Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
虚拟电厂(virtual power plant,VPP)通过先进的控制技术高效聚合容量小、数量多的分布式能源(distributed energy resource,DER)参与电力市场交易。随着DER数量的增加,其出力的波动性以及聚合后的收益问题需要解决。基于此,提出一种在...虚拟电厂(virtual power plant,VPP)通过先进的控制技术高效聚合容量小、数量多的分布式能源(distributed energy resource,DER)参与电力市场交易。随着DER数量的增加,其出力的波动性以及聚合后的收益问题需要解决。基于此,提出一种在日前电力市场下,多类型DER聚合于VPP的协同博弈调度模型。首先,提出多类型DER聚合于VPP的运营框架。其次,由于风光出力的不确定性严重影响系统的运行,建立基于变分模态分解(variational modal decomposition,VMD)和改进的双向多门控长短期记忆(bidirectional multi gated long short-term memory,Bi-MGLSTM)网络的组合预测模型。然后,同类型DER形成联盟,并以售电收益最大化为目标,构建VPP多联盟的合作博弈调度模型,为实现联盟及成员间收益分配的公平性,设计多因素改进shapley值法和基于奇偶循环核仁法的两阶段细化收益分配方案。最后,算例结果表明,所提方法能有效提高风光功率的预测精度,实现VPP内联盟间合作互补运行,保证了多个主体间收益分配的公平性与合理性。展开更多
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘虚拟电厂(virtual power plant,VPP)通过先进的控制技术高效聚合容量小、数量多的分布式能源(distributed energy resource,DER)参与电力市场交易。随着DER数量的增加,其出力的波动性以及聚合后的收益问题需要解决。基于此,提出一种在日前电力市场下,多类型DER聚合于VPP的协同博弈调度模型。首先,提出多类型DER聚合于VPP的运营框架。其次,由于风光出力的不确定性严重影响系统的运行,建立基于变分模态分解(variational modal decomposition,VMD)和改进的双向多门控长短期记忆(bidirectional multi gated long short-term memory,Bi-MGLSTM)网络的组合预测模型。然后,同类型DER形成联盟,并以售电收益最大化为目标,构建VPP多联盟的合作博弈调度模型,为实现联盟及成员间收益分配的公平性,设计多因素改进shapley值法和基于奇偶循环核仁法的两阶段细化收益分配方案。最后,算例结果表明,所提方法能有效提高风光功率的预测精度,实现VPP内联盟间合作互补运行,保证了多个主体间收益分配的公平性与合理性。
