A comparison of coal supply-demand in China and in the US based on a network model

Through analysis the actual coal supply and demand in the US and China, the properties of the coal supply-demand market in both countries are investigated based on the energy supply-demand network. The validity of our model is verified by comparing numerical results with empirical results. The comparison of empirical results and the comparison of coal network model parameters between in the US and in China reveal the essence of the internal differences and similarities of coal supply and demand in these two countries. The third stage of China＇s coal network was close to that of the US in 1995, indicating that the evolutional situation of China＇s coal market begins to transit to an oligopolistic type. Finally, suggestions for China＇s coal supply-demand strategy are put forward.

Supply Chain Network Equilibrium with Revenue Sharing Contract under Demand Disruptions

Contract is a common and effective mechanism for supply chain coordination,which has been studied extensively in recent years.For a supply chain network model,contracts can be used to coordinate it because it is too ideal to obtain the network equilibrium state in practical market competition.In order to achieve equilibrium,we introduce revenue sharing contract into a supply chain network equilibrium model with random demand in this paper.Then,we investigate the influence on this network equilibrium state from demand disruptions caused by unexpected emergencies.When demand disruptions happen,the supply chain network equilibrium state will be broken and change to a new one,so the decision makers need to adjust the contract parameters to achieve the new coordinated state through bargaining.Finally,a numerical example with a sudden demand increase as a result of emergent event is provided for illustrative purposes.

Network coding-aware cache replacement policy in on-demand broadcast environments

Network coding has been proved to be an effective technique in improving the performance of data broadcast systems because clients requesting different data items can be served simultaneously in one broadcast. Previous studies showed that its efficiency is highly related to the content of clients' cache. However, existing data broadcast systems do not take network coding information into account when making cache replacement decisions. In this paper, we propose two networks coding-aware cache replacement policies called DLRU and DLRU-CP to supplement network coding assisted data broadcast in on-demand broadcast environments. In DLRU, both data access and decoding contribution are taken into account to make replacement decisions. DLRU-CP is based on DLRU but allows clients to retrieve decodable data items that have not been requested yet. The performance gain of our proposed cache replacement policies over traditional cache replacement policy is shown in the simulation results, which demonstrate conclusively that the proposed policies can effectively reduce the overall response time.

Deadline-aware network coding for video on demand service over P2P networks

We are interested in providing Video-on-Demand (VoD) streaming service to a large population of clients using peer-to-peer (P2P) approach. Given the asynchronous demands from multiple clients, continuously changing of the buffered contents, and the continuous video display requirement, how to collaborate with potential partners to get expected data for future content delivery are very important and challenging. In this paper, we develop a novel scheduling algorithm based on deadline- aware network coding (DNC) to fully exploit the network resource for efficient VoD service. DNC generalizes the existing net- work coding (NC) paradigm, an elegant solution for ubiquitous data distribution. Yet, with deadline awareness, DNC improves the network throughput and meanwhile avoid missing the play deadline in high probability, which is a major deficiency of the con- ventional NC. Extensive simulation results demonstrated that DNC achieves high streaming continuity even in tight network conditions.

Ants-Based On-Demand Routing Algorithm for Mobile Ad Hoc Networks

An ants-based on-demand routing algorithm （AORA） specialized for mobile ad hoc networks is proposed. AORA measures the network＇s traffic information including delivery time, route energy etc. by the continuous delivery of data packets, then calculates the compositive parameter for each route which can be seen as the stigmity and uses it to choose the comparatively optimal route in real time. To adjust the weight of each traffic information, the algorithm can meet the different demand of the network＇s user. Multipath source self repair routing （MSSRR） algorithm and dynamic source routing （DSR） can be seen as the special samples of AORA. The routing overhead is not increased in this algorithm. By using simulation, it can be seen that the performance of AORA is better than that of DSR in all scenarios obviously, especially the delivery fraction is increased by more than 100 96.

Soft Computing of Biochemical Oxygen Demand Using an Improved T–S Fuzzy Neural Network

It is difficult to measure the online values of biochemical oxygen demand(BOD) due to the characteristics of nonlinear dynamics, large lag and uncertainty in wastewater treatment process. In this paper, based on the knowledge representation ability and learning capability, an improved T–S fuzzy neural network(TSFNN) is introduced to predict BOD values by the soft computing method. In this improved TSFNN, a K-means clustering is used to initialize the structure of TSFNN, including the number of fuzzy rules and parameters of membership function. For training TSFNN, a gradient descent method with the momentum item is used to adjust antecedent parameters and consequent parameters. This improved TSFNN is applied to predict the BOD values in effluent of the wastewater treatment process. The simulation results show that the TSFNN with K-means clustering algorithm can measure the BOD values accurately. The algorithm presents better approximation performance than some other methods.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

A closed-loop Supply Chain Network Equilibrium Model with Fuzzy Demand under Production Capacity Constraints and Price Rigidities

A closed-loop supply chain network equilibrium problem is examined,which consists of mutual competitive manufacturers with production capacity constraints and retailers with fuzzy market demands,mutual competitive collectors as well as a binding price ceiling of the commodities.By utilizing the credibility measure of fuzzy event,variational inequality and Lagrange dual theory,the optimum behaviors of manufactures,retailers,collectors and consumers are described.Therefore,the supply chain network equilibrium model is established.Numerical examples were given to illustrate the impact of production capacity constraints and price ceiling on the network equilibrium pattern.The results show that the shortage of commodities in consumer market will be more serious,the wholesale price of product and purchase price of waste products rise,the profits of retailers decrease,the profits of manufacturers and collectors increase as the government gradually reduce the binding price ceiling on competitive markets,and the trend will be more obvious in the existence of production capacity constraints at same time.

