aspect of Distributed Artificial Intelligent, Multi Agent System (MAS) is a challenging method for improving the intelligent properties of relay protection. This paper introduces the use of MAS approach on radial distribution system protection dominated with DER using dispersed adaptive rule-based protection supported by distributed database agent. The simulation results confirmed that the proposed algorithm can respond within 15.05 ms.

An IEEE 1547-Based Power Conditioner Test System for Distributed Energy Resources

Power conditioner, that is responsible for electric power conversion, is a critical component used in many renewable energy power generation systems. Most of the electric power produced by distributed energy resources cannot directly import to utility network without power conversion. Meanwhile, power conversion may includes several different types, for example AC/DC, and DC/AC, which is realized by a variety types of power conditioners in the electric power system. Currently, many concerns are focused on the operation of these power conditioners used in distributed energy resources due to the worse designing may cause the terrible influence on safety and performance characteristic of distributed energy resources. The power quality and reliability of interconnected electric power network may be affected as well. In the view of this, IEEE standards board provides a uniform standard for interconnection of distributed resources with electric power systems. It provides requirements relevant to the performance, operation, testing, safety considerations, and maintenance of the interconnection. Based on the IEEE 1547 standard, this paper presents a test system for power conditioners that are used in distributed energy resources or other renewable energy applications. Some of the test items that described in IEEE 1547.1 relevant to interconnection issues can be realized by proposed test system.

Distributed versus Lumped Optimization of Cropping Pattern and Water Resources Utilization

Several whole-farm agro-economic optimization models have been developed to deal with lumped planning issues in the agriculture sector. However, these models cannot be used to devise appropriate management strategies at land parcel level, because of the differences between farm characteristics, and the increased complexity of the hydrological processes. Based on Spatial Farm Database (SFD) which is consisted of a number of farm-level spatial data, including location, paddock properties, owner specifications and budgets, it is possible to provide the farm manager with some suggestions regarding the optimal choice of crops and the area to be allocated for each one. To this end, genetic algorithm is used in order to cope with model nonlinearity and a large number of decision variables. In order to test the proposed model, the Mobarakabad district is modeled with 126 agriculture fields, and the optimization model is run for this area. Results showed that the optimization procedure can find more realistic farm-level optimal solutions due to its advantage in adequate modeling of field characteristics, common groundwater resources, and the associated constraints. The results of lumped optimizations could also be used as benchmarks for the purposes of comparison and interpretation.

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

Resources Allocation and Failures in Step Topology under Distributed Computing System

In the past years, distributed computing is gaining the popularity due to reduction in execution time and low cost involvement. On the basis of this, Mobile Adhoc Network (MANET) is also increasing worldwide with major advantage that it has no involvement of wire and transfer of data can be done by the virtual paths if the existing path is congested. In the present work, MANET is considered in the form of step topology which consists of heterogeneous collection of the devices. The work demonstrates the resources allocation for execution of tasks and it consists of selection of right path if the link failures and by pass link failures. It also consists of the resource management over the new proposed step topology. Entire work is modeled with the help of well known modeling language known as Unified Modeling Language (UML) and model demonstrates the resources allocation for execution of the tasks.

Types and Spatial Distribution of Geo-tourism Resources in Jilin Province of China

Natural geological conditions and geo-tourism resources in Jilin Province were introduced,and distribution features of the local major tourist resources(vegetation-covered eastern region,grass swamp on western plain) were studied.Jingyu Volcanic Mineral Spring Geo-park,Changbai Mountain Geo-park and Qian'an Mud Forest Geo-park were studied as typical geo-tourism resources,so as to provide basic data for the systematic development and construction of geo-tourism resources in Jilin Province.

