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 0broadcast 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.

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。

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。

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。

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.

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。

Route maintenance of on-demand routing protocol in mobile ad hoc networks

In mobile ad hoc networks(MANETs),if a muhihop route breaks,route maintenance is typicallyperformed to establish a new route which causes high control overhead and long packet delay.To decreasethe time of recovery and the overhead of maintenance,a new route maintenance mechanism in which themaintenance is confined to the vicinity of the broken link is proposed.This mechanism patches brokenroute through some other nodes in which only nodes near the broken link(s)may need to be substitutedand the rest of nodes can be retained on the route.The broken link can be repaired without consideringits relative position in the whole path,so the ability of dealing with link failure is improved obviously.Simulation results show that the proposed strategy improves network performance significantly,such as inmerits of control overhead and packet delay.

含高比例光伏配电网中计及需求侧响应的混合储能配置优化

大力发展光伏等可再生能源是我国实现“双碳”目标的重要途径,而光伏高比例接入配电网后随之带来一系列问题。以含高比例光伏配电网为研究对象,提出了计及用户侧需求响应的混合储能优化配置方法。针对负荷和光伏出力的双重不确定性,提出了基于预测误差概率分布的不确定性场景确定方法。考虑系统不确定性,统筹运行阶段用户侧需求响应调控措施以及潮流约束,提出了储能配置的3层优化模型。针对优化模型提出了相应的分解方法,以及基于列和约束生成算法的求解实施流程。通过在IEEE-33节点测试系统中的应用,使得系统可平移负荷峰值下降了10%,节点电压维持在合理范围,并在指导储能配置等方面具备显著的应用效果。通过分析光伏比例灵敏性可知,不含光伏情况下的储能总容量相比100%光伏比例场景增加了90%。

考虑需求响应和用户满意度的主动配电网优化调度

为提高主动配电网(active distribution network,ADN)运行经济性和用户满意度,提出一种考虑需求响应和用户满意度的ADN优化调度方法。综合考虑ADN运行过程中的购电成本、发电成本、维护成本和需求响应成本,建立了以ADN总运行成本最小为目标函数的优化调度模型。利用混沌映射、莱维飞行和收敛因子非线性变化等策略对斑点鬣狗优化算法(spotted hyena optimization,SHO)进行优化,以提高斑点鬣狗算法的优化性能。采用改进斑点鬣狗优化算法(ISHO)对ADN优化调度模型进行求解,算例分析结果表明,ISHO算法的优化效果优于其他算法,2种需求响应同时参与系统调度时的ADN总运行成本最小,经济性更好。

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。

基于主成分分析-BP神经网络的风电备件需求预测

风电机组具有结构复杂,运维困难,且长期处于恶劣的工作环境的特点。风电备件的需求预测有助于为风电场配备最合适的备件数,以确保风电场的平稳、高效运行。构建主成分分析-反向传播(principal component analysis-back propagation,PCA-BP)模型,针对受多因素影响的复杂备件,先利用PCA将影响风电备件的要素进行筛选,再利用BP神经网络算法,得到最为精确的预测结果。比较自回归积分滑动平均(autoregressive integrated moving average,ARIMA)模型、BP神经网络预测和PCA-BP神经网络预测的结果。结果表明:PCA能显著降低神经网络预测误差,预测的精度为93.94%,高于BP神经网络预测的88.39%和ARIMA模型的85.31%,所以PCA-BP神经网络模型的预测精度准确且有可靠结果,能够适用于风机备件的需求预测。

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.

市场渗透率约束下的拼车奖励方案优化模型

目前,我国拼车出行交通模式的市场份额相对较低,在缓解交通拥堵、节能减排方面仍有巨大潜力未被充分挖掘。基于奖励的交通需求管理策略可以提高民众拼车出行意愿,但奖励方案的设置与拼车出行的市场渗透率高度相关,不合理的奖励方案容易导致成本增加,甚至项目破产。为进一步激发拼车需求,合理利用交通资源,文中提出一种基于路段的拼车奖励方案优化模型,以拼车为支点,以奖励为杠杆,实现社会出行总成本的降低。其中,上层模型旨在寻找最优拼车奖励方案以最小化社会出行总成本,下层模型为相应奖励方案下的拼车出行车辆和单人驾驶车辆用户均衡流量分配模型。采用嵌套Frank-Wolfe算法的遗传算法求解该模型,并以Sioux Falls路网和Nguyen Dupuis路网为算例对模型的可行性及有效性进行了验证。结果表明:不符合市场渗透率的预算投入会导致社会总成本大幅上涨;在最优拼车奖励方案下,社会出行总成本降低约24.53%,50%的拥堵路段的拥堵得到缓解,出行公平性问题得到缓和。文中提出的模型可为道路管理者设置科学、合理的拼车奖励方案提供理论基础。

考虑不确定性和需求响应的配电网储能优化配置

分布式电源的间歇性和不确定性等特点使其大规模接入电网对配电网的安全稳定运行带来一定挑战,而在配电网中合理配置储能系统可以有效应对此问题。为减少风光并网产生的不利影响,提高配电网电能质量,首先构建由分布式电源和储能系统共同接入的配电网模型;其次对可再生能源出力不确定性进行处理,并构建需求响应模型平滑负荷曲线;然后以年综合成本最低和电压偏差最小为优化目标,构建配电网储能多目标优化配置模型;最后以IEEE 33节点配电网络为算例,根据分布式电源接入后引起的节点电压偏移确定储能接入位置,并通过改进粒子群算法确定储能优化配置方案。仿真结果表明,该模型可以提高系统经济性和稳定性,同时改进后的粒子群算法在求解效果上更优。