Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor Networks

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

Stackelberg Game-Based Optimal Dispatch for PEDF Park and Power Grid Interaction under Multiple Incentive Mechanisms

The integration of photovoltaic,energy storage,direct current,and flexible load(PEDF)technologies in building power systems is an importantmeans to address the energy crisis and promote the development of green buildings.The friendly interaction between the PEDF systems and the power grid can promote the utilization of renewable energy and enhance the stability of the power grid.For this purpose,this work introduces a framework of multiple incentive mechanisms for a PEDF park,a building energy system that implements PEDF technologies.The incentive mechanisms proposed in this paper include both economic and noneconomic aspects,which is the most significant innovation of this paper.By modeling the relationship between a PEDF park and the power grid into a Stackelberg game,we demonstrate the effectiveness of these incentive measures in promoting the friendly interaction between the two entities.In this game model,the power grid determines on the prices of electricity trading and incentive subsidy,aiming to maximize its revenue while reducing the peak load of the PEDF park.On the other hand,the PEDF park make its dispatch plan according to the prices established by the grid,in order to reduce electricity consumption expense,improve electricity utility,and enhance the penetration rate of renewable energy.The results show that the proposed incentive mechanisms for the PEDF park can help to optimize energy consumption and promote sustainable energy practices.

Research on Operation Optimization of Energy Storage Power Station and Integrated Energy Microgrid Alliance Based on Stackelberg Game

With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.

基于Stackelberg博弈的V2G交易激励策略研究

随着电动汽车(EV)渗透率升高,电动汽车入网(V2G)技术帮助电网削峰填谷的优势逐渐显现,电力需求侧管理服务机构的中介作用日益凸显。针对V2G交易用户参与积极性较低的问题,提出基于Stackelberg博弈的V2G交易激励策略。首先,构建电网和EV用户间的Stackelberg博弈模型,运用库恩塔克(KKT)条件求解模型最优解;然后,分析不同激励策略对用户参与V2G交易积极性、V2G交易电量、用户期望收益和电网期望收益的影响;最后,通过算例分析验证了模型的可行性及所提策略的有效性。

基于Stackelberg-Cooperative博弈的多微网负荷聚合商优化运行策略

未来电网形态不断演变,柔性资源类型全面开放,为利用其发电、用电的时空互补特性,提高能源利用水平,提出一种基于混合博弈的多微网负荷聚合商电、热共享的经济调度策略。首先构建Starberg-Cooperative博弈模型框架,阐述多微网运营商与多类型负荷聚合商之间的互动与合作,将多微网运营商作为领导者(leader),负荷聚合商联盟作为跟随者(follower),构建主从博弈的能源供给模型,引入共享储能系统,建立负荷聚合商联盟与储能运营商能量共享的合作博弈模型。其次,基于纳什谈判理论对多微网负荷聚合商联盟的合作收益进行分配,保证能量共享联盟的稳定性。最后,利用新疆某地区数据构建算例来验证所提策略的有效性与可行性。结果表明,所提方法有效协调了多微网运营商与负荷聚合商的市场势力,降低了负荷聚合商的运行成本,提高了负荷聚合商联盟与共享储能运营商之间的能量共享利用程度。

Impulsive thrust strategy for orbital pursuit-evasion games based on impulse-like constraint

This paper proposes a novel impulsive thrust strategy guided by optimal continuous thrust strategy to address two-player orbital pursuit-evasion game under impulsive thrust control.The strategy seeks to enhance the interpretability of impulsive thrust strategy by integrating it within the framework of differential game in traditional continuous systems.First,this paper introduces an impulse-like constraint,with periodical changes in thrust amplitude,to characterize the impulsive thrust control.Then,the game with the impulse-like constraint is converted into the two-point boundary value problem,which is solved by the combined shooting and deep learning method proposed in this paper.Deep learning and numerical optimization are employed to obtain the guesses for unknown terminal adjoint variables and the game terminal time.Subsequently,the accurate values are solved by the shooting method to yield the optimal continuous thrust strategy with the impulse-like constraint.Finally,the shooting method is iteratively employed at each impulse decision moment to derive the impulsive thrust strategy guided by the optimal continuous thrust strategy.Numerical examples demonstrate the convergence of the combined shooting and deep learning method,even if the strongly nonlinear impulse-like constraint is introduced.The effect of the impulsive thrust strategy guided by the optimal continuous thrust strategy is also discussed.