Regional Distribution and Sustainable Development Strategy of Mineral Resources in China

This paper summarizes the distribution and production layout of the major mineral resources in China,including coal,iron ore,copper and bauxite,from a national perspective.It also identifies the incompatibility between the mineral resources distribution and regional economic development.Significant issues with China's mineral resource industry cause challenges for the sustainable development of both the mining industry and the national socio-economy.The sustainability of regional mineral resources and the environmental pollution by mining in the western China were also analyzed.Results show that the distribution of China's mineral resources is misaligned with its regional layout of economic development.China's mineral resources have been over-exploited,and the mineral resources production in the eastern China is unsustainable.The continuously expanding production of mineral resources in the western China has heavily endangered the ecological environment.We propose strategies to boost the sustainable development of mineral resources,including measures to accelerate economic development and enhance the sustainability of domestic mineral resources.We also offer suggestions for scientifically planning the mineral resource prospecting and exploitation and regional economic layout,as well as for proactively undertaking industry transfer in the eastern China and raising the environmental benchmark requirements for the mineral industry in the central and western China.

Distribution Characteristics of Cobalt-rich Ferromanganese Crust Resources on Submarine Seamounts in the Western Pacific

Based on the survey data of five submarine seamount provinces （chains） in the Western Pacific, the distribution characteristics of cobalt-rich ferromanganese crust resources have been researched in this paper by using the relative reference data and applying the theories of hotspot and seafloor spreading. The main research results obtained are as follows： The Co-rich crust thickness in the study area is gradually increasing from east to west and from south to north having a negative correlation （r = -0.59） with longitude and a positive correlation （r = 0.48） with latitude. The crust thickness varying along longitude and latitude is influenced by the hotspot and seafloor spreading. The oceanic crusts and seamounts in the northwest part of the study area are older, and the crust resources are superior to those in the southeast part. In the depth of 〈1500 m, 1500-2000 m, 2000-2500 m in the study area, the cobalt crust thickness is respectively 5.45 cm, 4.34 cm and 3.55 cm, and in the depth of 2500-3000 m and 3000-3500 m, it drops respectively to 2.84 cm and 3.37 cm. The Co-rich crust resources are mainly concentrated in the seamount summit margins and the upper flanks in the depth of 〈2500 m. There is a strong negative correlation （r = -0.67） between the cobalt crust abundance and the slope of the seamount, 75 kg/m^2 and 50 kg/mz at the slopes of 0°-20° and 20°-34° respectively. Cobalt crusts are mainly distributed in the parts whose slopes are less than 20°. It is consistent with the fractal result that the slope threshold of cobalt crust distribution is 19°, and slopes over 20° are not conducive to the crust growth. The cobalt crusts of high grade are mainly enriched in the region within 150°E-140°W and 30°S-30°N in the Pacific, where there are about 587 seamounts at the depth of 3500- 6000 m and over 30 Ma of the oceanic crusts. The perspective area rich in cobalt crust resources is about 41×104 km^2 and the resource quantity is approximately 27 billion tons.

Temporal and spatial distribution characteristics of water resources in Guangdong Province based on a cloud model

With a focus on the difficulty of quantitatively describing the degree of nonuniformity of temporal and spatial distributions of water resources, quantitative research was carried out on the temporal and spatial distribution characteristics of water resources in Guangdong Province from 1956 to 2000 based on a cloud model. The spatial variation of the temporal distribution characteristics and the temporal variation of the spatial distribution characteristics were both analyzed. In addition, the relationships between the numerical characteristics of the cloud model of temporal and spatial distributions of water resources and precipitation were also studied. The results show that, using a cloud model, it is possible to intuitively describe the temporal and spatial distribution characteristics of water resources in cloud images. Water resources in Guangdong Province and their temporal and spatial distribution characteristics are differentiated by their geographic locations. Downstream and coastal areas have a larger amount of water resources with greater uniformity and stronger stability in terms of temporal distribution. Regions with more precipitation possess larger amounts of water resources, and years with more precipitation show greater nonuniformity in the spatial distribution of water resources. The correlation between the nonuniformity of the temporal distribution and local precipitation is small, and no correlation is found between the stability of the nonuniformity of the temporal and spatial distributions of water resources and precipitation. The amount of water resources in Guangdong Province shows an increasing trend from 1956 to 2000, the nonuniformity of the spatial distribution of water resources declines, and the stability of the nonuniformity of the spatial distribution of water resources is enhanced.

