考虑储能用户与新能源双边交易调峰服务的电力系统联合运营模式

新能源并网规模的不断扩大和并网容量的大规模增加给电网的调节能力和新能源消纳问题带来了严峻的挑战,采用储能系统参与调峰是推进新能源发展的重要手段。为此,提出了综合考虑储能用户满意度和新能源调峰需求的电力系统双边交易联合运营模式和交易模型,以各方综合效益最大为目标,建立新能源侧、电网侧和用户侧的投标和出清模型,其中储能用户与新能源于日前完成双边交易以直接减小电网的调峰服务压力,最终由电网侧的储能消纳剩余新能源出力。算例仿真结果表明,所提运营模式不仅有效地调动了用户主动调峰的积极性,还减小了电网侧储能的调峰压力,在促进新能源发展和保障电力系统稳定运行方面具有重要意义。     

考虑风电消纳与火-储深度调峰的系统优化调度及补偿费用分摊策略EI北大核心CSCD

制定合理的深度调峰补偿分配机制是能源转型过程中必须解决的问题。将价格型需求响应(PDR)考虑在内,建立计及风电消纳与火-储深度调峰的系统优化调度模型。在此基础上综合考虑多种分摊因素建立分摊策略,为市场提供一种火-储补偿费用分摊的新思路。实施PDR优化负荷曲线,同时为独立储能电站(IESPS)提供套利基础;利用火电、IESPS深度调峰以促进系统调峰能力的释放,为分摊策略的建立提供数据支撑;针对火电、IESPS深度调峰补偿涉及的分摊问题,在综合考虑多种分摊因素的基础上,提出一种基于层次分析法的新分摊策略,旨在挖掘火电参与分摊的积极性。基于改进的IEEE 39节点系统进行算例仿真,算例结果验证了调度模型的有效性和分摊策略的合理性。     

两级电力市场环境下考虑条件风险价值的新能源场站最优售电模型EI北大核心CSCD

为充分发挥能源资源优化配置作用,我国正逐步建成“统一市场、两级运作”的两级电力市场。在两级电力市场建设初期,新能源场站参与省间电力现货交易的计划电力缺乏指导性是目前亟须研究的问题。对省间-省内两级电力市场的现行机制进行了深入研究,探讨了其运作框架。考虑省间电力现货交易与辅助服务市场的多时间、多空间耦合特性,构建了两级电力市场下新能源场站的售电决策双层优化模型,并基于条件风险价值法对下层模型进行转化,实现对新能源场站出力不确定性的控制。依托东北某省工程实际数据进行了算例分析,结果表明该模型能够在两级电力市场环境下提高新能源场站的市场收益。     

Multi-objective routing algorithms for low-earth orbit satellite network

The low-earth orbit (LEO) satellite network, composed of a large number of satellite nodes, is a hot research topic at present. Due to the characteristics of the large-scale LEO satellite network, such as many satellite nodes, short orbit period, large dynamic change of topology, and unstable link-state, its communication quality of service (QoS) requirements are difficult to meet. Aiming at this problem, various factors that may affect data transmission are first analyzed. The network link selection problem is modeled as a multi-constraint optimization decision problem, a routing mathematical model based on linear programming (LP) is designed, and its solution is solved. Aiming at the problem of limited onboard computing resources, a multi-object optimization Dijkstra algorithm (MOODA) is designed. The MOODA finds the optimal path according to the comprehensive performance of the link. It solves the problems of poor comprehensive QoS performance and the low degree of load balancing of the paths found by the Dijkstra algorithm. The simulation results show that the paths found by the two algorithms have good QoS, robustness, and load balancing performance.     

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.     

