在能源互联网的大背景下,综合能源系统(integrated energy system,IES)和电动汽车(electric vehicles,EV)受到广泛关注。该文提出基于电价引导的IES与EV交互策略,平抑IES总电负荷波动,提高运行效益。首先基于模糊算法建立计及电价影响...在能源互联网的大背景下,综合能源系统(integrated energy system,IES)和电动汽车(electric vehicles,EV)受到广泛关注。该文提出基于电价引导的IES与EV交互策略,平抑IES总电负荷波动,提高运行效益。首先基于模糊算法建立计及电价影响的EV用户充电概率仿真模型,提出动态分时定价策略引导EV有序充电,降低IES总电负荷峰谷差。其次,构建了包含EV电价引导层与IES能量调度层的双层能量调度模型,实现EV与IES的能量协同交互,并研究了基于电价引导的EV充电策略对IES能量优化调度和运行成本的影响。最后以北方地区某IES园区为运行场景开展算例研究,电价引导后IES的峰谷差降低了32.52%,IES运行成本降低了9.93%,EV充电费用降低了19.85%。结果表明,基于电价引导的EV与IES交互策略能够减小IES总电负荷峰谷差,提高IES运行效益并降低EV用户充电费用,且上述效果随EV负荷比重增加而趋于显著。展开更多
To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)mo...To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multi...The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multienergy complementary ways.Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network,a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper.The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm,and thereby achieved a hierarchical and non-repeated sampling.Then,the improved RelieF algorithm is used to identify the feature vectors,calculate the feature weights,and select the preferred feature subset according to the initially set threshold.In addition,a correlation coefficient method is applied to reduce the feature subset,and further eliminate the redundant feature vectors to obtain the optimal feature subset.Finally,the softmax classifier is used to obtain the early warnings of the integrated energy system.Case studies are conducted on an integrated energy system in the south of China to demonstrate the accuracy of fault risk warning method proposed in this paper.展开更多
为增强综合能源系统负荷精细化分解水平,充分利用误差信息以进一步提升预测性能,提出一种基于聚合混合模态分解和时序卷积神经网络(temporal convolutional network,TCN)的综合能源系统负荷修正预测框架。首先,采用改进完全集合经验模...为增强综合能源系统负荷精细化分解水平,充分利用误差信息以进一步提升预测性能,提出一种基于聚合混合模态分解和时序卷积神经网络(temporal convolutional network,TCN)的综合能源系统负荷修正预测框架。首先,采用改进完全集合经验模态分解对电、冷和热负荷初步分解处理,随后利用变分模态分解对具有强复杂性的子序列进一步分解。然后,依据最大信息系数(maximum information coefficient,MIC)分析多元负荷的耦合特性并通过多元相空间重构(multivariate phase space reconstruction,MPSR)丰富特征信息。最后,构建基于TCN的修正预测模型。以校园综合能源系统算例对比不同预测模型,结果显示所提修正预测框架的电、冷和热负荷预测均具有较低的平均绝对百分比误差,有效解决了预测中模态分解的模态混叠以及模态高频分量问题,实现预测误差修正。展开更多
对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重...对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重要的思路,但是现有方法依然存在相似参与方识别精度不高等不足。鉴于此,本文提出一种融合联邦学习和长短期记忆网络(long short-term memory,LSTM)的少数据综合能源多元负荷预测方法(multitask learning based on shared dot product confidentiality under federated learning,MT-SDPFL)。首先,给出一种基于共享向量点积保密协议的相似参与方识别方法,用来从诸多可用的综合能源系统中选出最为相似的参与方;接着,使用参数共享联邦学习算法对选中的各参与方联合训练,结合LSTM和finetune技术建立每个参与方的多元负荷预测模型。将所提方法应用于多个实际能源系统,实验结果表明,该方法可以在数据稀疏的情况下取得高精度的多源负荷预测结果。展开更多
文摘在能源互联网的大背景下,综合能源系统(integrated energy system,IES)和电动汽车(electric vehicles,EV)受到广泛关注。该文提出基于电价引导的IES与EV交互策略,平抑IES总电负荷波动,提高运行效益。首先基于模糊算法建立计及电价影响的EV用户充电概率仿真模型,提出动态分时定价策略引导EV有序充电,降低IES总电负荷峰谷差。其次,构建了包含EV电价引导层与IES能量调度层的双层能量调度模型,实现EV与IES的能量协同交互,并研究了基于电价引导的EV充电策略对IES能量优化调度和运行成本的影响。最后以北方地区某IES园区为运行场景开展算例研究,电价引导后IES的峰谷差降低了32.52%,IES运行成本降低了9.93%,EV充电费用降低了19.85%。结果表明,基于电价引导的EV与IES交互策略能够减小IES总电负荷峰谷差,提高IES运行效益并降低EV用户充电费用,且上述效果随EV负荷比重增加而趋于显著。
基金supported in part by the National Key Research and Development Program of China(No.2018YFB1500800)the National Natural Science Foundation of China(No.51807134)the State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology(No.EERI_KF20200014)。
文摘To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金Supported by National Natural Science Foundation of China(No.51777193).
文摘The integrated energy systems,usually including electric energy,natural gas and thermal energy,play a pivotal role in the energy Internet project,which could improve the accommodation of renewable energy through multienergy complementary ways.Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network,a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper.The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm,and thereby achieved a hierarchical and non-repeated sampling.Then,the improved RelieF algorithm is used to identify the feature vectors,calculate the feature weights,and select the preferred feature subset according to the initially set threshold.In addition,a correlation coefficient method is applied to reduce the feature subset,and further eliminate the redundant feature vectors to obtain the optimal feature subset.Finally,the softmax classifier is used to obtain the early warnings of the integrated energy system.Case studies are conducted on an integrated energy system in the south of China to demonstrate the accuracy of fault risk warning method proposed in this paper.
文摘为增强综合能源系统负荷精细化分解水平,充分利用误差信息以进一步提升预测性能,提出一种基于聚合混合模态分解和时序卷积神经网络(temporal convolutional network,TCN)的综合能源系统负荷修正预测框架。首先,采用改进完全集合经验模态分解对电、冷和热负荷初步分解处理,随后利用变分模态分解对具有强复杂性的子序列进一步分解。然后,依据最大信息系数(maximum information coefficient,MIC)分析多元负荷的耦合特性并通过多元相空间重构(multivariate phase space reconstruction,MPSR)丰富特征信息。最后,构建基于TCN的修正预测模型。以校园综合能源系统算例对比不同预测模型,结果显示所提修正预测框架的电、冷和热负荷预测均具有较低的平均绝对百分比误差,有效解决了预测中模态分解的模态混叠以及模态高频分量问题,实现预测误差修正。
文摘对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重要的思路,但是现有方法依然存在相似参与方识别精度不高等不足。鉴于此,本文提出一种融合联邦学习和长短期记忆网络(long short-term memory,LSTM)的少数据综合能源多元负荷预测方法(multitask learning based on shared dot product confidentiality under federated learning,MT-SDPFL)。首先,给出一种基于共享向量点积保密协议的相似参与方识别方法,用来从诸多可用的综合能源系统中选出最为相似的参与方;接着,使用参数共享联邦学习算法对选中的各参与方联合训练,结合LSTM和finetune技术建立每个参与方的多元负荷预测模型。将所提方法应用于多个实际能源系统,实验结果表明,该方法可以在数据稀疏的情况下取得高精度的多源负荷预测结果。
