为解决数据中心在电力-算力联合市场与低碳配电网协同规划中的问题,提出了一种多目标区间-随机优化方法。通过引入算力租赁机制,优化了资源利用效率与经济效益,同时降低了运营成本与碳排放。针对协同规划中的多目标优化与高维不确定性问...为解决数据中心在电力-算力联合市场与低碳配电网协同规划中的问题,提出了一种多目标区间-随机优化方法。通过引入算力租赁机制,优化了资源利用效率与经济效益,同时降低了运营成本与碳排放。针对协同规划中的多目标优化与高维不确定性问题,设计了一种基于分解的自适应约束处理区间多目标进化算法—采用两种交叉策略(adaptive constraint-handling interval multi-objective evolutionary algorithm based on decomposition with two crossover strategies,ACIMOEA/D-TCS)。该算法能够高效求解帕累托前沿,提供鲁棒性和可行性兼具的优化方案。结果表明,数据中心参与算力市场显著提高了资源利用效率和经济效益,同时有效降低了碳排放。通过对算力资源租赁与配电系统运行的优化,所提模型在经济和环境效益方面取得显著提升,为电力-算力联合市场下的协同规划问题提供了新的理论方法与解决方案。展开更多
This paper proposes a predictive compensation strategy to reduce the detrimental effect of stochastic time delays induced by communication networks on control performance. Values of a manipulated variable at the prese...This paper proposes a predictive compensation strategy to reduce the detrimental effect of stochastic time delays induced by communication networks on control performance. Values of a manipulated variable at the present sampling instant and future time instants can be determined by performing a receding horizon optimal procedure only once. When the present value of the manipulated variable does not arrive at a smart actuator, its predictive one is imposed to the corresponding process. Switching of a manipulated variable between its true present value and the predictive one usually results in unsmooth operation of a control system. This paper shows: 1) for a steady process,as long as its input is sufficiently smooth, the smoothness of its output can be guaranteed; 2) a manipulated variable can be switched smoothly by filtering the manipulated variable just using a simple low-pass filter. Thus the control performance can be improved. Finally, the effectiveness of the proposed method is demonstrated by simulation study.展开更多
当物联网设备(Internet of Things Device,IoTD)面临随机到达且复杂度高的计算任务时,因自身计算资源和能力所限,无法进行实时高效的处理。为了应对此类问题,设计了一种两层无人机辅助的移动边缘计算(Mobile Edge Computing,MEC)模型。...当物联网设备(Internet of Things Device,IoTD)面临随机到达且复杂度高的计算任务时,因自身计算资源和能力所限,无法进行实时高效的处理。为了应对此类问题,设计了一种两层无人机辅助的移动边缘计算(Mobile Edge Computing,MEC)模型。在该模型中,考虑到IoTD处理随机计算任务时的局限性,引入多架配备MEC服务器的下层无人机和单架上层无人机进行协同处理。为了实现系统能耗最优化,提出了一种资源优化和多无人机位置部署方案,根据计算任务到达的随机性,应用李雅普诺夫优化方法将能耗最小化问题转化为一个确定性问题,应用差分进化(Differential Evolution,DE)算法进行多次变异、交叉和选择取得无人机的优化部署方案;采用深度确定性策略梯度(Depth Deterministic policy Gradient,DDPG)算法对带宽分配、计算资源分配、传输功率分配和任务卸载分配进行联合优化。实验结果表明,该算法相较于对比算法系统能耗降低35%,充分验证了其可行性和有效性。展开更多
文摘为了提高辨识稳定图中真实模态的准确性与自动化程度,首先,从稳定点定义方式的角度论述了聚类算法效果欠佳的原因,并采用异阶系统非等权重的定义方式输出稳定点;其次,基于数据挖掘思想,采用改进的辨识聚类结构的有序点(ordering points to identify the clustering structure,简称OPTICS)算法自动清洗稳定点集,通过遍历性搜索的方式确定输入参数;然后,提出结合度矩阵去噪的自适应局部密度谱聚类(local density adaptive spectral clustering,简称SC-DA)算法分析稳定点集,并以簇中值作为模态参数的代表值,实现模态参数的自动化识别;最后,将含有密集模态的外滩大桥作为识别对象进行试验验证。试验结果表明:所提出方法具有较高的精度,与频域分解(frequency domain decomposition,简称FDD)法的频率结果最大相差仅为0.012 3 Hz,且在线识别的准确率达到82.86%,显著高于基于层次聚类的自动识别方法,实现了无人工干预下模态参数的自动、准确识别,具有一定的工程应用前景。
文摘为解决数据中心在电力-算力联合市场与低碳配电网协同规划中的问题,提出了一种多目标区间-随机优化方法。通过引入算力租赁机制,优化了资源利用效率与经济效益,同时降低了运营成本与碳排放。针对协同规划中的多目标优化与高维不确定性问题,设计了一种基于分解的自适应约束处理区间多目标进化算法—采用两种交叉策略(adaptive constraint-handling interval multi-objective evolutionary algorithm based on decomposition with two crossover strategies,ACIMOEA/D-TCS)。该算法能够高效求解帕累托前沿,提供鲁棒性和可行性兼具的优化方案。结果表明,数据中心参与算力市场显著提高了资源利用效率和经济效益,同时有效降低了碳排放。通过对算力资源租赁与配电系统运行的优化,所提模型在经济和环境效益方面取得显著提升,为电力-算力联合市场下的协同规划问题提供了新的理论方法与解决方案。
基金Supported by National Natural Science Foundation of China(10571036)the Key Discipline Development Program of Beijing Municipal Commission (XK100080537)
基金Supported by National High Technology Research and Development Program of P. R. China (2002AA412510)National Natural Science Foundation of P. R. China (60274034)
文摘This paper proposes a predictive compensation strategy to reduce the detrimental effect of stochastic time delays induced by communication networks on control performance. Values of a manipulated variable at the present sampling instant and future time instants can be determined by performing a receding horizon optimal procedure only once. When the present value of the manipulated variable does not arrive at a smart actuator, its predictive one is imposed to the corresponding process. Switching of a manipulated variable between its true present value and the predictive one usually results in unsmooth operation of a control system. This paper shows: 1) for a steady process,as long as its input is sufficiently smooth, the smoothness of its output can be guaranteed; 2) a manipulated variable can be switched smoothly by filtering the manipulated variable just using a simple low-pass filter. Thus the control performance can be improved. Finally, the effectiveness of the proposed method is demonstrated by simulation study.
文摘当物联网设备(Internet of Things Device,IoTD)面临随机到达且复杂度高的计算任务时,因自身计算资源和能力所限,无法进行实时高效的处理。为了应对此类问题,设计了一种两层无人机辅助的移动边缘计算(Mobile Edge Computing,MEC)模型。在该模型中,考虑到IoTD处理随机计算任务时的局限性,引入多架配备MEC服务器的下层无人机和单架上层无人机进行协同处理。为了实现系统能耗最优化,提出了一种资源优化和多无人机位置部署方案,根据计算任务到达的随机性,应用李雅普诺夫优化方法将能耗最小化问题转化为一个确定性问题,应用差分进化(Differential Evolution,DE)算法进行多次变异、交叉和选择取得无人机的优化部署方案;采用深度确定性策略梯度(Depth Deterministic policy Gradient,DDPG)算法对带宽分配、计算资源分配、传输功率分配和任务卸载分配进行联合优化。实验结果表明,该算法相较于对比算法系统能耗降低35%,充分验证了其可行性和有效性。