基于深度学习的心脏图像分割研究现状

面对严重的医学影像分析缺口,深度学习的发展能够满足国内医疗行业的需求。心脏图像的处理方法可大致分为传统的图像处理技术、基于图谱的方法（atlas-based methods）、基于模型的方法（model-based methods）以及目前热门的采用机器学习和深度学习的方法。在深度学习兴起之前,传统的机器学习技术如模型法和图集法在心脏图像分割中有良好表现,但通常需要大量的特征工程知识或先验知识才能获得令人满意的精度。而基于深度学习的算法能从数据中自动发现复杂的特征以进行对象检测和分割。得益于先进的计算机硬件以及更多可用于训练的数据集,基于深度学习的分割算法已超越了以往的传统方法。本文回顾了2012—2022年有关心室、心外膜和心包脂肪的图像处理的各项方法、衡量指标及其目前的研究现状,并结合分割技术的发展,讨论了心脏分割的发展趋势。     

发用两侧参与调峰的现货市场联合出清模式设计

针对新型电力系统下新能源装机容量大,发电侧调峰资源紧缺,弃风、弃光现象严重的问题,文中设计了一种考虑发用两侧参与调峰的现货市场联合出清模式,将发电侧深度调峰、柔性负荷调峰、新能源消纳与现货市场相结合,充分发掘发用两侧的调峰能力。首先,将独立储能和用户作为用户侧调峰资源,并根据用户是否具有实时跟踪调度指令的能力,将其分为灵活调节用户和非灵活调节用户,在此基础上设计了发用两侧参与调峰的日前市场和实时市场出清模式并建立出清模型。其次,综合考虑各市场主体的贡献和受益程度,设计了调峰辅助服务费用的结算与分摊机制。最后,通过算例验证了所提市场模式对于新能源消纳的有效性和合理性。算例分析中,文中所提市场模式相比仅发电侧参与调峰的模式可降低17.3%的弃风弃光量。     

Analysis of Critical Factors in Manufacturing by Adopting a Cloud Computing Service

The advantages of a cloud computing service are cost advantages, availability, scalability, flexibility, reduced time to market, and dynamic access to computing resources. Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors. To find critical factors, this study first reviewed the literature and established a three-layer hierarchical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework. Then, a hybrid method that combines two multi-criteria decision-making tools—called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage—was used to objectively identify critical factors for the adoption of a cloud computing service, replacing the subjective decision of the authors. The results of this study determined five critical factors, namely data access security, information transmission security, senior management support, fallback cloud management, and employee acceptance. Finally, the paper presents the findings and implications of the study.     

A real options model of phased migration to cellular manufacturing

The literature is replete with models that examine various aspects of cellular manufacturing (CM), such as optimisation of cell layouts. However, many firms may realise zero to marginal returns from CM. Given this uncertainty, the manager should first determine the value of CM to the firm before deploying it. Although traditional valuation models employing discounted cash flow analysis allow for uncertainty, they treat future investments as fixed when computing the investment’s present value. The real options (RO) logic of valuation allows the manager to exercise the option to invest in or abandon a project based on expected outcomes. Future investments are thus options. This paper presents an RO model for CM migration that addresses whether a firm should migrate to CM; and it prescribes the sequence of cell deployment, which has not been addressed in the literature. Our model is also much more transparent and accessible to practitioners, with an accompanying software tool for prospective users. Finally, we use simulation extensively to discover the drivers of the optimal cell deployment sequence. Our results show that there is a complex interplay between net present value, speed of cellularisation, inter-cell learning and volatility in terms of their influence on the cell sequence.     

New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management

Greening the supply chain is an increasingly important concern for many business enterprises and a challenge for logistics management. Critical functions within green supply chain management are internal improvements and selection of green suppliers. This study proposes a novel, hybrid model that addresses dependent relationships between various criteria and the vague information coming from decision-makers. The Decision-making Trial and Evaluation Laboratory (DEMATEL) technique structures the relationships among criteria, thereby constructing an influential network relationship map (INRM). Meanwhile the DEMATEL-based, analytical network process (ANP) method aids in obtaining influential weights of the criteria. Decision-makers may hold diverse opinions and preferences due to incomplete information, differences in knowledge or simply conflicts that are inherent between various departments. This can make it difficult to judge the performance of alternatives. One remedy is to apply a modified COmplex PRoportional ASsessment of alternatives with Grey relations. Next, this is applied to improve each criterion for integration of the performance values obtained in closing the aspiration level from different expert opinions based on INRM. An empirical example using data from a Taiwanese electronics company is provided to demonstrate our proposed method. The results can provide firms with a knowledge-based understanding of the source of some problems, thus reducing the performance gaps and closing the aspiration levels. Finally, there is a discussion on certain managerial implications.