考虑综合能源站柔性调控作用的城市配电网多阶段规划方法

随着城市经济发展与终端用能电气化的普及,以制冷用电为首的电负荷需求持续增加且峰谷差情况愈发严重化,导致现有配电网运行负担逐渐加重。在此背景下,将综合能源站(IES)作为多能流耦合节点,在配电网中引入多能协调互补及蓄能移峰功能,计及多个阶段负荷发展及多类型负荷特征,提出在IES柔性调控作用下的配电网多阶段规划方法。基于IES能流结构及其运行机制,分别在不同气候类型地区负荷模拟场景下,以总规划期内投资与运行经济性最优为目标,建立由线路、变电站和IES构成决策对象的配电网双层多阶段规划模型,规划与运行层模型通过决策变量关联达成一体化协同求解。通过修改后的IEEE 33节点配电系统对所提方法的有效性进行校验,并进一步将所提方法应用于某地实际152节点配电系统,分析不同负荷特征下配电网规划运行效益、负荷峰谷差调节效果及IES运行状况,得出计及IES的城市配电网规划方法的实用性结论。     

Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Heterogeneous Networks (HetNets) and cell densification represent promising solutions for the surging data traffic demand in wireless networks. In dense HetNets, user traffic is steered toward the Low-Power Node (LPN) when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency. However, because of the transmit power differences in different tiers of HetNets and irregular service demand, a load imbalance typically exists among different serving nodes. To offload more traffic to LPNs and coordinate the Inter-Cell Interference (ICI), Third-Generation Partnership Project (3GPP) has facilitated the development of the Cell Range Expansion (CRE), enhanced Inter-Cell Interference Coordination (eICIC) and Further enhanced ICIC (FeICIC). In this paper, we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe (LPS) approach. Our solution allows the separation of User Association (UA) functions at the User Equipment (UE) and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength (max-RSS) based UA scheme, where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system. The proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service regions. Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.     

Incentives for quality improvement efforts coordination in supply chains with partial cost allocation contract

In this paper, we consider quality improvement efforts coordination in a two-stage decentralised supply chain with a partial cost allocation contract. The supply chain consists of one supplier and one manufacturer, both of which produce defective products. Two kinds of failure cost occur within the supply chain: internal and external. The supplier and the manufacturer determine their individual quality levels to maximise their own profits. We propose a partial cost allocation contract, under which the external failure cost is allocated between the manufacturer and the supplier at different rates based on information derived from failure root cause analysis. If the quality levels of the supplier and the manufacturer are observable, we show that the partial cost allocation contract coordinates the supply chain, provided that the failure root cause analysis does not erroneously identify the manufacturer’s fault as the supplier’s, and the supplier does not take responsibility for the manufacture’s fault. In the single moral hazard model, where only the quality level of the supplier is unobservable, the optimal share rates require the supplier to take some responsibility for the manufacture’s fault. However, in the double moral hazard model, where quality levels of the supplier and the manufacturer are unobservable to each other, the optimal share rates require the supplier not to take responsibility for the manufacturer’s fault. It is noted that the root cause analysis conducted by the manufacturer may have its disadvantage in attributing the fault to the supplier when both sides are at fault. We also propose a contract based on the dual root cause analysis to reduce the supplier’s penalty cost. Numerical results illustrate that the partial cost allocation contract satisfies the fairness criterion compared with the traditional cost allocation contract.     

团体标准协调机理研究:一个动态分析

从标准经济学属性上,团体标准是介于事实标准和正式标准之间的一类标准.如何协调好事实标准和正式标准之间的选择关系是团体标准微观层面高效的关键.本文基于"囚徒困境"经典双人博弈模型,构建三阶段协调博弈模型分析团体标准在演化过程中事实标准与正式标准的协调选择问题.研究表明,在已知未来正式标准(即团体标准)优于事实标准以及正式标准的绩效高于事实标准的前提下,低折现和参与者缺乏耐心能够促成尽早"合作".在此基础上,本文基于实际给出提高团体标准收益预期、加快标准版本更新速率和重视"小"企业等政策建议.     

