针对新能源机车(new energy locomotives,NELs)接入光储(photovoltaic energy storage,PVES)微电网的迫切现实需求,提出基于洪水算法的机务段微电网光储容量优化配置方法。首先,结合新能源机车蓄电池、储能站等柔性负荷特征,构建面向新...针对新能源机车(new energy locomotives,NELs)接入光储(photovoltaic energy storage,PVES)微电网的迫切现实需求,提出基于洪水算法的机务段微电网光储容量优化配置方法。首先,结合新能源机车蓄电池、储能站等柔性负荷特征,构建面向新能源机车机务段微电网的光储容量优化配置数学模型。其次,以负荷缺电率、弃光率、光伏能源渗透率作为评价指标,选取春夏秋冬4个典型日进行算例分析。最后,采用洪水算法对所建模型进行求解,并对比分析3种新能源机车充电方案和4种优化算法。仿真结果表明,洪水算法能够在更少的迭代次数内达到最优解,证实了其在复杂优化问题中的高效性和优越性。优化所得配置方案充分调动了新能源机车蓄电池充电这一柔性负荷的灵活性,实现了“荷随源变”,平均光伏发电渗透率达93.98%,保证了矿区铁路运输的低碳化和经济性。展开更多
With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an i...With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.展开更多
The TOPKAPI (TOPographic Kinematic APproximation and Integration) model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-r...The TOPKAPI (TOPographic Kinematic APproximation and Integration) model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-runoff and runoff routing processes are described by three nonlinear reservoir differential equations that are structurally similar and describe different hydrological and hydraulic processes. Equations are integrated over grid cells that describe the geometry of the catchment, leading to a cascade of nonlinear reservoir equations. For the sake of improving the model's computation precision, this paper provides the general form of these equations and describes the solution by means of a numerical algorithm, the variable-step fourth-order Runge-Kutta algorithm. For the purpose of assessing the quality of the comprehensive numerical algorithm, this paper presents a case study application to the Buliu River Basin, which has an area of 3 310 km^2, using a DEM (digital elevation model) grid with a resolution of 1 km. The results show that the variable-step fourth-order Runge-Kutta algorithm for nonlinear reservoir equations is a good approximation of subsurface flow in the soil matrix, overland flow over the slopes, and surface flow in the channel network, allowing us to retain the physical properties of the original equations at scales ranging from a few meters to 1 km.展开更多
摘要针对新能源机车(new energy locomotives,NELs)接入光储(photovoltaic energy storage,PVES)微电网的迫切现实需求,提出基于洪水算法的机务段微电网光储容量优化配置方法。首先,结合新能源机车蓄电池、储能站等柔性负荷特征,构建面向新能源机车机务段微电网的光储容量优化配置数学模型。其次,以负荷缺电率、弃光率、光伏能源渗透率作为评价指标,选取春夏秋冬4个典型日进行算例分析。最后,采用洪水算法对所建模型进行求解,并对比分析3种新能源机车充电方案和4种优化算法。仿真结果表明,洪水算法能够在更少的迭代次数内达到最优解,证实了其在复杂优化问题中的高效性和优越性。优化所得配置方案充分调动了新能源机车蓄电池充电这一柔性负荷的灵活性,实现了“荷随源变”,平均光伏发电渗透率达93.98%,保证了矿区铁路运输的低碳化和经济性。
基金supported by National Natural Science Foundation of China (61471109, 61501104 and 91438110)Fundamental Research Funds for the Central Universities ( N140405005 , N150401002 and N150404002)Open Fund of IPOC (BUPT, IPOC2015B006)
摘要With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.
基金supported by the National Natural Science Foundation of China(Grant No.50479017)the Program for Changjiang Scholars and Innovative Research Teams in Universities(Grant No.IRT071)
摘要The TOPKAPI (TOPographic Kinematic APproximation and Integration) model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-runoff and runoff routing processes are described by three nonlinear reservoir differential equations that are structurally similar and describe different hydrological and hydraulic processes. Equations are integrated over grid cells that describe the geometry of the catchment, leading to a cascade of nonlinear reservoir equations. For the sake of improving the model's computation precision, this paper provides the general form of these equations and describes the solution by means of a numerical algorithm, the variable-step fourth-order Runge-Kutta algorithm. For the purpose of assessing the quality of the comprehensive numerical algorithm, this paper presents a case study application to the Buliu River Basin, which has an area of 3 310 km^2, using a DEM (digital elevation model) grid with a resolution of 1 km. The results show that the variable-step fourth-order Runge-Kutta algorithm for nonlinear reservoir equations is a good approximation of subsurface flow in the soil matrix, overland flow over the slopes, and surface flow in the channel network, allowing us to retain the physical properties of the original equations at scales ranging from a few meters to 1 km.