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在家庭能量管理系统中,可再生能源的发电功率具有不确定性和间断性,成为影响家庭能量优化调度的因素。储能系统在优化过程中过多充放电次数也会增加储能折旧费用。针对上述问题,文中提出一种储能分组能量管理优化策略。根据可再生能源出力不确定部分和确定部分为储能系统配置充电部分和调度部分。首先建立风力发电系统、光伏发电系统和储能系统模型,然后在此基础上搭建以每日用电费用最小为目标的家庭能量管理优化调度模型。最后以上海市一住宅用电为例,通过改进遗传算法对模型求解。仿真算例分析表明所提策略降低用电费用的同时可以减小可再生能源发电不确定性对能量优化调度的影响,具有一定的有效性和参考价值。 相似文献
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阐述智能电网调度控制系统的结构和现状,提升电网智能调度控制系统应用效果的措施,包括电网智能运行一体化系统、可信计算与安全免疫技术、短期电力市场多级多时段优化技术、运行方式自描述及动态解析技术。 相似文献
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“碳达峰、碳中和”已经上升成为我国未来发展的重要战略目标,综合能源系统对能源的梯级利用能为这一目标提供助力。以园区综合能源为基本构架,构建一种含燃气轮机、燃气锅炉、电制冷机、电锅炉等机组的冷热电联供系统优化模型。在此基础上,建立一种兼顾设备优化配置及优化运行的双层系统规划模型,外层采用改进粒子群优化算法,以系统年投资最优为目标进行设备容量配置,内层调用CPLEX以运行成本最小为目标,优化各机组出力。最后利用改进后的粒子群优化算法对所建立的规划及优化运行的多约束模型进行求解,验证所提优化配置能够同时兼顾系统的经济性和可靠性。 相似文献
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应用多体动力学方法模拟贴片机系统动态过程,仿真研究贴片机的动态精度问题。根据贴片机的结构形式,建立贴片机的数学模型和虚拟样机模型。用考虑结合部影响的刚柔耦合模型模拟实际系统,仿真分析影响贴片机动态精度的各种因素,根据分析结果建立优化模型,并对比分析原始模型与优化模型的动态精度。采用激光干涉仪对仿真分析结果进行试验验证.试验结果与仿真结果能较好地吻合,验证了该方法的正确性。 相似文献
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扩散相关光谱法(DCS)是一种新兴的无创检测组织深层血流流速变化的光学方法。开发了一种基于多时延光子相关器的多通道DCS拓扑成像系统,该系统主要包含长相干激光器、光子计数式光电倍增管和光子相关器。多时延光子相关器结构可以保证光强时间自相关曲线的高分辨率和大动态范围。结合该系统的成像特点,将基于相关扩散方程解析解的约束非线性优化算法作为模型,实现半无限空间条件下相关扩散方程解析解与实验测得的相关曲线的最佳匹配,以计算血流指数。动态仿体实验验证了该系统可以分辨液体介质的流速,且可以重建出不同流速的二维图像。 相似文献
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针对由蓄电池和氢储能装置的混合储能系统,提出一种基于模型预测-动态规划的混合储能系统能量管理策略,协调能源并网对电网造成的冲击、降低系统能量损耗和储能运行成本。建立混合储能系统功率预测模型,构建罚函数将三个评价目标转化为单一目标求解,约束储能系统的容量、功率等指标,并采用动态规划算法优化蓄电池充放电控制。算例结果表明,该控制策略协调了混合储能的功率分配,具有更好的并网平波抑制、降低能耗效果,微网运行具有良好的经济性。 相似文献
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为了进一步激发综合能源系统的多能互补特性和挖掘需求侧可参与系统优化调度的调节潜力,提升综合能源系统的可靠性水平,提出一种考虑需求响应的综合能源系统可靠性评估方法。首先,根据负荷响应特性引入价格型和替代型两类需求响应,分别建立基于电价弹性矩阵的价格型需求响应模型,及考虑不同能源需求在同一时间点上相互转换的替代型需求响应模型;其次,建立以系统购买能源成本最小与负荷削减惩罚成本最小为目标的优化调度模型,并在Matlab平台上调用CPLEX进行求解;然后,基于时序蒙特卡洛模拟法获得各设备状态并设计出可靠性评估流程;最后,通过北方某工业园区综合能源系统进行算例分析,比较了系统在不同运行方式下的可靠性指标与经济性水平。算例仿真结果表明,引入价格型和替代型需求响应可协调优化各时段用能需求,能够有效提高综合能源系统的可靠性和经济性。 相似文献
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Mao Jianfeng Cassandras Christos G. Zhao Qianchuan 《Mobile Computing, IEEE Transactions on》2007,6(6):678-688
Dynamic voltage scaling is used in energy-limited systems as a means of conserving energy and prolonging their life. We consider a setting in which the tasks performed by such a system are nonpreemptive and aperiodic. Our objective is to control the processing rate over different tasks so as to minimize energy subject to hard real-time processing constraints. Under any given task scheduling policy, we prove that the optimal solution to the offline version of the problem can be efficiently obtained by exploiting the structure of optimal sample paths, leading to a new dynamic voltage scaling algorithm termed the critical task decomposition algorithm (CTDA). The efficiency of the algorithm rests on the existence of a set of critical tasks that decompose the optimal sample path into decoupled segments within which optimal processing times are easily determined. The algorithm is readily extended to an online version of the problem as well. Its worst-case complexity of both offline and online problems is O(N2) 相似文献
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针对实时系统能耗管理中动态电压调节(DVS)技术的应用会导致系统可靠性下降的问题,该文提出一种基于改进鸟群(IoBSA)算法的动态能耗管理法。首先,采用佳点集原理均匀地初始化种群,从而提高初始解的质量,有效增强种群多样性;其次,为了更好地平衡BSA算法的全局和局部搜索能力,提出非线性动态调整因子;接着,针对嵌入式实时系统中处理器频率可以动态调整的特点,建立具有时间和可靠性约束的功耗模型;最后,在保证实时性和稳定性的前提下,利用提出的IoBSA算法,寻求最小能耗的解决方案。通过实验结果表明,与传统BSA等常见算法相比,改进鸟群算法在求解最小能耗上有着很强的优势及较快的处理速度。 相似文献
