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.

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.

Control of Distributed Generation Using Non-Sinusoidal Pulse Width Modulation

The islanded mode is one of the connection modes of the grid distributed generation resources.In this study,a distributed generation resource is connected to linear and nonlinear loads via a three-phase inverter where a control method needing no current sensors or compensator elements is applied to the distribute generation system in the islanded mode.This control method has two main loops in each phase.The first loop controls the voltage control loops that adjust the three-phase point of common coupling,the amplitude of the non-sinusoidal reference waveform and the near-state pulse width modulation(NSPWM)method.The next loop compensates the harmonic compensator loop that calculates the voltage harmonics of the point of common coupling in each phase,and injects them to compensate the non-sinusoidal reference waveforms of each phase.The simulation results in MATLAB/SIMULINK show that this method can generate balanced threephase sinusoidal voltage with an acceptable total harmonic distortion(THD)at the joint connection point.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

Research on Formation Mechanisms of Hot Dry Rock Resources in China

As an important geothermal resource, hot dry rock （HDR） reserves have been studied in many countries. HDR resources in China have huge capacity and have become one of the most important resources for the potential replacement of fossil fuels. However, HDR resources are difficult to develop and utilise. Technologies for use with HDR, such as high-temperature drilling, reservoir characterisation, reservoir fracturing, microseismic monitoring and high-temperature power stations, originate from the field of oil and drilling. Addressing how to take advantage of these developed technologies is a key factor in the development of HDR reserves. Based on the thermal crustal structure in China, HDR resources can be divided into four types： high radioactive heat production, sedimentary basin, modern volcano and the inner-plate active tectonic belt. The prospective regions of HDR resources are located in South Tibet, West Yunnan, the southeast coast of China, Bohai Rim, Songliao Basin and Guanzhong Basin. The related essential technologies are relatively mature, and the prospect of HDR power generation is promising. Therefore, analysing the formation mechanisms of HDR resources and promoting the transformation of technological achievements, large-scale development and the utilisation of HDR resources can be achieved in China.

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.

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.

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.

A UDDI Based Shared Resources Management Mechanism in Virtual Enetrprises

Service and information resources are two kinds of major shared resources in virtual enterprises, where, service resources include third party’s service resources and enterprise’s legacy service resources,and information resources include database resources and file resources. Since service and information resources have distributed, autonomic, remotely bound as well as some other similar characteristics, an implementation mechanism on managing shared resources based on UDDI are presented. XML instances are used to describe resources authorization information, WSDL based special XML instance represents information resource metadata, and these XML instances are referenced by tModel for realizing the purpose of managing shared resources metadata based on UDDI.

Supply modes for renewable-based distributed energy systems and their applications:case studies in China

Distributed energy systems(DES),as an integrated energy system with coupled distributed energy resources,have great potential in reducing carbon dioxide emissions and improving energy efficiencies.Considering the background of urbanization and the energy revolution in China,the study investigates the renewable-based DESs supply modes and their application in China.A new method is proposed to classify DESs supply modes into three categories considering the renewable resource in domination,and their application domains are discussed.A comprehensive model is given for economic and environmental evaluation.Typical case studies show that the renewable-based DES systems can supply the energy in a cost-effective and environment-friendly way.Among them,the biomass waste dominated supply mode can not only achieve"zero"carbon emissions but also"zero"energy consumption,even though not yet economically attractive under the present policy and market conditions.Thus,recommendations are given to promote the further deployment of renewable-based DESs,regarding their supply modes,policy requirements,and issues to be addressed.

The concept and the accumulation characteristics of unconventional hydrocarbon resources

Unconventional hydrocarbon resources, which are only marginally economically explored and developed by traditional methods and techniques, are different from conventional hydrocarbon resources in their accumulation mechanisms, occurrence states, distribution models, and exploration and development manners. The types of unconventional hydrocarbon are controlled by the evolu- tion of the source rocks and the combinations of different types of unconventional reservoirs. The fundamental dis- tinction between unconventional hydrocarbon resources and conventional hydrocarbon resources is their non- buoyancy-driven migration. The development of the micro- to nano-scale pores results in rather high capillary resis- tance. The accumulation mechanisms of the unconven- tional and the conventional hydrocarbon resources are also greatly different. In conventional hydrocarbon resources, oil and gas entrapment is controlled by reservoir-forming factors and geological events, which is a dynamic balance process; while for unconventional hydrocarbon resources, the gas content is affected by the temperature and pressure fields, and their preservation is crucial. Unconventional and conventional hydrocarbons are distributed in an orderly manner in subsurface space, having three distribution models of intra-source rock, basin-centered, and source rock interlayer. These results will be of great significance to unconventional hydrocarbon exploration.