Robust Design of Virtual Topology for WDM Networks under Bandwidth Demand Uncertainties

In this paper, a novel method is proposed to address the problem of designing virtual topology over wavelength division multiplexing （WDM） networks under bandwidth demand uncertainties. And a bandwidth demand model under uncertainties is presented. The optimization goal of virtual topology design is defined as minimizing the maximum value amongp percentiles of the bandwidth demand distribution on all Hght-paths. Correspondingly, we propose a heuristic algorithm called an improved decreasing multi-hop logical topology design algorithm （ID-MLTDA） that involves with a degree of uncertainties to design virtual topology. The proposed algorithm yields better performance than previous algorithms. Additionally, the simplicity and efficiency of the proposed algorithm can be in favor of the feasibility for topology design of large networks.

一种安全的Ad Hoc On-demand路由协议

由于Ad Hoc网络固有的弱点,设计安全、有效的Ad Hoc路由协议是困难的。本文从新的角度设计了一个简单、安全的On-demand路由协议。在路径请求和响应阶段,源节点和目的节点的身份分别被隐藏,只有那些位于目的节点选择的最优路径上的节点可以获得完整的路由信息,从而产生有效的前向和反向路径。同时,一个公开的单向Hash函数可以利用隐藏的路由信息构建单向Hash链用于路由信息的认证,从而不需要预先的共享密钥。在一次路由计算中,只有源节点和目的节点需要进行一次非对称密码运算。

Minimizing the Impact of Long Propagation Delay on Multiple Access for Wireless Networks

A multiple access protocol is proposed to greatly improve multiple access performance of wireless networks with long propagation delay. All the nodes with packets to send can make rapid successful reservation in access reservation mini-slots, which is adaptively adjusted according to current traffic load and idle channel resources. A Central Control Node (CCN) coordinates channel reservation and allocates on-demand channel resources to the successfully accessed nodes on two channels. Each node can employ only one handshake to accomplish each communication session, and transmit one or multiple data packets piggybacked with acknowledgment (ACK) information to one or multiple destination nodes in each frame until the end of their communication sessions, which greatly minimizes the impact of long propagation delay caused by handshakes and improves channel efficiency. Simulation results show that the proposed protocol obviously outperforms the Centralized Scheduling-based Medium Access Control (CSMAC) protocol, especially in the presence of long propagation delay.

Load Flow Analysis Framework for Active Distribution Networks Based on Smart Meter Reading System

With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distribution networks demands voltage visibility and control at all voltage levels. Distribution system state estimations, however, have traditionally been less prioritized due to the lack of enough measurement points while being the major role player in knowing the real-time system states of active distribution networks. The advent of smart meters at LV loads, on the other hand, is giving relief to this shortcoming. This study explores the potential of bottom up load flow analysis based on customer level Automatic Meter Reading (AMRs) to compute short time forecasts of demands and distribution network system states. A state estimation frame-work, which makes use of available AMR data, is proposed and discussed.

诉求统筹下城中村改造利益相关者协同治理——以天津市北辰区改造项目为例

利益相关者协同治理是推动城中村改造项目顺利高效实施的关键,但改造实践中各阶段利益相关者关系及利益诉求错综复杂。以天津市北辰区改造项目为例,采用社会网络分析方法,系统、全面地分析项目各阶段利益相关者关系及其诉求的网络特征、演化规律。结果表明,拆迁安置补偿阶段利益相关者利益诉求协调难度最高;同一利益主体在项目不同阶段的角色功能差异明显;建设公众满意的生活环境是多元利益相关者的核心利益诉求。采用社会网络分析方法,剖析利益相关者关系及诉求协调的重点问题,为城中村改造利益相关者协同治理提供科学参考与依据。

基于区块链的城市智能交通网络均衡分配方法

为了提高城市智能交通网络的应用效率,有效分配网络中存在的资源,同时保证网络资源分配的均衡度。因此,提出区块链技术下城市智能交通网络均衡分配方法。通过建立城市智能交通网络模型,分析用户在交通网络中的资源需求。根据用户的需求,采用区块链技术分配网络中的资源,通过引入椭圆曲线数字签名算法,对需求和资源的发布信息加密处理,并根据资源需求与资源供应之间的匹配,完成城市智能交通网络资源的均衡分配。实验结果表明,所提方法的用户吞吐率高,且Jain公平指数在0.8以上,表明所提方法的资源分配合理性高。