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Thanks to the rapidly development of optimization algorithm, more energy can be saved in the communication system when executing an application. In recent years, allocating limited resources in a cooperative manner to maximize energy efficiency in emerging sensor networks has attracted a lot of attention. In this paper, a one-layer projection neural network subject to linear equalities and bound constraints is introduced and applied in mobile wireless sensor network to make it energy-efficient by reasonably allocating local and remote data sizes when processing an application within a certain period of time. Firstly, an optimal partition to minimize the total energy consumption of the local and helper sensor nodes is proposed. Then, the neural network model is described and the optimality and convergence of the proposed model are analyzed. Finally, some simulation results are given to show that the proposed algorithm is very effective to solve the energy efficient cooperative computing. 相似文献
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针对机动目标的高动态属性导致雷达系统不能精确地分配系统资源问题,本文提出了一种基于改进的当前统计模型的组网机会阵雷达功率分配算法。该算法通过改进的当前统计模型预测机动目标运动状态,采用预测的条件克拉美罗界作为功率分配时目标跟踪性能的衡量基准。针对目标信息的不确定性,引入随机变量表征目标RCS,建立基于机会约束规划的功率资源分配模型,并设计混合智能优化算法求解满足机会约束的最优功率分配。仿真结果表明,预测的条件克拉美罗界能够提供一个更加精确的跟踪性能衡量边界,该算法能够有效提高雷达系统资源利用率。 相似文献
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A low complexity asymptotic regularized zero forcing cooperative beamforming algorithm based on energy efficiency in heterogeneous massive MIMO system was proposed,aiming at the problem that the current multi-flow regularization zero forcing beamforming algorithm sets the power constraint of each antenna in the regularization term as a fixed value and ignores the influences of factors such as the number of antennas,the number of users and QoS.The algorithm selects the optimal antenna power constraint set through the optimization method,and the optimal beamforming was asymptotically ob-tained to balance the interference among users to achieve the optimal energy efficiency,considering the impact of the number of antennas and users with the constraints of the antenna power and QoS.In view of the importance of backhaul in massive MIMO system,a backhaul power consumption model and the impact of backhaul power consumption on system performance was analyzed.Analysis and simulation results show that the proposed algorithm has great improvement of the performance,especially when the number of antennas is large.The algorithm is close to optimal performance,especially suitable for massive MIMO system of next generation communication. 相似文献
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针对NFV/SDN架构下,服务功能链(SFC)的资源需求动态变化引起的虚拟网络功能(VNF)迁移优化问题,该文提出一种基于深度强化学习的VNF迁移优化算法。首先,在底层CPU、带宽资源和SFC端到端时延约束下,建立基于马尔可夫决策过程(MDP)的随机优化模型,该模型通过迁移VNF来联合优化网络能耗和SFC端到端时延。其次,由于状态空间和动作空间是连续值集合,提出一种基于深度确定性策略梯度(DDPG)的VNF智能迁移算法,从而得到近似最优的VNF迁移策略。仿真结果表明,该算法可以实现网络能耗和SFC端到端时延的折中,并提高物理网络的资源利用率。 相似文献
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Energy optimal control for time-varying wireless networks 总被引:1,自引:0,他引:1
Neely M.J. 《IEEE transactions on information theory / Professional Technical Group on Information Theory》2006,52(7):2915-2934
We develop a dynamic control strategy for minimizing energy expenditure in a time-varying wireless network with adaptive transmission rates. The algorithm operates without knowledge of traffic rates or channel statistics, and yields average power that is arbitrarily close to the minimum possible value achieved by an algorithm optimized with complete knowledge of future events. Proximity to this optimal solution is shown to be inversely proportional to network delay. We then present a similar algorithm that solves the related problem of maximizing network throughput subject to peak and average power constraints. The techniques used in this paper are novel and establish a foundation for stochastic network optimization 相似文献