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基于VMD-ORELM-EC的超短期风速组合预测模型

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合肥工业大学学报（自然科学版） 2024年第5期47卷 703-711页

作者：谢东良郅伦海周康胡峰合肥工业大学土木与水利工程学院安徽合肥230009

为提高超短期风速预测的精度,文章提出一种基于变分模态分解(variational mode decomposition,VMD)、离群鲁棒极限学习机(outlier-robust extreme learning machine,ORELM)和误差修正(error correction,EC)的超短期风速组合预测模型VMD-ORELM-EC。首先利用VMD将原始风速序列分解,并对每个分解子序列分别建立ORELM模型,将各子模型预测结果相加得到模型初步预测序列;然后将原始风速序列与初步预测序列相减得到模型的误差序列,并对误差序列进行VMD分解,对分解得到的误差子序列建立ORELM模型,从而得到误差预测序列;最后将模型的初步预测序列与误差预测序列组合得到最终的风速预测序列。利用该文提出的预测模型对北京测风塔实测的风速数据进行分析,结果表明模型可以有效挖掘风速序列特性,在超短期风速预测上具有较高的预测性能。

关键词：超短期风速预测变分模态分解(VMD) 离群鲁棒极限学习机(ORELM) 误差修正(EC)

博弈视角下电-气互联综合能源系统多目标协同优化

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电力系统自动化 2024年第11期48卷 173-183页

作者：殷晨旭孙永辉谢东亮张兆卿周伟孟雲帆河海大学能源与电气学院江苏省南京市210098 南瑞集团有限公司(国网电力科学研究院有限公司) 江苏省南京市211106

考虑到现有综合能源系统多目标优化存在主观性,文中提出一种基于非合作博弈理论的单主体多目标协同优化方法。首先,建立耦合配电网络、配气网络和能源站的电-气互联综合能源系统模型。为使优化调度结果更贴近现实,在配气网模型中采用可... 详细信息

考虑到现有综合能源系统多目标优化存在主观性,文中提出一种基于非合作博弈理论的单主体多目标协同优化方法。首先,建立耦合配电网络、配气网络和能源站的电-气互联综合能源系统模型。为使优化调度结果更贴近现实,在配气网模型中采用可变压缩比的压缩机模型,并考虑其非线性耗量特性。其次,基于非合作博弈理论,将能源站单一利益主体的经济性目标和环保性目标视为完全平等且理性的虚拟博弈者,其策略空间由系统内各设备约束组成。再次,为确保博弈模型存在纳什均衡解,在新增辅助变量和约束作用下将收益函数(目标函数)转为拟凸的可微函数。采用凸松弛方法处理模型中非凸非线性约束,并提出非线性迭代策略加速松弛收紧。为便于求解出纳什均衡,使用Nikaido-Isoda函数重构收益函数,将原模型转变为全局最优问题。最后,通过算例验证了所提方法的有效性。

关键词：综合能源系统协同优化非合作博弈非线性耗量凸松弛

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基于混合特征双重衍生和误差修正的风电功率超短期预测

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电力系统自动化 2024年第5期48卷 68-76页

作者：袁畅王森孙永辉武云逸谢东亮河海大学能源与电气学院江苏省南京市210098 南瑞集团有限公司(国网电力科学研究院有限公司) 江苏省南京市211106

随着风电渗透率的不断提高,对风电功率进行精准、可靠的预测是提升风电消纳水平的有效措施。针对功率预测时风电数据种类不足和特征数量稀缺的问题,提出基于混合特征双重衍生和误差修正的风电功率超短期预测模型。首先,在原始功率特征... 详细信息

随着风电渗透率的不断提高,对风电功率进行精准、可靠的预测是提升风电消纳水平的有效措施。针对功率预测时风电数据种类不足和特征数量稀缺的问题,提出基于混合特征双重衍生和误差修正的风电功率超短期预测模型。首先,在原始功率特征中施加混沌噪声,构造出多条混沌扰动特征,改善原始功率特征分布过于单一的状况。其次,提出基于免疫算法的特征衍生算法,挖掘风电功率数据的潜在信息,增加优质特征数量,进而构建误差预测模型,通过预测风电功率预测误差修正风电功率预测结果,进一步提升预测准确率。最后,基于比利时风电场实际运行数据进行算例分析。所提模型预测效果较好,且相较其他传统预测模型精确度更高,验证了所提模型的有效性。

关键词：风电功率预测风电场特征稀缺回归预测特征衍生误差修正超短期预测

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计及改进阶梯型碳交易和热电联产机组灵活输出的园区综合能源系统低碳调度

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电网技术 2024年第1期48卷 61-69页

作者：周伟孙永辉谢东亮殷晨旭孟雲帆河海大学能源与电气学院江苏省南京市210098 南瑞集团有限公司(国网电力科学研究院有限公司) 江苏省南京市211106

为了进一步降低园区综合能源系统(park-level integrated energy system,PIES)碳排放量,优化热电联产(combined heat and power,CHP)机组出力的灵活性,提出一种考虑改进阶梯型碳交易和CHP热电灵活输出的PIES低碳经济调度策略。首先,将... 详细信息

为了进一步降低园区综合能源系统(park-level integrated energy system,PIES)碳排放量,优化热电联产(combined heat and power,CHP)机组出力的灵活性,提出一种考虑改进阶梯型碳交易和CHP热电灵活输出的PIES低碳经济调度策略。首先,将遗传算法与模糊控制相结合,设计一种遗传模糊碳交易参数优化器,从而对现有阶梯型碳交易机制进行改进,实现该机制参数的自适应变化;其次,在传统CHP中加入卡琳娜(Kalina)循环与电锅炉(electricboiler,EB),构造CHP热电灵活输出模型,以同时满足电、热负荷的不同需求;然后,提出一种柔性指标——电、热输出占比率,进而计算出电、热输出占比率区间,以衡量CHP运行灵活性;最后,将改进阶梯型碳交易机制和CHP热电灵活输出模型协同优化,以系统运行成本和碳交易成本之和最小为目标,构建PIES低碳经济优化模型。算例分析表明,所提策略可有效降低经济成本和碳排放量,同时还可扩展CHP灵活输出调节范围,能够为PIES低碳经济调度提供参考。