The P_(5)-saturation Game

Let F,G and H be three graphs with G■H.We call G an F-saturated graph relative to H,if there is no copy of F in G but there is a copy of F in G+e for any e∈E(H)\E(G).The F-saturation game on host graph H consists of two players,named Max and Min,who alternately add edges of H to G such that each chosen edge avoids creating a copy of F in G,and the players continue to choose edges until G becomes F-saturated relative to H.Max wishes to maximize the length of the game,while Min wishes to minimize the process.Let sat_(g)(F,H)(resp.sat_(g)'(F,H))denote the number of edges chosen when Max(resp.when Min)starts the game and both players play optimally.In this article,we show that sat_(g)(P_(5),K_(n))=sat_(g)'(P_(5),K_(n))=n+2 for n≥15,and satg(P_(5),K_(m,n)),sat_(g)'(P_(5),K_(m,n))lie in{m+n-[m+2/4];m+n-[m-3/4]}if n≥5/2m and m≥4,respectively.

基于Stackelberg Game诱导策略的网络调度算法

首先建立了网络Stackelberg Game模型,分析了该模型下Nash均衡的存在性,给出了网络的队最优解。在网络资源管理中,引入基于Stackelberg Game的网络诱导策略,利用动态博弈和多次逐步诱导的方法,提出了一种网络由一般状态到最优运行状态的动态调度算法。队最优解保证了网络在最优运行状态下的稳定性。数值仿真验证了该算法的有效性。

基于Stackelberg Game模型的网络性能研究

通过对多局域世界模型(Multi-Local-World Model,MLWM)的分析,抽取出一个基本的拓扑子结构,并将主从博弈的思想引入到网络流量控制中,建立了一个基于网络拓扑的Stackelberg博弈模型.在该模型下,分析了Nash均衡的存在性与唯一性,求出了Nash均衡时最佳的链路价格和流量,给出了网络最佳运行状态的一种定量描述,最后分析了网络拓扑参数对网络最佳运行状态的影响,研究了该模型下网络拓扑变化引起的网络性能的演化特性.

车联网中基于三方Stackelberg博弈的动态多媒体定价方案

在当前车联网的应用场景下,中继车辆数据转发的积极性低下与存储空间有限,导致用户体验质量(QoE)降低,为此提出基于三方Stackelberg博弈的动态多媒体定价方案.为了激励中继车辆参与转发多媒体内容,提出多媒体内容定价框架,其中中继车辆获得全额佣金后向路侧单元(RSU)支付部分佣金.设计基于Stackelberg博弈的动态定价模型,根据中继车辆、用户车辆与RSU三方的存储空间利用率、内容数据大小和成本因素,建立各自的效用函数,并将其转化为三方四阶段Stackelberg定价模型.通过反向归纳法证明纳什均衡的存在,实现三方之间的动态定价以得到各自最优策略.仿真结果表明,所提方案有效解决了中继车辆存储空间过载问题,并提高了中继车辆积极性,且在提升用户QoE方面较传统方案具有优势.

