Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.

Space-time correlations of passive scalar in Kraichnan model

We consider the two-point,two-time(space-time)correlation of passive scalar R(r,τ)in the Kraichnan model under the assumption of homogeneity and isotropy.Using the fine-gird PDF method,we find that R(r,τ)satisfies a diffusion equation with constant diffusion coefficient determined by velocity variance and molecular diffusion.Itssolution can be expressed in terms of the two-point,one time correlation of passive scalar,i.e.,R(r,0).Moreover,the decorrelation o R(k,τ),which is the Fourier transform of R(r,τ),is determined byR(k,0)and a diffusion kernal.

Performance of Cross-Layer Design with Power Allocation in Space-Time Coded MIMO Systems

A cross-layer design(CLD)scheme with combination of power allocation,adaptive modulation(AM)and automatic repeat request(ARQ)is presented for space-time coded MIMO system under imperfect feedback,and the corresponding system performance is investigated in a Rayleigh fading channel.Based on imperfect feedback information,a suboptimal power allocation(PA)scheme is derived to maximize the average spectral efficiency(SE)of the system.The scheme is based on a so-called compressed SNR criterion,and has a closed-form expression for positive power allocation,thus being computationally efficient.Moreover,it can improve SE of the presented CLD.Besides,due to better approximation,it obtains the performance close to the existing optimal approach which requires numerical search.Simulation results show that the proposed CLD with PA can achieve higher SE than the conventional CLD with equal power allocation scheme,and has almost the same performance as CLD with optimal PA.However,it has lower calculation complexity.

An Iterative Power Allocation Algorithm for Group-wise Space-Time Block Coding Systems

An iterative transmit power allocation (PA) algorithm was proposed for group-wise space-time block coding (G-STBC) systems with group-wise successive interference cancellation (GSIC) receivers. Group-wise interference suppression (GIS) filters are employed to separate each group's transmit signals from other interferences and noise. While the total power on all transmit symbols is constrained, all transmit PA coefficients are updated jointly according to the channel information at each iteration. Through PA, each detection symbol has the same post-detection signal to interference-and-noise ratio (SINR). The simulation results verify that the proposed PA algorithm converges at the equilibrium quickly after few iterations, and it achieves much lower bit error rates than the previous single symbol SIC PA and the fixed ratio PA algorithms for G-STBC systems with GSIC receivers .

Optimized Allocation Model for Regional Agricultural Fertilizer Resources in Southern Xinjiang from the Perspective of Sustainable Development

[Objective] This study was to establish an optimized model for the allocation of agricultural fertilizer resources in Southern Xinjiang from the perspective of sustainable development.[Method] An optimized model for the allocation of agricultural fertilizer resources was established based on their allocation structure.Combined with the actual agricultural production in Aksu areas of Southern Xinjiang,by establishing a rational evaluation index system,under the premise of considering the planting area constraints,the total water resources constraints and the security constraints of food production,we established the empirical optimal allocation model of the regional agricultural fertilizer resources in Aksu area of Southern Xinjiang.Moreover,we solved the model by using the search algorithm of computer and lingo programming.[Result] The increased economic benefit was near to 1.8 billion Yuan by adopting the optimal allocation methods,with a relative increment of about 34.4%.[Conclusion] Our results provided theoretical basis for achieving the sustainable development of agricultural economy in Southern Xinjiang.

The Operation Mechanism of Agricultural Products Supply Chain and Profit Allocation Model in the Context of Asymmetric Information

The research performed analysis on causes of asymmetric information of agricultural product supply chain and made conclusion on operation mechanism and characteristics of supply chain based on asymmetric information. Finally, the research detailed profit sharing of agricultural product supply chain in the context of asymmetric information and proposed suggestions, providing references of pricing and profit sharing of supply chains of agricultural products.

Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

A Reliability Allocation Method of CNC Lathes Based on Copula Failure Correlation Model

The current research of reliability allocation of CNC lathes always treat CNC lathes as independent series systems. However, CNC lathes are complex systems in the actual situation. Failure correlation is rarely considered when reliabil?ity allocation is conducted. In this paper, drawbacks of reliability model based on failure independence assumption are illustrated, after which, reliability model of CNC lathes considering failure correlation of subsystems is established based on Copula theory, which is an improvement of traditional reliability model of series systems. As the failure time of CNC lathes often obeys Weibull or exponential distribution, Gumbel Copula is selected to build correlation model. After that, a reliability allocation method considering failure correlation is analyzed based on the model established before. Reliability goal is set first and then failure rates are allocated to subsystems according to the allocation vector through solving the correlation model. Reliability allocation is conducted for t = 1. A real case of a CNC lathe and a numerical case are presented together to illustrate the advantages of the reliability model established consider?ing failure correlation and the corresponding allocation method. It shows that the model accords to facts and real working condition more, and failure rates allocated to all the subsystems are increased to some extent. This research proposes a reliability allocation method which takes failure correlation among subsystems of CNC lathes into consid?eration, and costs for design and manufacture could be decreased.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Optimizing Resource Allocation Framework for Multi-Cloud Environment

