针对锂离子电池荷电状态(State of Charge, SOC)的预测精度问题,提出了一种基于双重状态转移算法优化的径向基函数(RadialBasisFunction,RBF)神经网络的锂离子电池SOC估算方法。该方法将K-means聚类算法运用于RBF神经网络隐含层个数的确...针对锂离子电池荷电状态(State of Charge, SOC)的预测精度问题,提出了一种基于双重状态转移算法优化的径向基函数(RadialBasisFunction,RBF)神经网络的锂离子电池SOC估算方法。该方法将K-means聚类算法运用于RBF神经网络隐含层个数的确定,并采用状态转移算法(State Transition Algorithm, STA)对K-means聚类算法进行优化,合理确定了RBF神经网络的网络结构。基于最优的网络结构,利用STA调整网络的参数,包括核函数中心点、宽度和连接权值。将训练好的RBF神经网络用于估算锂离子电池SOC。为了证明所提的混合算法的有效性,使用安时积分法和BP神经网络算法进行对比。结果表明,该方法优于其他方法。展开更多
The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in futu...The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.展开更多
基金Project(2022YFC2904502)supported by the National Key Research and Development Program of ChinaProject(62273357)supported by the National Natural Science Foundation of China。
文摘The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.
