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.

SCHEDULING ALGORITHM BASED ON FOLLOW-UP SHARING CHARACTER FOR POST-EVENT RESPONSE RESOURCE DISTRIBUTION IN LARGE-SCAL DISASTERS

Post-event response planners must develop effective and efficient plans for the proper allocation and distribution of resources to impacted areas （IAs） within a critical time window. To determine the effectiveness and efficiency of distribution plans, this study addresses resource allocation effectiveness losses （RAEL, or losses caused by the mismatch between supply and demand in IAs） and emergency logistics time costs （ELTC, or transportation time of logistics processes under emergency conditions）. Moreover, this study examines a follow-up sharing character （FSC） that coordinates resources among different phases. This research proposes an integrated model （IM） based on this character. This model aims to minimize RAEL and ELTC. Furthermore, the IM combines the time dimension model （TDM）, which coordinates the demands and supplies of all phases in the planning horizon, and the space dimension model （SDM）, which generates a specific distribution plan for the first phase. An analytical solution is obtained for the TDM as per the definition of FSC, after which the SDM is solved through a single-objective linear programming model. After solving the IM effectively, we fred that the proposed methodology fits the emergency circumstance well. Insights derived from the model are also presented in the conclusion.）

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.

Distribution,Origin and Development Status of Underground Brine Resource along the Coast of Laizhou Bay,Shandong Province

The underground brine resource along the coast of Laizhou Bay is the world’s rare shallow underground brine resource,which occurs in the Laizhou city in the east,and extends to the coastal area of the Binzhou city in

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.

Distribution and Path of Red Resource in Yuexiu District of Guangzhou City

Guangzhou has abundant red cultural resources, and many historical events took place here,such as the Third National Congress of the Communist Party of China, the first Kuomingtang-Communist Party cooperation, and Guangzhou Uprising. Yuexiu District is the region with the most abundant and concentrated red resources in Guangzhou, covering an area of 33.8 km;. There are 31 red historical sites and 23 patriotic education bases in the district, which record the activities of the Communist Party of China, and are of great significance for tracing history and patriotic education. In this paper, the red resources in Yuexiu District are classified and sorted out. Based on the distribution and protection status of the red resources, the path planning of the red resources in Yuexiu District is made to contribute to the development of red tourism.

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.

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.

Investigation on the Resources of Paris Polyphylla var.yunnanensis

In this study,the resource distribution,morphological characteristics,artificial cultivation and market circulation of Paris Polyphylla var.yunnanensis and its polygerm varieties in Yunnan were investigated by referring to the research literature,field investigation and our planting experience.It is found that P.polyphylla var.yunnanensis is widely distributed in Yunnan Province,realizing large-scale artificial cultivation,while the wild resources of polygerm varieties are almost on the verge of extinction and are in urgent need of protection.

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.

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.

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.

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.

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.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Quantifying Contribution of DER-Integrated EV Parking Lots to Reliability of Power Distribution Systems

In the future smart cities,parking lots(PLs)can accommodate hundreds of electric vehicles(EVs)at the same time.This trend creates an opportunity for PLs to serve as a potential flexibility resource,considering growing penetration of EVs and integration of distributed energy resources DER(such as photovoltaic and energy storages).Given this background,this paper proposes a comprehensive evaluation framework to investigate the potential role of DER-integrated PLs(DPL)with the capability of vehicle-to-grid(V2G)in improving the reliability of the distribution network.For this aim,first,an overview for the distribution system with DPLs is provided.Then,a generic model for the available generation capacity(AGC)of DPLs with consideration of EV scheduling strategy is developed.On the above basis,an iterative-based algorithm leveraging sequential Monte Carlo simulation is presented to quantify the contribution of DPLs to the reliability of the system.In order to verify the effectiveness of the proposed method,a series of numerical studies are carried out.The simulation results show that the integration of DPLs with the V2G capability could help to improve the reliability performance of distribution grid to a great extent and reduce the adverse impact incurred by EV accommodation,if utilized properly.

Optimal Intelligent Reconfiguration of Distribution Network in the Presence of Distributed Generation and Storage System

In the present paper,the distribution feeder reconfiguration in the presence of distributed generation resources(DGR)and energy storage systems(ESS)is solved in the dynamic form.Since studies on the reconfiguration problem have ignored the grid security and reliability,the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem.To achieve the mentioned benefits,there are several practical plans in the distribution network.One of these applications is the network rearrangement plan,which is the simplest and least expensive way to add equipment to the network.Besides,by adding the DGRs to the distribution grid,the radial mode of the grid and the one-sided passage of power are eliminated,and the ordinary and simple grid is replaced with a complex grid.In this paper,an improved particle clustering algorithm is used to solve the distribution network rearrangement problem with the presence of distributed generation sources.The PQ model and the PV model are both considered,and for this purpose,a model based on the compensation technique is used to model the PV busbars.The proposed developed model has particularly improved the local and global search of this algorithm.The reconfiguration problem is discussed and investigated considering different scenarios in a standard 33-bus grid as a well-known power system in different scenarios in the presence and absence of the DGRs.Then,the obtained results are compared.

An Integrated Image Task Planning in Satellite Networks:From Instruction Release and Observation Perspective

The unreasonable observation arrangements in the satellite operation control center(SOCC)may result in the observation data cannot be downloaded as scheduled.Meanwhile,if the operation instructions released by the satellite telemetry tracking center(STTC)for the on-board payloads are not injected on the specific satellites in time,the corresponding satellites cannot perform the observation operations as planned.Therefore,there is an urgent need to design an integrated instruction release,and observation task planning(I-IRO-TP)scheme by efficiently collaborating the SOCC and STTC.Motivated by this fact,we design an interaction mechanism between the SOCC and the STTC,where we first formulate the I-IRO-TP problem as a constraint satisfaction problem aiming at maximizing the number of completed tasks.Furthermore,we propose an interactive imaging task planning algorithm based on the analysis of resource distribution in the STTC during the previous planning periods to preferentially select the observation arcs that not only satisfy the requirements in the observation resource allocation phase but also facilitate the arrangement of measurement and control instruction release.We conduct extensive simulations to demonstrate the effectiveness of the proposed algorithm in terms of the number of completed tasks.

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.