Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and proces...Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.展开更多
Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading conditions.Therefore,before installing solar arrays,it is necessary to simulate and determine the pos...Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading conditions.Therefore,before installing solar arrays,it is necessary to simulate and determine the possible power generated.Maximum power point tracking is needed in order to make sure that,at any time,the maximum power will be extracted from the photovoltaic system.However,maximum power point tracking is not a suitable solution for mismatches and partial shading conditions.To overcome the drawbacks of maximum power point tracking due to mismatches and shadows,distributed maximum power point tracking is util-ized in this paper.The solar farm can be distributed in different ways,including one DC-DC converter per group of modules or per module.In this paper,distributed maximum power point tracking per module is implemented,which has the highest efficiency.This technology is applied to electric vehicles(EVs)that can be charged with a Level 3 charging station in<1 hour.However,the problem is that charging an EV in<1 hour puts a lot of stress on the power grid,and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate.Therefore,a Level 3(fast DC)EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue.Finally,the simulation result is reported using MATLAB®,LTSPICE and the System Advisor Model.Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day,which is enough to fully charge~120 EVs each day.Additionally,the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants.For example,instead of supplying EVs with power from coal-fired power plants,1989 pounds of CO_(2) will be eliminated from the air per hour.展开更多
文摘Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.
基金supported by the National Key Research and Development Program(No.2022YFB3306100)the Aeronautical Science Fund of China(No.2019ZE105001)the General Project of Chongqing Natural Science Foundation(No.cstc2019jcyjmsxmX0530).
基金support of the National Science Foundation(NSF)under Award Number:2115427 is gratefully acknowledged.SRS RN:Sustainable Transportation Electrification for an Equitable and Resilient Society(STEERS).
文摘Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading conditions.Therefore,before installing solar arrays,it is necessary to simulate and determine the possible power generated.Maximum power point tracking is needed in order to make sure that,at any time,the maximum power will be extracted from the photovoltaic system.However,maximum power point tracking is not a suitable solution for mismatches and partial shading conditions.To overcome the drawbacks of maximum power point tracking due to mismatches and shadows,distributed maximum power point tracking is util-ized in this paper.The solar farm can be distributed in different ways,including one DC-DC converter per group of modules or per module.In this paper,distributed maximum power point tracking per module is implemented,which has the highest efficiency.This technology is applied to electric vehicles(EVs)that can be charged with a Level 3 charging station in<1 hour.However,the problem is that charging an EV in<1 hour puts a lot of stress on the power grid,and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate.Therefore,a Level 3(fast DC)EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue.Finally,the simulation result is reported using MATLAB®,LTSPICE and the System Advisor Model.Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day,which is enough to fully charge~120 EVs each day.Additionally,the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants.For example,instead of supplying EVs with power from coal-fired power plants,1989 pounds of CO_(2) will be eliminated from the air per hour.
