Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Incentive Mechanism Model Design for Sharing of Information Resources in Rural Areas

In order to solve the issues concerning the cross-unit sharing of information resources in rural areas, we analyze the incentive problem of the sharing of information resources in rural areas using the incentive theory method; establish corresponding incentive mechanism model (It is divided into positive incentive model and negative incentive model, and only when the two models guarantee each other and are used at the same time can they be effective). Based on this, we put forward the institutional design for sharing of information resources in rural areas as follows: firstly, establishing an administrative agency of rural information resources sharing, above the authority of all units, responsible for related work on sharing of information resources in rural areas; secondly, establishing and improving the positive and negative incentive mechanisms, to ensure the realization of sharing of information resources in rural areas.

Research on Credit Default Swaps Pricing Considering Moral Hazard Incentive under Reduce-Form Model

Equilibrium pricing of credit default swaps(CDS)promotes efficient identification of credit risk in the market,which in turn leads to efficient allocation of resources.However,even when CDS have been priced in equilibrium,i.e.,when premiums are equal to anticipated payments,the moral hazard incentives of CDS buyers increase with CDS transactions.Consequentially,it becomes an interesting research direction to study the impact of moral hazard incentives on the trading mechanism or pricing of derivatives(CDS).Most of the existing literature on the impact of moral hazard incentives in CDS pricing on derivatives trading mechanisms takes a macro perspective and focuses on the agreement risk effect.The literature exploring the analysis of the impact of moral hazard on the probability of agreement default from a micro perspective is not yet available.With this in mind,this paper focuses on the mechanisms by which“fraud”,an extreme manifestation of micro-moral hazard incentives,affects the probability of default.This paper introduces for the first time the concept of“claiming fraud”by credit protection buyers,which is different from the macro perspective of moral hazard incentives,and thus defines a specific extreme form of moral hazard incentives.Meanwhile,to address the intrinsic feature of the lack of economic explanatory power of the reduce-form model,this paper introduces a moral hazard incentive factor into the reduce-form model,and proposes a moral hazard state variable as a function of the asset value of the reference entity,which gives the reduce-form model strong economic explanatory power,and the default predictability is reduced by the description of the reduce-form model.In terms of the object of study,this paper considers the issue of moral hazard incentives in the presence of claiming fraud in two reference entities to further explore the impact of moral hazard incentives on default protection at the micro level in terms of cyclic default.Finally,based on the analysis of the results of the numerical simulation experiments,it is proposed that increasing the number of reference assets for CDS buyers will help to reduce the moral hazard incentives of the buyer,and thus the anticipated payments to the buyer,i.e.,we attempt to endogenize the credit risk of an asset by allowing the asset holder to choose the probability of the asset going up or down,which helps to understand the phenomenon of moral hazard incentives in CDS trading.

An Improved Extreme Learning Machine Prediction Model for Ionospheric Total Electron Content

Earth’s ionosphere is an important medium for navigation,communication,and radio wave transmission.Total Electron Content(TEC)is a descriptive quantify for ionospheric research.However,the traditional empirical model could not fully consider the changes of TEC time series,the prediction accuracy level of TEC data performed not high.In this study,an improved Extreme Learning Machine(ELM)model is proposed for ionospheric TEC prediction.Improvements involved the use of Empirical Mode Decomposition(EMD)and a Fuzzy C-Means(FCM)clustering algorithm to pre-process data used as input to the ELM model.The proposed model fully uses the TEC data characteristics and expected to perform better prediction accuracy.TEC measurements provided by the Centre for Orbit Determination in Europe(CODE)were used to evaluate the performance of the improved ELM model in terms of prediction accuracy,applicable latitude,and the number of required training samples.Experimental results produced a Mean Relative Error(MRE)and a Root Mean Square Error(RMSE)of 8.5%and 1.39 TECU,respectively,outperforming the ELM algorithm(RMSE=2.33 TECU and MRE=17.1%).The improved ELM model exhibited particularly high prediction accuracy in mid-latitude regions,with a mean relative error of 7.6%.This value improved further as the number of available training data increased and when 20-doys data were trained,achieving a mean relative error of 4.9%.These results suggest the proposed model offers higher prediction accuracy than conventional algorithms.

