随着全球气候变暖和海平面上升,台风发生的频次和强度呈逐年增大的趋势。由台风引发的灾害及带来的后果促使人们必须重新评估核电站的海岸防护标准。文中对核电安全规定中的"可能最大台风"(Probable Maximum Typhoon)、"...随着全球气候变暖和海平面上升,台风发生的频次和强度呈逐年增大的趋势。由台风引发的灾害及带来的后果促使人们必须重新评估核电站的海岸防护标准。文中对核电安全规定中的"可能最大台风"(Probable Maximum Typhoon)、"可能最大暴潮"(Probable Maximum Storm Surge)、"设计基准洪水"(Design Basic Flood)进行了概率分析,并用"双层嵌套多目标概率模式"(DLNMPM)对设计规范计算结果进行了修正。展开更多
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero....Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.展开更多
针对高渗透率分布式新能源接入低压配电网中引起局部节点电压偏高的问题,提出考虑空调群虚拟储能的配电网电压无功优化控制模型。首先基于楼宇蓄热特性对电热虚拟储能进行建模,该模型以配电网日运行成本、电压偏差最小为目标函数,采用...针对高渗透率分布式新能源接入低压配电网中引起局部节点电压偏高的问题,提出考虑空调群虚拟储能的配电网电压无功优化控制模型。首先基于楼宇蓄热特性对电热虚拟储能进行建模,该模型以配电网日运行成本、电压偏差最小为目标函数,采用多目标模糊规划模型,合理控制配电网中分布式能源、储能电池、静止无功补偿装置(static var generator,SVG)和上级电网的输出功率。最后以南方夏季系统运行场景为例,对控制系统进行仿真实验。通过IEEE 33节点系统仿真验证,结果表明:考虑虚拟储能的低压配电网系统在保证建筑内人体舒适度的同时对于维持电压稳定性具有良好的效果,并削减了储能电池充放电次数与深度,降低了配电网的日运行成本。展开更多
文摘随着全球气候变暖和海平面上升,台风发生的频次和强度呈逐年增大的趋势。由台风引发的灾害及带来的后果促使人们必须重新评估核电站的海岸防护标准。文中对核电安全规定中的"可能最大台风"(Probable Maximum Typhoon)、"可能最大暴潮"(Probable Maximum Storm Surge)、"设计基准洪水"(Design Basic Flood)进行了概率分析,并用"双层嵌套多目标概率模式"(DLNMPM)对设计规范计算结果进行了修正。
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJ-Z03)the Science and Technology Innovation Program of Humnan Province(2023RC1002)。
文摘Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
文摘针对高渗透率分布式新能源接入低压配电网中引起局部节点电压偏高的问题,提出考虑空调群虚拟储能的配电网电压无功优化控制模型。首先基于楼宇蓄热特性对电热虚拟储能进行建模,该模型以配电网日运行成本、电压偏差最小为目标函数,采用多目标模糊规划模型,合理控制配电网中分布式能源、储能电池、静止无功补偿装置(static var generator,SVG)和上级电网的输出功率。最后以南方夏季系统运行场景为例,对控制系统进行仿真实验。通过IEEE 33节点系统仿真验证,结果表明:考虑虚拟储能的低压配电网系统在保证建筑内人体舒适度的同时对于维持电压稳定性具有良好的效果,并削减了储能电池充放电次数与深度,降低了配电网的日运行成本。