窗口流量控制的干线动态协调控制方法

城市交通干线在高峰时期经常处于饱和状态,排队车辆溢出至上游交叉口的"死锁"现象时有发生.为避免干线阻塞、提高饱和条件下干线交通流的运行效率,首先提出一种有效带宽评价方法对已有干线绿波协调控制系统的运行状态及控制效果进行监控;其次借鉴TCP/IP窗口流量控制思想,设计一种窗口流量控制的干线动态协调控制方法,控制城市干线拥挤交通流.模拟试验及对比结果表明,通过窗口流量通告的方式,下游交叉口可以向上游交叉口实时告知路段的有效容量,便于各交叉口信号机根据当前的交通需求及路段的有效容量重新分配各股车流的绿灯时间.     

C语言程序设计研究型教学实践

C语言程序设计是计算机科学与技术以及相关专业的一门重要专业基础课,但长期以来,教与学的效果不尽人意。本文从研究与教学的关系,教学模式与教学观念的转变,协同学习模式的建立,教学内容的改革,评价体系的构建以及教学手段的改革等方面,全面阐述了C语言研究型教学实践活动。     

“团队协作,角色分工”个性化教学在课改中的探索——以网页设计与制作课程教学为例

本文介绍作者根据霍兰德职业兴趣模型建立的职业兴趣与项目角色模型,阐述在教学过程中采用的"团队协作,角色分工"个性化教学改革,并对教改的得失进行初步总结。     

基于相位差渐进循环协调的应急车辆优先信号控制

为了提高应急车辆的通行效率,针对应急车辆需要“绝对优先”路权的特点进行了信号控制的研究,提出了一种基于相位差渐进循环协调的信号控制策略,并将其与应急车辆优先信号配时转换策略相结合进行应急车辆优先信号控制,给出了应急车辆优先信号配时转换控制策略和动态相位差的计算方法。在由三交叉口组成的城市道路系统中,采用三种应急车辆的信号控制策略进行了微观仿真实验,结果表明,文章提出的基于相位差渐进循环协调的应急车辆优先信号控制策略通过对各交叉口相位动态循环渐进微调后,再采用应急车辆优先信号配时转换策略进行控制,可以使应急车辆安全不停车地通过交叉口,且达到了对非应急车辆影响最小的效果。     

信息化助力研究型企业管理效率提升

做为现代企业的重要模式研究型企业因其独特的盈利模式更需要提升管理效率．管理体系信息化是提升管理效率的重要手段,但是管理信息系统开发的目的以及应用过程会影响到它在企业内部的应用效果,系统开发完毕后在企业被空置、流于形式的现象大量存在．如何使管理信息系统真正成为企业管理的有效辅助工具需要从系统建设目的和建设过程开始研究．本单位立足企业自身管理体系,以满足管理人员日常管理需要为目标,采用敏捷开发方法将管理体系信息化,取得了良好效果．     

Decentralized MDPs with sparse interactions

Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limited to specific parts of the state space. In this work, we explore how such local interactions can simplify coordination in multiagent systems. We focus on problems in which the interaction between the agents is sparse and contribute a new decision-theoretic model for decentralized sparse-interaction multiagent systems, Dec-SIMDPs, that explicitly distinguishes the situations in which the agents in the team must coordinate from those in which they can act independently. We relate our new model to other existing models such as MMDPs and Dec-MDPs. We then propose a solution method that takes advantage of the particular structure of Dec-SIMDPs and provide theoretical error bounds on the quality of the obtained solution. Finally, we show a reinforcement learning algorithm in which independent agents learn both individual policies and when and how to coordinate. We illustrate the application of the algorithms throughout the paper in several multiagent navigation scenarios.