In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits...In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.展开更多
储能集装箱是锂电池储能电站的核心设备,每个集装箱由数千只电芯串并联构成。因此,对集装箱电芯锂电池荷电状态(state of charge,SOC)的准确估计成为表征储能电站运行最核心最基础的参数,并且为辅助新能源高效并网,储能系统的工作状态...储能集装箱是锂电池储能电站的核心设备,每个集装箱由数千只电芯串并联构成。因此,对集装箱电芯锂电池荷电状态(state of charge,SOC)的准确估计成为表征储能电站运行最核心最基础的参数,并且为辅助新能源高效并网,储能系统的工作状态也会相应地呈现随机性、波动性和不确定性,这对电芯状态估计的准确度提出了更高的要求。为此,首先基于基尔霍夫定律建立Thevenin电池模型,根据安时积分法列出系统的状态和观测方程,并且将其状态和观测方程作为扩展卡尔曼滤波(extended Kalman filtering,EKF)算法的研究对象。然后利用EKF算法对估计值电池SOC更新迭代,再将EKF算法中得到的卡尔曼矩阵和状态变量更新误差值以及UDDS工况下的电池数据,作为长短期记忆(long short-term memory,LSTM)神经网络算法的训练数据集,由此完成LSTM-EKF联合算法,实现对储能集装箱电芯SOC的优化估计。该文所提LSTM-EKF算法可将电芯SOC的误差值降低到1%以下。最后对优化算法在储能电站安全运行与监控平台中的应用情况进行介绍。展开更多
“双碳”背景下风电的渗透率不断提高,将对电力系统的形态和运行机制产生深刻影响。本文提出了一种基于双向长短期记忆Bi-LSTM(bidirectional long short-term memory)循环神经网络的风储系统控制策略。采用双向长短时循环神经网络提取...“双碳”背景下风电的渗透率不断提高,将对电力系统的形态和运行机制产生深刻影响。本文提出了一种基于双向长短期记忆Bi-LSTM(bidirectional long short-term memory)循环神经网络的风储系统控制策略。采用双向长短时循环神经网络提取控制结果与风电场实际出力以及储能状态间的时序信息,通过构建基于双向长短时记忆循环神经网络的控制模型,使得风电场在多种运行工况下能够快速、准确地得到储能系统调节结果。基于实际风电场数据仿真结果表明,本文所提控制策略能够保证在一定经济效益的前提下,将风储系统控制误差保持在0.50%~1.37%。展开更多
基金supported by a State Grid Zhejiang Electric Power Co.,Ltd.Economic and Technical Research Institute Project(Key Technologies and Empirical Research of Diversified Integrated Operation of User-Side Energy Storage in Power Market Environment,No.5211JY19000W)supported by the National Natural Science Foundation of China(Research on Power Market Management to Promote Large-Scale New Energy Consumption,No.71804045).
文摘In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.
文摘“双碳”背景下风电的渗透率不断提高,将对电力系统的形态和运行机制产生深刻影响。本文提出了一种基于双向长短期记忆Bi-LSTM(bidirectional long short-term memory)循环神经网络的风储系统控制策略。采用双向长短时循环神经网络提取控制结果与风电场实际出力以及储能状态间的时序信息,通过构建基于双向长短时记忆循环神经网络的控制模型,使得风电场在多种运行工况下能够快速、准确地得到储能系统调节结果。基于实际风电场数据仿真结果表明,本文所提控制策略能够保证在一定经济效益的前提下,将风储系统控制误差保持在0.50%~1.37%。