Trusted Data Acquisition Mechanism for Cloud Resource Scheduling Based on Distributed Agents

Goud computing is a new paradigm in which dynamic and virtualized computing resources are provided as services over the Internet. However, because cloud resource is open and dynamically configured, resource allocation and scheduling are extremely important challenges in cloud infrastructure. Based on distributed agents, this paper presents trusted data acquisition mechanism for efficient scheduling cloud resources to satisfy various user requests. Our mechanism defines, collects and analyzes multiple key trust targets of cloud service resources based on historical information of servers in a cloud data center. As a result, using our trust computing mechanism, cloud providers can utilize their resources efficiently and also provide highly trusted resources and services to many users.

Distributed Resource Allocation in Ultra-Dense Networks via Belief Propagation

Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes promise huge performance improvement at the cost of cooperation among base stations,the large numbers of user equipment and base station make jointly optimizing the available resource very challenging and even prohibitive. How to decompose the resource allocation problem is a critical issue. In this paper,we exploit factor graphs to design a distributed resource allocation algorithm for ultra dense networks,which consists of power allocation,subcarrier allocation and cell association. The proposed factor graph based distributed algorithm can decompose the joint optimization problem of resource allocation into a series of low complexity subproblems with much lower dimensionality,and the original optimization problem can be efficiently solved via solving these subproblems iteratively. In addition,based on the proposed algorithm the amounts of exchanging information overhead between the resulting subprob-lems are also reduced. The proposed distributed algorithm can be understood as solving largely dimensional optimization problem in a soft manner,which is much preferred in practical scenarios. Finally,the performance of the proposed low complexity distributed algorithm is evaluated by several numerical results.

Optimal distributed resource allocation in a wireless sensor network for control systems

Wireless technology is applied increasingly in networked control systems. A new form of wireless network called wireless sensor network can bring control systems some advantages, such as flexibility and feasibility of network deployment at low costs, while it also raises some new challenges. First, the communication resources shared by all the control loops are limited. Second, the wireless and multi-hop character of sensor network makes the resources scheduling more difficult. Thus, how to effectively allocate the limited communication resources for those control loops is an important problem. In this paper, this problem is formulated as an optimal sampling frequency assignment problem, where the objective function is to maximize the utility of control systems, subject to channel capacity constraints. Then an iterative distributed algorithm based on local buffer information is proposed. Finally, the simulation results show that the proposed algorithm can effectively allocate the limited communication resource in a distributed way. It can achieve the optimal quality of the control system and adapt to the network load changes.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

A new quantum key distribution resource allocation and routing optimization scheme

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

Collaborative Multiple Access And Energy-Efficient Resource Allocation in Distributed Maritime Wireless Networks

With the rapid increasing of maritime activities, maritime wireless networks(MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper,we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access(RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency(EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover,the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

Resource Allocation based on Shared Criterion in OFDMA Distributed Radio Access Network

Distributed radio access network (DRAN) is a novel wireless access architecture and can solve the problem of the available spectrum scarcity in wireless communications. In this paper, we investigate resource allocation for the downlink of OFDMA DRAN. Unlike previous exclusive criterion based algorithms that allocate each subcarrier to only one user in the system, the proposed algorithms are based on shared criterion that allow each subcarrier to be allocated to multiple users through different antennas and to only one user through same antenna. First, an adaptive resource allocation algorithm based on shared criterion is proposed to maximize total system rate under each user's minimal rate and each antenna's maximal power constraints. Then we improve the above algorithm by considering the influence of the resource allocation scheme on single user. The simulation results show that the shared criterion based algorithm provide much higher total system rate than that of the exclusive criterion based algorithm at the expense of the outage performance and the fairness, while the improved algorithm based on shared criterion can achieve a good tradeoff performance.

Energy Economic Dispatch for Photovoltaic-Storage via Distributed Event-Triggered Surplus Algorithm

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

In this paper,accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network,which enables a hyper-exponential convergence rate.Specifically,an inertial fast-slow dynamical system with vanishing damping is introduced,based on which the distributed saddle point algorithm is designed.The dual variables are updated in two time scales,i.e.,the fast manifold and the slow manifold.In the fast manifold,the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol.In the slow manifold,the updating of the Lagrangian multipliers is accelerated by inertial terms.Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation.The simulation of the application in the energy dispatch problem verifies the result,which demonstrates the fast convergence of the proposed saddle point dynamics.