Distributed Human Resource Redistribution System Based on Agent and Ontology

A computer system for human resource （HR） redistribution system is presented to solve the balance problem of the ＂surplus resources＂ and ＂surplus tasks＂ among a group of project units. The system architecture is designed in a compositional manner using the elements of agent technology and knowledge technology. A combination of generic agent models, ontology and knowledge provides an effective approach to address the dynamic, distributed and knowledge-intensive characters of the HR management. In the system, the broker agent acting as intermediary provides matchmaking services to the domain agents, and the individual domain agents communicate directly with each other. The HR ontology provides the semantic match of the surplus task and the surplus resource. Finally, an application example is presented to illustrate the achieved solution for a concrete scenario. This novel way offers a comprehensive HR exchange solution and is snitablc for both intra-organizational and inter-organizational HR management.

Distributed Chunk-Based Optimization for MultiCarrier Ultra-Dense Networks

In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.

Security of the Decoy State Two-Way Quantum Key Distribution with Finite Resources

The quantum key distribution （QKD） allows two parties to share a secret key by typically making use of a one-way quantum channel. Howevery the two-way QKD has its own unique advantages, which means the two-way QKD has become a focus recently. To improve the practieM performance of the two-way QKD, we present a security analysis of a two-way QKD protocol based on the decoy method with heralded single-photon sources （HSPSs）. We make use of two approaches to calculate the yield and the quantum bit error rate of single-photon and two-photon pulses. Then we present the secret key generation rate based on the GLLP formula. The numerical simulation shows that the protocol with HSPSs has an advantage in the secure distance compared with weak coherent state sources. In addition, we present the final secret key by considering the statistical fluctuation of the yield generation rate of the LM05 protocol with finite resources and the error rate.

A Distributed User Association and Resource Allocation Method in Cache-Enabled Small Cell Networks

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5 G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming(MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

The Major Ore Clusters of Super-Large Iron Deposits in the World, Present Situation of Iron Resources in China, and Prospect

The metamorphosed sedimentary type of iron deposits（BIF） is the most important type of iron deposits in the world, and super-large iron ore clusters of this type include the Quadrilatero Ferrifero district and Carajas in Brazil, Hamersley in Australia, Kursk in Russia, Central Province of India and Anshan-Benxi in China. Subordinated types of iron deposits are magmatic, volcanic-hosted and sedimentary ones. This paper briefly introduces the geological characteristics of major super-large iron ore clusters in the world. The proven reserves of iron ores in China are relatively abundant, but they are mainly low-grade ores. Moreover, a considerate part of iron ores are difficult to utilize for their difficult ore dressing, deep burial or other reasons. Iron ore deposits are relatively concentrated in 11 metallogenic provinces（belts）, such as the Anshan-Benxi, eastern Hebei, Xichang-Central Yunnan Province and middle-lower reaches of Yangtze River. The main minerogenetic epoches vary widely from the Archean to Quaternary, and are mainly the Late Archean to Middle Proterozoic, Variscan, and Yanshanian periods. The main 7 genetic types of iron deposits in China are metamorphosed sedimentary type（BIF）, magmatic type, volcanic-hosted type, skarn type, hydrothermal type, sedimentary type and weathered leaching type. The iron-rich ores occur predominantly in the skarn and marine volcanic-hosted iron deposits, locally in the metamorphosed sedimentary type（BIF） as hydrothermal reformation products. The theory of minerogenetic series of mineral deposits and minerogenic models has applied in investigation and prospecting of iron ore deposits. A combination of deep analyses of aeromagnetic anomalies and geomagnetic anomalies, with gravity anomalies are an effective method to seeking large and deep-buried iron deposits. China has a relatively great oresearching potential of iron ores, especially for metamorphosed sedimentary, skarn, and marine volcanic-hosted iron deposits. For the lower guarantee degree of iron and steel industry, China should give a trading and open the foreign mining markets.

Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.

Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks

As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch problem.However,these RL based methods are typically formulated without safety guarantees,which hinders their application in real world.In this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online execution.In the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed actions.Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.