针对电动汽车无序充电行为影响综合能源系统运行经济性的问题,提出一种考虑电动汽车电池荷电状态(state of charge,SOC)弹性电价需求响应的综合能源系统优化调度策略。该策略首先分析了电动汽车充电行为,提出了考虑SOC的电动汽车弹性电...针对电动汽车无序充电行为影响综合能源系统运行经济性的问题,提出一种考虑电动汽车电池荷电状态(state of charge,SOC)弹性电价需求响应的综合能源系统优化调度策略。该策略首先分析了电动汽车充电行为,提出了考虑SOC的电动汽车弹性电价,建立基于电动汽车SOC因子的价格弹性需求响应优化调度模型;其次,在满足系统正常运行的前提下,以综合能源系统运行成本最小为目标,建立含电动汽车需求响应的区域综合能源系统模型,并采用混合线性规划进行求解;最后,根据电动汽车SOC弹性电价引导电动汽车参与需求响应,设置了3个场景进行对比,从电负荷、需求响应和热负荷3个方面分析了综合能源系统设备出力情况和系统运行成本。仿真结果表明,通过SOC弹性电价引导电动汽车参与综合能源系统需求响应,具有较好的可行性和经济性。展开更多
Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of models.Plen...Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of models.Plenty of methods have been pro-posed to defend against adversarial examples.How-ever,the majority of them are suffering the follow-ing weaknesses:1)lack of generalization and prac-ticality.2)fail to deal with unknown attacks.To ad-dress the above issues,we design the adversarial na-ture eraser(ANE)and feature map detector(FMD)to detect fragile and high-intensity adversarial examples,respectively.Then,we apply the ensemble learning method to compose our detector,dealing with adver-sarial examples with diverse magnitudes in a divide-and-conquer manner.Experimental results show that our approach achieves 99.30%and 99.62%Area un-der Curve(AUC)scores on average when tested with various Lp norm-based attacks on CIFAR-10 and Im-ageNet,respectively.Furthermore,our approach also shows its potential in detecting unknown attacks.展开更多
文摘针对电动汽车无序充电行为影响综合能源系统运行经济性的问题,提出一种考虑电动汽车电池荷电状态(state of charge,SOC)弹性电价需求响应的综合能源系统优化调度策略。该策略首先分析了电动汽车充电行为,提出了考虑SOC的电动汽车弹性电价,建立基于电动汽车SOC因子的价格弹性需求响应优化调度模型;其次,在满足系统正常运行的前提下,以综合能源系统运行成本最小为目标,建立含电动汽车需求响应的区域综合能源系统模型,并采用混合线性规划进行求解;最后,根据电动汽车SOC弹性电价引导电动汽车参与需求响应,设置了3个场景进行对比,从电负荷、需求响应和热负荷3个方面分析了综合能源系统设备出力情况和系统运行成本。仿真结果表明,通过SOC弹性电价引导电动汽车参与综合能源系统需求响应,具有较好的可行性和经济性。
基金This work was partly supported by the National Natural Science Foundation of China under No.62372334,61876134,and U1836112.
文摘Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of models.Plenty of methods have been pro-posed to defend against adversarial examples.How-ever,the majority of them are suffering the follow-ing weaknesses:1)lack of generalization and prac-ticality.2)fail to deal with unknown attacks.To ad-dress the above issues,we design the adversarial na-ture eraser(ANE)and feature map detector(FMD)to detect fragile and high-intensity adversarial examples,respectively.Then,we apply the ensemble learning method to compose our detector,dealing with adver-sarial examples with diverse magnitudes in a divide-and-conquer manner.Experimental results show that our approach achieves 99.30%and 99.62%Area un-der Curve(AUC)scores on average when tested with various Lp norm-based attacks on CIFAR-10 and Im-ageNet,respectively.Furthermore,our approach also shows its potential in detecting unknown attacks.