Optimization of Chinese power enterprises R&D staff incentive mechanism

With the deepening of electric power market reform in China,the monopoly edge of the state-owned electric power enterprises will lose.On the basis of the existing post performance salary mechanism,Chinese power enterprises need to optimize the incentive mechanism of R&D staff,to arouse the R&D staff's enthusiasm and creativity,to adapt to the new market competition and further improve market value.Whilst the incentive mechanism optimizing processing needs to consider not only the changing market environment but also the personal and working characteristics of R&D staff.This paper summarizes the characteristics of the current Chinese power enterprises' R&D staff:staff's theory quality is high,but insensitive to the market;they are confronted with heavy workload and diversified job choices;managers can observe their behavior choices or not;besides,the process of R&D is complex and the market reactions of R&D achievements are uncertain.Based on the premise of the above features,two incentive models are established in this paper from the point of view of enterprise managers.One is for the situation when staff's behavior choices can be observed;the other is for the situation when staff's behavior choices cannot be observed.Through solving the model,we analyze the optimization path of electric power enterprises R&D staff incentive mechanism under these conditions:(1) when staff's behavior choices can be observed,managers can pay more to the R&D staff who develop products with higher output value,in order to encourage them to work harder.(2) when staff's behavior choices cannot be observed,managers should take reasonable strategies according to the different situations:a.when R&D staff incentive totally depend on the market value of the R&D achievements,managers should allocate workload rationally according to their different technical levels;b.when the market reactions of R&D results become more precarious,managers need to reduce the incentive intensity which based on the market value and raise their fixed salary level;c.when R&D staff become more risk averse,managers should reduce the incentive intensity which based on the market value and raise their fixed salary level;on the contrary,managers should improve the incentive intensity and reduce the fixed salary level.

沿海省份城市发展活力评价及其互动关系研究——以副省级城市为例

以德鲁克社会创新为理论基础,基于城市内三大行为活动主体构建包含人居环境、营商环境、治理环境系统的发展活力评价指标体系,利用多因子分层动态激励模型对2006—2020年我国沿海省份8个副省级城市的发展活力进行评价,运用耦合协调模型和面板向量自回归模型分别测度并分析3个子系统间的耦合协调情况和互动关系。结果表明:(1)各城市的发展活力由高到低依次为广州、深圳、杭州、南京、青岛、宁波、大连、厦门,城市间发展活力综合评价结果两极分化明显,营商环境是影响城市发展活力提升最重要的因素,人居环境次之,治理环境最小。(2)8个城市的耦合协调级别不高,整体处于失调类型。(3)3个系统间相互作用,但其发展水平主要受自身影响。其中,人居环境系统显著受其他两个系统影响;营商环境系统受治理环境系统影响较强;治理环境系统主要依靠自身调节,受其他系统的影响较小。

虚拟社区激励措施对用户知识贡献倾向的影响机理研究

基于SOR理论和隐马尔科夫模型,采用马蜂窝旅游虚拟社区用户知识贡献行为数据和激励数据,探讨外在激励如何通过用户知识贡献倾向影响其知识贡献行为。研究表明:依据构建的隐马尔科夫模型,挖掘用户具有沉默、参与、积极3种隐含的知识贡献倾向;同一知识贡献倾向下,用户首次回答问题时间越早、旅游经验越丰富、累计贡献数量越多,用户知识贡献数量就越多;物质激励、精神激励以及二者联合激励与用户动态变化知识贡献倾向匹配,才能取得正向的激励作用。

产业共性技术跨组织合作研发激励模型研究

本文研究了一个由高校院所进行基础研究和制造企业负责后续商业开发的共性技术跨组织合作研发激励模型及其选择问题。本文将政府支持与合同激励放在同一框架,运用博弈论方法为制造企业设计产出导向单一激励模型、产出导向+过程监控激励模型及“政府支持+”综合激励模型以激发高校院所基础研究的积极性,同时解析了激励模型选择、影响因素及机理。研究表明:“政府支持+”综合激励模型是三种激励模型中的占优模型、产出导向+过程监控激励模型优于产出导向单一激励模型;双方收益分享系数具有促进和抑制高校院所认真履责行为的双重作用,且对其机会主义行为具有抑制作用,对制造企业后续商业开发行为及预期收益有“倒U型”影响;高校院所基础研究的影响系数在双方收益分享系数对制造企业后续商业开发努力的影响中发挥调节作用;政府补贴对高校院所和制造企业的合作研发行为具有明显的正向激励作用;惩罚系数可有效抑制高校院所的机会主义行为。