基于Stackelberg主从博弈的微电网双层能量管理策略

针对含高比例可再生能源的微电网中能量调度合理性和经济性问题,提出一种双层主从博弈的微电网能量管理策略,模型上层将微电网能量管理中心作为领导者,以售电利润最大化作为优化目标,通过调整购、售电价格策略来降低支出并提高收益,同时当微电网内部供需不平衡时与上级电网进行交互;下层模型将分布式电源运营商和用户作为跟随者,分别以最大售电收益和最小用电成本作为规划目标,通过改变内燃设备出力和用户的用能策略从而优化各主体的利益。经验证,所提模型在Stackelberg均衡框架下具备存在性与唯一性。通过应用优化后的遗传算法进行求解,成功得到各参与方的最优策略。最后经算例验证,所提双层博弈模型能最大化彼此利益,有效管理各方能源使用。

面向无线边缘网络的分层Stackelberg博弈群体激励方法

现有分布式机器学习模型的相关激励机制大多基于单层服务器架构,难以适应当前异构无线计算场景,同时存在计算资源分配不平衡、通信成本高昂等问题.针对上述问题,创新地提出一种面向无线边缘网络的分层Stackelberg博弈群体激励方法(Hierarchical Stackelberg game Swarm Learning Incentive method for wireless edge network,HSISL),创新地将Stackelberg博弈机制引入群体学习模型中,依据各参与方性能差异,云端聚合平台、边缘簇节点、边缘计算节点三方进行动态博弈,通过双定价公平激励过程,共同制定个性化分层资源分配策略,得到模型训练的最优纳什解,有效引导边缘计算模型进行正向加速.通过理论与实验分析,HSISL能够有效提升模型公平性与训练效率,其在MNIST数据集上的准确率可达96.06%.

基于Stackelberg博弈的停车场管理策略

为了解决城市停车难问题,基于Stackelberg博弈论提出了停车场运营者与交通管理者共同决策的城市停车场管理策略。针对停车者、停车场运营者和交通管理者的交互关系,从博弈论角度分析了这三方参与者之间的影响,并建立了这三方参与者的相应效用函数;以交通管理者与停车场运营者利益主体矛盾最小化为目标,基于Stackelberg博弈建立了这三方的博弈模型,确定了实时停车场进出管理原则;基于数值仿真将差分进化算法与Kriging代理模型进行联立对博弈模型进行求解,得到相应停车预约管理方案及三方效用;通过数值仿真将基于Stackelberg博弈得到的方案与停车场运营者利益最大化方案进行对比分析。研究结果表明:在主线流量大、中、小三种情景下,基于Stackelberg博弈方案是各方利益矛盾最小的方案。

双碳目标下基于Stackelberg和合作博弈的虚拟电厂双层优化调度

在中国的双碳目标下,大规模新能源发电接入虚拟电厂时,将导致电力市场交易中的高额惩罚成本。为此,设计了虚拟电厂参与日前市场竞标的双层博弈模型,以双层收益最大化为目标对虚拟电厂进行调度优化。首先,利用场景生成与削减技术考虑风力发电商和电动汽车聚合商的不确定性,设计了由风力发电商、电动汽车聚合商和燃气轮机合作组成的虚拟电厂下层电量竞标模型,以及虚拟电厂控制中心的上层电价竞标模型。其次,基于两个子模型之间的主从递阶关系,利用卡罗需库-恩塔-克(KKT)条件将双层模型转化为一个混合互补问题,通过求解获得优化方案,并基于Shapley值法对合作方进行利润分配。数值算例表明,设计的模型能有效减少碳排放和降低风电惩罚成本,同时增加参与者的收益。

IWSNs防移动干扰节点的两层协作Stackelberg博弈

针对工业无线传感器网络物理层移动干扰攻击节点的攻击问题,研究sink节点和簇头节点共同抵御攻击的两层协作博弈模型。在簇头节点中选择最佳协作节点,共同抵御移动干扰攻击节点;以网络安全速率为优化目标,将sink节点、最佳协作节点和攻击节点之间的合作竞争关系建模为两层协作Stackelberg博弈模型,采用逆向归纳法求解博弈均衡解。仿真实验结果表明,与未采用协作节点的方案相比,采用的最佳协作节点选择方案对移动攻击节点的抵御效果更佳,且与其他协作博弈功率控制模型相比,两层协作Stackelberg博弈模型可有效提高工业无线传感器网络的安全速率。

