融合FDA-LSTM风电预测的煤电机组跟踪新能源联合供热

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融合FDA-LSTM风电预测的煤电机组跟踪新能源联合供热

更新时间：2025-10-29

- 融合FDA-LSTM风电预测的煤电机组跟踪新能源联合供热
- Issue 4, Pages: 99-107(2025)
- 作者机构：
  
  1. 国核电力规划设计研究院有限公司
  2. 华北电力大学控制与计算机工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM61;TP183
- Published：2025
- 稿件说明：
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[J]. 2025, (4): 99-107.
DOI：

[J]. 2025, (4): 99-107. DOI：

摘要

为实现热电联产机组供暖季的低碳经济运行，构建了融合风电预测的煤电机组跟踪新能源联合供热优化调度模型。首先提出一种融合函数型数据分析与长短期记忆网络的预测模型，对风电机组出力进行预测；其次建立热电联产机组跟踪新能源联合供热系统模型，探究供暖季风能全部消纳前提下的风机最佳容量配置；最后以联合供热系统上网稳定性和供能侧经济收益为性能指标，以机组爬坡率、风机出力不确定性、整个供暖季供热平衡等为约束条件，建立热电联产机组跟踪新能源联合供热系统优化调度模型，仿真结果表明了联合供热系统中新能源最优容量配置的经济性和优化调度模型的有效性。

Abstract

In order to realize low-carbon economic operation of combine heat and power(CHP) units during the heat-supply season

we propose an optimal scheduling model of coal-fired unit tracking renewable energy for combined heating based on wind power prediction. Firstly

we propose a prediction model integrating functional data analysis and long short-term memory network to predict the output of wind turbine units. Secondly

we establish a system model of CHP units tracking renewable energy for combined heating

which explores the optimal capacity allocation of wind turbine units under the premise of full wind power absorption during the heating season. Finally

considering the power grid stability and the economic benefits on the energy supply side of the combined heat-supply system as performance indicators

and taking into account constraints such as unit ramp rate

uncertainty in wind power output

and heating balance throughout the heat-supply season

we establish an optimal scheduling model of CHP units tracking renewable energy for combined heat-supply. The simulation results show that the economy of the new energy optimal capacity allocation in the combined heat-supply system and the effectiveness of the optimal scheduling model.

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references

赵宇洋,赵浩然,谭建鑫,张礽恺,井延伟,孙鹤旭.面向风电制氢的超短期组合功率预测[J].太阳能学报,2023(03).

王维高,魏云冰,滕旭东.基于VMD-SSA-LSSVM的短期风电预测[J].太阳能学报,2023(03).

王进峰,吴盛威,花广如,吴自高.基于Bi-LSTM和改进残差学习的风电功率超短期预测方法[J].华北电力大学学报(自然科学版),2025(01).

徐泽鹏,陈洁,张敬轩.考虑碳交易的CSP电站-电极锅炉-热电联产联合供热系统优化策略[J].现代电子技术,2023(03).

何烨,李超,高佳圣,周艳.集中供热网蓄热式热电联产机组的综合热力学分析[J].动力工程学报,2022(12).

庞新富,张旭,刘莉,尤石,崔馨元,裴根.含储热装置的热电联产系统改进人工蜂群优化调度方法[J].科学技术与工程,2022(27).

林铮,刘可真,沈赋,赵现平,梁玉平,董敏.考虑海上风电多机组时空特性的超短期功率预测模型[J].电力系统自动化,2022(23).

徐斌,崔海林.热电联产机组联合电极锅炉深度调峰性能试验研究[J].宁夏电力,2022(04).

王子杰,顾煜炯,刘浩晨,李长耘.热电联产机组热电解耦技术对比分析[J].化工进展,2022(07).

曹丽华,潘同洋,司和勇,姜铁骝,曹兴,赵金峰.热电厂配置调峰电锅炉最佳容量确定方法[J].中国电力,2020(06).

薛阳,王琳,王舒,张亚飞,张宁.一种结合CNN和GRU网络的超短期风电预测模型[J].可再生能源,2019(03).

张磊,罗毅,罗恒恒,苗世洪,叶婧,周桂平,孙亮.基于集中供热系统储热特性的热电联产机组多时间尺度灵活性协调调度[J].中国电机工程学报,2018(04).

裴哲义,王新雷,董存,邢媛,梁志峰.东北供热机组对新能源消纳的影响分析及热电解耦措施[J].电网技术,2017(06).

吕泉,陈天佑,王海霞,于汀,李群,汤伟.配置储热后热电机组调峰能力分析[J].电力系统自动化,2014(11).

Wang Hantao,Gu Chenghong,Zhang Xin,Li Furong.Optimal CHP Planning in Integrated Energy Systems Considering Network Charges[J].IEEE Systems Journal,2019.

Shahidehpour Mohammad,Zhou Yizhou,Wei Zhinong,Chen Sheng,Li Zhiyi,Sun Guoqiang.Distributionally Robust Co-optimization of Energy and Reserve for Combined Distribution Networks of Power and District Heating[J].IEEE Transactions on Power Systems,2019.

Dai Yuanhang,Chen Lei,Min Yong,Mancarella Pierluigi,Chen Qun,Hao Junhong,Hu Kang,Xu Fei.A General Model for Thermal Energy Storage in Combined Heat and Power Dispatch Considering Heat Transfer Constraints[J].IEEE Transactions on Sustainable Energy,2018.

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