基于动态电价机制的配网运营商与用户储能商双层协调多目标优化策略

为在电力市场和调度运用中发挥用户侧分布式储能的价值,充分利用电价的优化调节能力,提出考虑负荷需求响应的多目标双层动态电价模型。其中,上层以区域配网代理运营商运营成本和平均负荷波动率最小为目标,下层考虑储能商负荷需求响应,以各储能商运营成本最小为目标。此外,采用改进优劣解距离法对模型求得的Pareto最优解集进行多属性决策,以避免传统优劣解距离法中人为赋权的主观性。最后,决策出最优的内部电价、充放电计划和负荷安排。算例表明,该策略可以降低储能商的运行成本,提高区域配网运营商的收益与区域供电可靠性,同时有利于平滑负荷曲线。

考虑碳排放流的电-气互联系统源荷互动低碳经济调度

为应对电力系统存在的弃风、碳排放较高等问题,基于碳排放流理论提出了一种电-气互联系统源荷互动低碳经济调度模型。基于电力系统碳排放流计算方法,推导得到考虑气网动态特性的天然气系统碳排放流计算方法;构建以储液罐装置为主的储碳系统(CSS),CSS与火电机组(TU)、风电机组(WT)、电转气(P2G)设备组成TU-CSS-P2G-WT的联合运行方式;源侧以系统日运行总成本最小计算得到能源网络的碳势时空分布,荷侧根据源侧的碳势信号,基于低碳响应机制建立激励型低碳需求响应模型,合理调整用能行为最小化用户购能总成本;建立源荷互动两阶段低碳经济调度模型,并利用GUROBI进行求解。通过改进由IEEE 39节点和比利时20节点构成的电-气互联系统进行算例分析,验证了所提模型的低碳经济性和灵活性。

多周期农产品废弃物供应链网络均衡模型构建

目的研究农产品废弃物可持续利用问题,既实现供应链整体利润最优,又减轻对环境的污染。方法在随机需求的背景下,考虑生产合作社对零售商的不同激励度,分析生产合作社、零售商、需求市场的最优状态,利用变分不等式建立相应的多周期供应链网络均衡模型。结果从多周期角度,实现了农产品废弃物供应链网络均衡利益最优化,可为合作社与零售商在较长时间达到一种均衡状态提供有效解决方案。结论既实现了多周期农产品废弃物的持续利用,又促进了循环经济的发展,同时还实现了供应链网络均衡、供应链整体利润最优和减轻环境污染等目标。

基于多类需求响应的配电网优化调度

针对当前激励型定价补偿机制无法满足多类型负荷差异化需求的问题,提出了一种分段式补偿价格形式的可中断负荷补偿方法,同时引入负荷转移时间和负荷转移功率的二维交变函数,建立了量化负荷转移成本的可转移负荷补偿模型;计及可中断负荷不确定性,建立了多类型需求响应参与配电网优化运行模型,针对模型中非凸非线性的约束,利用二阶锥松弛方法将模型转化为混合整数二阶锥规划模型,采用CPLEX求解器对模型进行求解,并引入贡献度和置信度作为评估用户响应能力的评价指标。仿真结果表明,新型补偿机制能更加合理地指导用户调整用电负荷,平滑负荷曲线,提高配电网运行经济性。

考虑需求响应和共享储能的微网联盟两阶段优化调度

随着共享储能的推广,能源信息的流动与共享得到进一步发展,为促进能源管理和提升利用效率,提出一种基于共享储能和需求响应的微网联盟两阶段优化调度策略。首先,建立多微网联盟-共享储能系统运行框架,并基于综合贡献度对微网利益分配过程进行建模;其次,建立两阶段优化调度模型,第一阶段以多微网联盟-共享储能系统净收益最大为目标,第二阶段考虑用户的需求响应,求解耗能用户的最低购能费用;再次,利用求解器对两阶段模型分别进行求解,得到多微网系统的分时能源价格、各微网设备的运行状态、需求响应后负荷和各微网利益;最后,设置4个方案作为算例,验证所提优化策略在提高各微网收益和激励用户需求响应的有效性。

基于跨链技术的食品供需网信息共享激励模式构建

随着食品种类多元化和消费者需求的提升,食品生产流程更加复杂,供应链之间交流越来越多,相关企业、食品供应链和行业机构之间的交互形成了复杂的食品供需网结构。食品供需网成员之间信息共享有利于企业之间充分合作实现长期发展,但仍有许多企业共享信息意识较弱,无法进行有效信息沟通。区块链跨链技术的应用和发展,为食品供需网信息共享提供了新思路。根据食品供需网复杂性,引入区块链技术和声誉激励机制,将声誉进行量化管理,结合区块链智能合约技术实现声誉激励机制自动化。进一步引入区块链跨链技术,打破不同行业、地区区块链之间的交易壁垒,构建基于区块链跨链技术的食品供需网信息共享声誉激励模式,以猪肉供需网为例阐述跨链信息共享声誉激励流程。该激励模式不仅保障共享信息安全性与准确性,同时保护企业隐私,最大程度保护交易方自身利益,破解信息无法共享的障碍,激发食品供需网企业参与信息共享积极性。不仅实现食品供需网内部异构区块链之间的信息交流和价值互通,还为食品供需网价值信息交流互通的发展提供新思路。