柔性多状态开关(soft normal open points,SNOP)接入配电网可实现负荷的不间断供电,而且能改善故障期间网络的电能质量。该文首先建立SNOP的数学模型,分析在正常状态下SNOP控制方式和潮流方向,以及故障状态下与配电自动化相配合的过程...柔性多状态开关(soft normal open points,SNOP)接入配电网可实现负荷的不间断供电,而且能改善故障期间网络的电能质量。该文首先建立SNOP的数学模型,分析在正常状态下SNOP控制方式和潮流方向,以及故障状态下与配电自动化相配合的过程。然后,建立两阶段故障恢复模型,第一阶段以失负荷风险最小为目标函数,求解获得开关状态与SNOP各端口功率变化范围;不改变网络拓扑,第二阶段引入区间数描述分布式电源和负荷预测的不确定性,将鲁棒优化应用于故障恢复期间的运行状态优化,并对不满足约束的情况进行拓扑调整后重新优化。最后,以三端SNOP连接的3个IEEE33节点系统作为测试算例,分析误差,分布式电源渗透率和SNOP单端容量/线路容量对优化策略的影响。展开更多
Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Parti...Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase.The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style.Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.展开更多
Well-known oil recovery factor estimation techniques such as analogy,volumetric calculations,material balance,decline curve analysis,hydrodynamic simulations have certain limitations.Those techniques are time-consumin...Well-known oil recovery factor estimation techniques such as analogy,volumetric calculations,material balance,decline curve analysis,hydrodynamic simulations have certain limitations.Those techniques are time-consuming,and require specific data and expert knowledge.Besides,though uncertainty estimation is highly desirable for this problem,the methods above do not include this by default.In this work,we present a data-driven technique for oil recovery factor(limited to water flooding)estimation using reservoir parameters and representative statistics.We apply advanced machine learning methods to historical worldwide oilfields datasets(more than 2000 oil reservoirs).The data-driven model might be used as a general tool for rapid and completely objective estimation of the oil recovery factor.In addition,it includes the ability to work with partial input data and to estimate the prediction interval of the oil recovery factor.We perform the evaluation in terms of accuracy and prediction intervals coverage for several tree-based machine learning techniques in application to the following two cases:(1)using parameters only related to geometry,geology,transport,storage and fluid properties,(2)using an extended set of parameters including development and production data.For both cases,the model proved itself to be robust and reliable.We conclude that the proposed data-driven approach overcomes several limitations of the traditional methods and is suitable for rapid,reliable and objective estimation of oil recovery factor for hydrocarbon reservoir.展开更多
文摘柔性多状态开关(soft normal open points,SNOP)接入配电网可实现负荷的不间断供电,而且能改善故障期间网络的电能质量。该文首先建立SNOP的数学模型,分析在正常状态下SNOP控制方式和潮流方向,以及故障状态下与配电自动化相配合的过程。然后,建立两阶段故障恢复模型,第一阶段以失负荷风险最小为目标函数,求解获得开关状态与SNOP各端口功率变化范围;不改变网络拓扑,第二阶段引入区间数描述分布式电源和负荷预测的不确定性,将鲁棒优化应用于故障恢复期间的运行状态优化,并对不满足约束的情况进行拓扑调整后重新优化。最后,以三端SNOP连接的3个IEEE33节点系统作为测试算例,分析误差,分布式电源渗透率和SNOP单端容量/线路容量对优化策略的影响。
基金supported by the National Natural Science Foundation of China(51479158)the Fundamental Research Funds for the Central Universities(WUT:2018III061GX)
文摘Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase.The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style.Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.
基金The work of Evgeny Burnaev in Sections was supported by Ministry of Science and Higher Education grant No.075-10-2021-068.
文摘Well-known oil recovery factor estimation techniques such as analogy,volumetric calculations,material balance,decline curve analysis,hydrodynamic simulations have certain limitations.Those techniques are time-consuming,and require specific data and expert knowledge.Besides,though uncertainty estimation is highly desirable for this problem,the methods above do not include this by default.In this work,we present a data-driven technique for oil recovery factor(limited to water flooding)estimation using reservoir parameters and representative statistics.We apply advanced machine learning methods to historical worldwide oilfields datasets(more than 2000 oil reservoirs).The data-driven model might be used as a general tool for rapid and completely objective estimation of the oil recovery factor.In addition,it includes the ability to work with partial input data and to estimate the prediction interval of the oil recovery factor.We perform the evaluation in terms of accuracy and prediction intervals coverage for several tree-based machine learning techniques in application to the following two cases:(1)using parameters only related to geometry,geology,transport,storage and fluid properties,(2)using an extended set of parameters including development and production data.For both cases,the model proved itself to be robust and reliable.We conclude that the proposed data-driven approach overcomes several limitations of the traditional methods and is suitable for rapid,reliable and objective estimation of oil recovery factor for hydrocarbon reservoir.