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。

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。

基于主动激励的掘进机回转台振动抑制研究

悬臂式掘进机截割过程由于煤岩形状、硬度分布的随机性,当截割到硬岩时截割头会产生无规则的剧烈振动,瞬时高强度的冲击对掘进机关键受力部件回转台影响最大,其动态可靠性和寿命影响着截割效率和截割稳定性,为减弱截割过程回转台的振动,首先,基于截割现场回转台易失效损坏现象,建立截割过程回转台力学模型,推导回转台力学方程,研究回转台受力随截割参数变化规律;通过搭建截割试验台,测试截割过程回转台振动响应,结合有限元进行回转台振动模态分析,求解回转台前6阶固有频率及振型,分析回转台剧烈振动的原因。其次,基于工况路径分析理论,建立截割过程回转台工况传递路径分析模型,模拟回转台合成振动,分析各激励点对回转台振动贡献量及贡献程度,准确找到回转台主要激振源。最后,通过施加外部主动激励的方式,分析不同激励频率加载下回转台振动情况,探寻能够降低截割过程回转台振动的激励频段。试验结果表明:外部激振频率处于20~60 Hz,回转台振动幅值降低明显,此频段激励频率对回转台振动控制效果良好,有益于提高回转台寿命,且激振频率为40 Hz时回转台振动最小,回转台振动抑制效果最佳。研究结果有益于丰富回转台振动相关研究理论,实现在不改变机械结构基础上,降低回转台受力与结构变形,可为掘进机回转台的振动问题提供理论参考。

管理层风险偏好异质性、股权结构与股权激励模式组合

近年来,越来越多上市公司在推出的同一期股权激励计划中同时包含两种以上股权激励模式。以2006—2020年实施股权激励模式组合计划的沪深A股上市公司为研究对象,实证研究管理层风险偏好异质性对上市公司股权激励模式组合选择行为的影响,并探究股权结构对两者关系的调节作用。研究发现:管理层风险偏好异质性越大,公司越有可能选择股权激励模式组合,以匹配管理层异质性的风险偏好,从而实现管理层风险承担总水平最优;股权结构对管理层风险偏好异质性与股权激励模式组合选择之间关系具有显著的调节效应,国有控股和股权集中度增大能够弱化管理层风险偏好异质性对股权激励模式组合选择的正向影响;股权激励模式组合能够匹配管理层风险偏好异质性,使得管理层风险承担总水平实现最优,进而提升公司价值。

不对称信息下的提成制销售激励合同设计

代理商销售模式下,不对称信息对制造商产品定价与销售合同设计的影响越来越不容忽视。本文基于委托代理模型,分别构建了两种信息情况下提成制激励合同模型,对比分析了信息不对称下产品定价与代理商提成的关系,以及信息不对称对代理商收益、制造商利润的影响。研究发现,制造商最优定价与高需求实现概率和努力成本密切相关,当高需求市场实现概率较小且努力成本较高时,制造商将放弃对代理商的激励,并设定较低产品价格。当制造商激励代理商努力时,不对称信息有利于提高代理商期望收益,但会降低制造商期望利润,不影响价格高低。此外,不对称信息会缩小制造商可接受的努力成本区间,当代理商努力成本较大时,制造商会因薪酬支出过高而放弃对代理商的激励,而不对称信息使得代理商薪酬进一步提高,使得制造商更倾向于不激励代理商努力,相应定价也随之降低。

晋升激励、资源错配与经济高质量发展——基于新旧考核模式差异化视角

基于“晋升锦标赛”治理模式的特征事实,使用2003—2016年287个地级及以上城市面板数据,实证考察新旧考核模式下晋升激励对经济高质量发展的差异化影响。结果表明:(1)传统考核观下晋升激励显著阻碍了经济高质量发展,资源错配是晋升激励阻碍经济高质量发展的重要原因。(2)在资源丰富程度较低、政府财政透明度较低、年轻官员主政的城市以及直辖市中,晋升激励对经济高质量发展的负面影响更大;晋升激励对政绩存在“可视性偏差”。(3)新型考核观下晋升激励缓解了资源错配问题,促进了城市经济高质量发展,具有差异化空间溢出的特点。

