Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenario...Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.展开更多
为构建科学合理的城市道路自动驾驶测试场景,基于美国加利福尼亚州机动车管理局(California Department of Motor Vehicles, DMV)2021—2023年公开的280起自动驾驶汽车(Autonomous Vehicle, AV)碰撞事故报告,挖掘典型危险场景并完成测...为构建科学合理的城市道路自动驾驶测试场景,基于美国加利福尼亚州机动车管理局(California Department of Motor Vehicles, DMV)2021—2023年公开的280起自动驾驶汽车(Autonomous Vehicle, AV)碰撞事故报告,挖掘典型危险场景并完成测试场景转化。首先,通过多元Logistic回归分析提取人员受伤情况的显著影响因素。其次,引入独热编码(One-Hot Encoding)对分类变量进行二进制向量转换,消除传统标签编码的数值顺序偏差。然后,采用二阶聚类算法挖掘典型危险场景组,并进一步通过交叉表分析场景组与事故结果变量、道路环境变量间的关联性。最后,将危险场景转化设计为自动驾驶测试场景。结果显示,独热编码处理后的变量,聚类质量较传统方法提升50%;聚类分析共识别出12类典型危险场景,且交叉表分析表明场景组与事故结果及道路环境变量显著相关;进一步结合事故机理与测试需求,将这12类危险场景归纳为6类代表性测试场景,其中“AV停止或减速状态下被后方直行车辆追尾”的场景最为典型,在全部场景中占比46.1%。研究表明,独热编码方法显著提升了聚类分析的准确性,基于真实事故数据的场景聚类方法能识别AV在城市道路的事故模式,并为自动驾驶测试场景库的优先级划分与标准化设计提供数据驱动支撑。展开更多
随着光伏电源大规模入网,光伏出力的不确定性给电网规划运行带来了挑战。针对流域光伏集群出力不确定性的描述问题,引入参数离心系数与均匀缩放系数改进(density-based spatial clustering of application with noise,DBSCAN)算法,对出...随着光伏电源大规模入网,光伏出力的不确定性给电网规划运行带来了挑战。针对流域光伏集群出力不确定性的描述问题,引入参数离心系数与均匀缩放系数改进(density-based spatial clustering of application with noise,DBSCAN)算法,对出力数据去噪重构,使用Kmeans++算法对去噪后数据进行场景提取,基于此提出了一种耦合DBSCAN-Kmeans++算法的流域光伏集群出力的场景提取方法。此方法应用于某省A、B、C 3个流域的实例研究表明,三江枯水期场景提取的F值较原始方法分别提升了57.68%、182.74%、57.41%。研究生成了三江丰枯期光伏出力场景集,提取结果与光伏年内出力特性相符,对比三江出力场景,表明B流域的光伏效能更优。所提出的方法可用于有噪数据环境下多站尺度的光伏出力不确定性描述。展开更多
基金the Science and Technology Project of State Grid Corporation of China,Grant Number 5108-202304065A-1-1-ZN.
文摘Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.
文摘为构建科学合理的城市道路自动驾驶测试场景,基于美国加利福尼亚州机动车管理局(California Department of Motor Vehicles, DMV)2021—2023年公开的280起自动驾驶汽车(Autonomous Vehicle, AV)碰撞事故报告,挖掘典型危险场景并完成测试场景转化。首先,通过多元Logistic回归分析提取人员受伤情况的显著影响因素。其次,引入独热编码(One-Hot Encoding)对分类变量进行二进制向量转换,消除传统标签编码的数值顺序偏差。然后,采用二阶聚类算法挖掘典型危险场景组,并进一步通过交叉表分析场景组与事故结果变量、道路环境变量间的关联性。最后,将危险场景转化设计为自动驾驶测试场景。结果显示,独热编码处理后的变量,聚类质量较传统方法提升50%;聚类分析共识别出12类典型危险场景,且交叉表分析表明场景组与事故结果及道路环境变量显著相关;进一步结合事故机理与测试需求,将这12类危险场景归纳为6类代表性测试场景,其中“AV停止或减速状态下被后方直行车辆追尾”的场景最为典型,在全部场景中占比46.1%。研究表明,独热编码方法显著提升了聚类分析的准确性,基于真实事故数据的场景聚类方法能识别AV在城市道路的事故模式,并为自动驾驶测试场景库的优先级划分与标准化设计提供数据驱动支撑。
文摘随着光伏电源大规模入网,光伏出力的不确定性给电网规划运行带来了挑战。针对流域光伏集群出力不确定性的描述问题,引入参数离心系数与均匀缩放系数改进(density-based spatial clustering of application with noise,DBSCAN)算法,对出力数据去噪重构,使用Kmeans++算法对去噪后数据进行场景提取,基于此提出了一种耦合DBSCAN-Kmeans++算法的流域光伏集群出力的场景提取方法。此方法应用于某省A、B、C 3个流域的实例研究表明,三江枯水期场景提取的F值较原始方法分别提升了57.68%、182.74%、57.41%。研究生成了三江丰枯期光伏出力场景集,提取结果与光伏年内出力特性相符,对比三江出力场景,表明B流域的光伏效能更优。所提出的方法可用于有噪数据环境下多站尺度的光伏出力不确定性描述。