互补品供应链Stackelberg博弈下纵向决策研究

以两条均由一个制造商和一个零售商构成的互补品供应链为研究对象,探讨互补品供应链采用不同决策方式时的链间Stackelberg博弈和链内Stackelberg博弈问题,研究市场规模、成本等参数对市场价格、需求、利润的影响。进一步地,对链间Stackelberg博弈下的四种决策方式(集中-集中、集中-分散、分散-集中、分散-分散)下的均衡解进行比较分析。研究表明:互补产品生产成本与需求、利润负相关;自身价格与互补产品成本负相关;集中决策是互补品主导供应链的占优策略;对于跟随者供应链而言,采取集中决策还是分散决策方式,首先取决于主导者供应链的决策方式,其次取决于产品之间的互补程度;集中-集中决策方式是互补品供应链的帕累托均衡解。因此,互补品供应链内部成员之间可设计一种增量利润分享机制,确保供应链集中决策的稳定性;互补品供应链之间应注重两个方面:一是相互合作共同降低产品成本,实现降本增效;二是以某条供应链产品进行低价引流,促进消费者对互补产品的需求,进而实现互补品供应链利润最大化。

基于Stackelberg博弈的电炉企业供应链渠道选择模型

为了提升电炉企业供应链渠道的风险抵抗力,设计一种基于Stackelberg博弈的电炉企业供应链渠道选择模型。在Stackelberg博弈中,分析电炉企业供应链成员的Stackelberg博弈条件,作出相关假设;将Stackelberg博弈基本元素与电炉企业供应链元素一一对应,计算实际市场需求量和收益函数,确定电炉企业渠道选择的影响因素,建立物流企业供应链渠道选择模型;设计电炉企业供应链渠道选择步骤,选择最优的电炉企业供应链渠道。实验结果表明:研究方法选择的供应链渠道在内部风险和外部风险条件均获得较高收益,具有较优的抗风险能力。

Multi-Stage Hierarchical Channel Allocation in UAV-Assisted D2D Networks:A Stackelberg Game Approach

UAV-assisted D2D networks can provide auxiliary communication for areas with poor communication facilities by using the characteristics of easy deployment of unmanned aerial vehicle(UAV),then it becomes a promising technology.However,the coexistence of UAV and D2D aggravates the conflict of spectrum resources.In addition,when the UAV performs the communication service,it will inevitably cause the location change,which will make the original channel allocation no longer applicable.Inspired by the influence of frequent channel switching on channel allocation,we define the communication utility as a tradeoff between the throughput and channel switching cost.In the considered model,we investigate the multi-stage hierarchical spectrum access problem with maximizing aggregate communication utilities in UAV-assisted D2D networks.In particular,due to the hierarchical feature of the considered network,we adopt Stackelberg game to formulate this spectrum access problem where both the throughput and channel switching cost are considered.We prove that the proposed game has a stable Stackelberg equilibrium(SE),and the heterogeneous network based channel allocation(HN-CA)algorithm is proposed to achieve the desired solution.Simulation results verify the validity of the proposed game and show the effectiveness of the HN-CA algorithm.

基于Stackelberg博弈的电动汽车充电管理策略

电动汽车的充电效率和充电成本是电动汽车电源管理的重要问题,也是影响电动汽车推广的关键因素。对此,提出一种基于Stackelberg博弈模型的收费控制策略。在博弈中,电网、零售商和用户都参与决策,充电策略可以灵活地满足电网、零售商和用户的不同需求,采用逆归纳法得到博弈模型的均衡解。通过仿真、无序充电过程和基于静态分时电价的充电控制方法对其效益进行比较,分析博弈模型中参数对收费过程的影响,验证了该方法的有效性。

Optimal flexibility dispatch of demand side resources with high penetration of renewables:a Stackelberg game method

To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.