现代粮食全产业链标准综合体制定流程优化及应用研究

提高现代粮食全产业链韧性的核心之一是龙头企业制定标准综合体,那么,需要解决关键共性技术识别难、产业链中各环节利益主体所用标准衔接难的问题。首先,基于LDA主题聚类模型,构建HMM-HITS主题关键性识别模型,其特点是识别的关键技术不仅具有竞争力还具有强传播力;再结合基于共类偏度的主题共性识别模型,构建了关键共性技术识别模型;依据价值性、稀有性、难以模仿性、不可替代性和必然实施性的原则,判别该模型识别出的关键共性技术是否进入龙头企业标准体系表。其次,基于农产品供应链机制,构建以提升韧性为目标的标准衔接协商机制。最后,得出现代粮食全产业链的标准综合体制定优化流程,该流程在粳稻标准综合体制定中得到验证。

基于移动众包网络动态激励机制的恶意代码传播模型

基于移动众包网络(MCN)的固有特性,引入动态激励促进网络移动用户(MU)的活跃性,研究了恶意代码在MCN中传播的动态行为,提出一个新的恶意代码传播模型SIR-M,M节点表示处理节点任务的众包节点。首先,考虑到刚被感染节点的能动性,受感染节点可通过MCN的众包机制寻求MU对此节点进行隔离和免疫强化。其次,通过稳定性分析和数值仿真验证模型有效性,与SIR模型进行比较以分析众包机制对系统的影响。结果表明,移动众包网络的众包机制显著减缓了恶意代码的传播速度,降低了恶意代码在网络中大规模泛滥的风险。

基于SEM模型的强化激励对不安全行为影响研究

煤矿员工的不安全行为是导致煤矿安全事故发生的重要原因,为规范矿工的不安全行为,防止煤矿安全事故的发生,本文基于激励理论中的强化理论,以不安全行为意向作为中介调节变量,探讨正向激励与负向激励对不安全行为意向及不安全行为的影响,设计调查问卷,对山西某煤矿一线员工收集数据进行实证研究,构建正向激励、负向激励、不安全行为意向与不安全行为的结构方程模型(SEM)。研究结果表明:精神奖励、精神惩罚显著负向影响不安全行为意向,羞愧惩罚显著负向影响不安全行为,不安全行为意向显著正向影响不安全行为。

五年制高职机械制造类专业学徒制实践创新机制研究

研究五年制高职机械制造类专业学徒制培养创新机制问题。以江苏联合职业技术学院无锡交通分院模具设计与制造专业为例,分析现师徒制培养中存在的问题,提出以职业为导向校企联合招生招工-培养、纳入人才培养体系的综合评价和多主体参与补偿的创新性机制框架,并针对政策法规调整、教学内容优化、培养模式改进等关键环节提出建议,达到实现产教融合高技能准员工的人才培养目的。

基于改进SlowFast模型的设施黄瓜农事行为识别方法

[目的/意义]农事行为活动识别对设施蔬菜生产精准化调控有着重要意义,在一定程度上可以通过查看农事操作的时间、操作过程是否合理来减少因农事行为不当导致产量下降。为了解决农事行为识别方法中由于黄瓜叶片和设施遮挡导致识别准确率不高的问题,提出一种名为SlowFast-SMC-ECA (SlowFast-Spatio-Temporal Excitation、Channel Excitation、Motion Excitation-Efficient Channel Attention)的农事活动行为识别算法。[方法]该算法主要基于SlowFast模型,通过改进Fast Pathway和Slow Pathway中的网络结构来提高对于农事活动中手部动作特征和关键特征的提取能力。在Fast Pathway中,引入多路径激励残差网络的概念,通过在信道之间插入卷积操作来增强它们在时域上的相互关联性,从而更好地捕捉快速运动信息的细微时间变化。在Slow Pathway中,将传统的Residual Block替换为ECA-Res结构,以提高对通道信息的捕获能力。这两项改进有效地加强了通道之间的联系,提升了特征之间的语义信息传递,进而显著提升了农事行为识别的准确率。此外,为了解决数据集中类别不均衡的问题,设计了平衡损失函数(Smoothing Loss),通过引入正则化系数,平衡损失函数可以有效地处理数据集中的类别不均衡情况,提高模型在各个类别上的表现。[结果和讨论]改进的SlowFast-SMC-ECA模型在农事行为识别中表现出良好的性能,各类行为的平均识别精度达到80.47%,相较于原始的SlowFast模型有约3.5%的提升。[结论]本研究在农事行为识别中展现出良好的性能。这对农业生产的智能化管理和决策具有重要意义。