Cloud computingmakes dynamic resource provisioning more accessible.Monitoring a functioning service is crucial,and changes are made when particular criteria are surpassed.This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to resources.Resource allocation is a complex challenge due to the versatile service providers and resource providers.The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service.The objective of a coherent and rational resource allocation is to attain the quality of service.It also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment.The three main parameters of the proposed framework are data accessibility,optimization,and collaboration.Using an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service quality.The CloudSim simulator has been used to validate the suggested framework.Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS.The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Weighted De-Synchronization Based Resource Allocation in Wireless Networks

Considering the exponential growth of wireless devices with datastarving applications fused with artificial intelligence,the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment.TheKuramoto model is described as nonlinear selfsustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength,in which a mutual behavior is accomplished.In this work,we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network,where each user has different quality of service(QoS)requirements.Because the original Kuramoto model is the synchronization model,we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieveweighted fair resource allocation for users with different QoS requirements.The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner.We analyze the convergence condition for the proposed model,and the results reveal that the proposed algorithm achieves aweighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.

Intensity Allocation Criteria of Carbon Emissions Permits and Regional Economic Development in China——Based on a 30-Province/Autonomous Region Computable General Equilibrium Model

The intensity allocation criteria of carbon emissions permits and its influence on China＇s regional development are analyzed through the 30-province/autonomous region computable general equilibrium （CGE） model. Simulation results show that： industrial intensity criteria without taking regional economic development into account deepen the unbalance of regional economic development; regional intensity criteria without taking industrial properties into account exert little negative impact on regional harmonious development, but relatively high negative influence on high-carbon emission industries. The two-step allocation scheme that the central government allocates emissions permits to provincial governments based on regional economic development and then provincial governments allocate emissions permits to emission resources or entities based on industrial properties is a feasible and operable choice.

COVID-19 Vaccine Allocation: Modeling Health Outcomes and Equity Implications of Alternative Strategies

Given the scarcity of safe and effective COVID-19 vaccines,a chief policy question is how to allocate them among different sociodemographic groups.This paper evaluates COVID-19 vaccine prioritization strategies proposed to date,focusing on their stated goals;the mechanisms through which the selected allocations affect the course and burden of the pandemic;and the main epidemiological,economic,logistical,and political issues that arise when setting the prioritization strategy.The paper uses a simple,agestratified susceptible–exposed–infectious–recovered model applied to the United States to quantitatively assess the performance of alternative prioritization strategies with respect to avoided deaths,avoided infections,and life-years gained.We demonstrate that prioritizing essential workers is a viable strategy for reducing the number of cases and years of life lost,while the largest reduction in deaths is achieved by prioritizing older adults in most scenarios,even if the vaccine is effective at blocking viral transmission.Uncertainty regarding this property and potential delays in dose delivery reinforce the call for prioritizing older adults.Additionally,we investigate the strength of the equity motive that would support an allocation strategy attaching absolute priority to essential workers for a vaccine that reduces infectionfatality risk.

Research on Model and Algorithm of Task Allocation and Path Planning for Multi-Robot

Based on the modeling of robot working environment, the shortest distance matrix between points is solved by Floyd algorithm. With the objective of minimizing the sum of the fixed cost of robot and the cost of robot operation, an integer programming model is established and a genetic algorithm for solving the model is designed. In order to make coordination to accomplish their respective tasks for each robot with high efficiency, this paper uses natural number encoding way. The objective function is based on penalty term constructed with the total number of collisions in the running path of robots. The fitness function is constructed by using the objective function with penalty term. Based on elitist retention strategy, a genetic algorithm with collision detection is designed. Using this algorithm for task allocation and path planning of multi-robot, it can effectively avoid or reduce the number of collisions in the process of multi-robot performing tasks. Finally, an example is used to validate the method.

A Space-Time Reverberation Model for Moving Target Detection

In recent years,moving target detection methods based on low-rank and sparse matrix decomposition have been developed,and they have achieved good results.However,there is not enough interpretation to support the assumption that there is a high correlation among the reverberations after each transmitting pulse.In order to explain the correlation of reverberations,a new reverberation model is proposed from the perspective of scattering cells in this paper.The scattering cells are the subarea divided from the detection area.The energy fluctuation of a scattering cell with time and the influence of the neighboring cells are considered.Key parameters of the model were analyzed by numerical analysis,and the applicability of the model was verified by experimental analysis.The results showed that the model can be used for several simulations to evaluate the performance of moving target detection methods.

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.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

Intellicise Model Transmission for Semantic Communication in Intelligence-Native 6G Networks

To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence(AI)models that provide abilities of semantic extraction and recovery.Nevertheless,it is not feasible to preload all AI models on resource-constrained terminals.Thus,in-time model transmission becomes a crucial problem.This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication.The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data.We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability(MTOP)over the Rayleigh channel.Besides,we define the effective model accuracy(EMA)to evaluate the model transmission performance of both communication and intelligence.Then we propose a joint model selection and resource allocation(JMSRA)algorithm to maximize the average EMA of all users.Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%.