关键词：园区综合能源系统遗传模糊碳交易参数优化器热电联产机组灵活输出电、热输出占比率低碳经济调度

Multiple Mobile Sinks Data Dissemination Mechanism for Large Scale Wireless Sensor Network

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China Communications 2014年第A1期11卷 1-8页

作者： xie dongliang WU Xiaojie LI Dan SUN Jia StateKeyLaboratoryofNetworkingandSwitchingTechnology BeijingUniversityofPostsandTelecommunicationsBeijing100876China.

WSN has been developing from traditional environment monitoring applications to the ubiquitous information services such as the Congestion-oriented Intelligent Transportation System (CoITS). However, the mobility of nodes makes data dissemination a hard nut to crack. In this paper, we propose MSDD, a multiple mobile sinks data dissemination mechanism for solving the dissemination problem. The main ideas of MSDD are constructing a two-tier grid structure by a designated sink, exploiting a hierarchical monitoring mechanism, and employing a global agent to track the sink locations in order to make the emergencies reported to the sinks immediately, In addition, MSDD supports the query-driven data dissemination. Being compared with TTDD, MSDD is theoretically proved to have less communication overhead. We also validate MSDD outperforms TTDD in reliability and the emergency delivery latency by simulations.

关键词：传播机制移动接收无线传感器网络智能交通系统信息服务环境监测数据发布网架结构

Scenario-based Unit Commitment Optimization for Power System with Large-scale Wind Power Participating in Primary Frequency Regulation

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1259-1267页

作者： Lili Hao Jing Ji dongliang xie Haohao Wang Wei Li Philip Asaah the School of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816China State Grid Electric Power Research Institute Nanjing 211106China

Continuous increase of wind power penetration brings high randomness to power system,and also leads to serious shortage of primary frequency regulation(PFR)reserve for power system whose reserve capacity is typically provided by conventional *** large-scale wind power participating in PFR,this paper proposes a unit commitment optimization model with respect to coordination of steady state and transient *** addition to traditional operation costs,losses of wind farm de-loaded operation,environmental benefits and transient frequency safety costs in high-risk stochastic scenarios are also considered in the ***,the model makes full use of interruptible loads on demand side as one of the PFR reserve sources.A selection method for high-risk scenarios is also proposed to improve the calculation ***,this paper proposes an inner-outer iterative optimization method for the model *** method is validated by the New England 10-machine system,and the results show that the optimization model can guarantee both the safety of transient frequency and the economy of system operation.

关键词： Unit commitment optimization primary frequency regulation(PFR) wind power transient frequency safety high-risk stochastic scenario inner-outer iterative optimization

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轻量化汉语唇读模型及数据集构建

北京邮电大学学报 2023年第4期46卷 58-63页

作者：孙保胜谢东亮北京邮电大学计算机学院北京100876

为了促进汉语唇读的快速发展和实际应用,提出了一种基于交错组卷积和空洞卷积组合的轻量化唇读模型。所提模型通过分组卷积学习不同特征间的相关性,通过空洞卷积扩展模型视野,在大幅度降低模型参数量和复杂度的同时提高模型识别精度。... 详细信息

为了促进汉语唇读的快速发展和实际应用,提出了一种基于交错组卷积和空洞卷积组合的轻量化唇读模型。所提模型通过分组卷积学习不同特征间的相关性,通过空洞卷积扩展模型视野,在大幅度降低模型参数量和复杂度的同时提高模型识别精度。针对汉语唇读数据集较少的问题,在可控制环境下录制了一个句子级汉语唇读数据集。在录制数据集和公开数据集上对轻量化唇读模型适用性进行实验验证,证明了模型的有效性。并通过热图可视化的方法分析了模型对视频帧和文本映射关系的学习能力。

关键词：汉语唇读轻量化交错组卷积空洞卷积

LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1205-1216页

作者： Huayanran Zhou Yihong Zhou Junjie Hu Guangya Yang dongliang xie Yusheng Xue Lars Nordström State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Center for Electric Power and Energy Technical University of DenmarkLyngbyDenmark State Grid Electric Power Research Institute NanjingChina Division of Electric Power and Energy Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden

As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management ***,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online *** address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building *** the proposed system control structure,the LSTM algorithm can be separated into offline and online *** the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network *** the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM *** simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

关键词： Building energy management system(BEMS) electric vehicle(EV) long short-term memory(LSTM) recurrent neural network machine learning prosumer

Multi-agent modeling and analysis of EV users' travel willingness based on an integrated causal/statistical/behavioral model

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Journal of Modern Power Systems and Clean Energy 2018年第6期6卷 1255-1263页

作者： Juai WU Yusheng XUE dongliang xie Kang LI Fushuan WEN Junhua ZHAO Guangya YANG Qiuwei WU Nanjing University of Science & Technology State Grid Electric Power Research Institute Queen’s University Belfast Northern Ireland UK College of Electrical Engineering Zhejiang University Chinese University of Hong Kong Department of Electrical Engineering Center for Electric Power and Energy Technical University of Denmark

An electric vehicle(EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users' travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics(EE)-based simulation method can be used to analyze thebehaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EEbased simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users' travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users' travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine(ICE) vehicle users.

关键词： Travel willingness Questionnaire design Multi-agent Experimental economics Causal analysis