基于数据驱动的电−热−气综合能源系统概率多能流计算分析

周永旺; 蔡政彤; 许灿城; 倪强

doi:10.12052/gdutxb.240084

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基于数据驱动的电−热−气综合能源系统概率多能流计算分析

电气工程 | 更新时间：2026-05-27

- 基于数据驱动的电−热−气综合能源系统概率多能流计算分析
- Probabilistic Multi-energy Flow Calculation and Analysis for Electricity-heating-gas Integrated Energy System Based on Data-driven
- 广东工业大学学报 2024年41卷第5期页码：1-12
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  周永旺(1966–)，男，副教授，硕士，主要研究方向为电力系统分析计算与稳定控制、综合能源优化等，E-mail：zhouyongwang@sohu.com
  倪强(1987–)，男，讲师，博士，主要研究方向为综合能源系统优化、电力电子系统故障诊断与健康管理，E-mail：nq666@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62103109);广东省自然科学基金资助项目(2024A1515011966)
- DOI：10.12052/gdutxb.240084
  中图分类号： TM744
- 纸质出版：2024
- 稿件说明：
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周永旺, 蔡政彤, 许灿城, 倪强. 基于数据驱动的电−热−气综合能源系统概率多能流计算分析[J]. 广东工业大学学报, 2024, 41(5): 1-12.

Zhou Yong-wang, Cai Zheng-tong, Xu Can-cheng, et al. Probabilistic Multi-energy Flow Calculation and Analysis for Electricity-heating-gas Integrated Energy System Based on Data-driven[J]. 2024, 41(5): 1-12.
周永旺, 蔡政彤, 许灿城, 倪强. 基于数据驱动的电−热−气综合能源系统概率多能流计算分析[J]. 广东工业大学学报, 2024, 41(5): 1-12. DOI： 10.12052/gdutxb.240084.

Zhou Yong-wang, Cai Zheng-tong, Xu Can-cheng, et al. Probabilistic Multi-energy Flow Calculation and Analysis for Electricity-heating-gas Integrated Energy System Based on Data-driven[J]. 2024, 41(5): 1-12. DOI： 10.12052/gdutxb.240084.

摘要

针对可再生能源与系统负荷波动对综合能源系统多能流分布的不确定性量化问题，提出一种基于数据驱动的综合能源系统概率多能流计算方法。首先，提出了考虑压缩机不同工作模式的综合能源系统多能流计算统一模型，并探讨了压缩机不同工作模式对能流分布的影响；其次，提出基于支持向量回归的概率能流计算方法，先通过多次重复的确定性多能流计算，构建数据样本集，再用支持向量回归挖掘出综合能源系统中已知负荷、网络节点信息与未知节点参数的非线性映射关系；最后，通过算例分析对提出的多能流计算统一模型在不同压缩机工作模式下的有效性进行了验证；通过与传统概率多能流计算方法对比研究，证明提出的数据驱动概率能流计算方法具有更高的计算精度与效率。

Abstract

In order to quantify the uncertainty of multi-energy flow distribution in the integrated energy system

a probabilistic multi-energy flow calculation method of integrated energy system based on data-driven is proposed. Firstly

a unified multi-energy flow calculation model suitable for different working modes of compressors in integrated energy system is established

and the impact of different operating modes of compressors on the multi-energy flow distribution is also discussed. Secondly

a probabilistic multi-energy flow calculation method based on support vector regression is developed. The method first constructs a data set by calculating deterministic multi-energy flow repeatedly

and then the support vector regression is used to mine the nonlinear mapping relationship between known loads

network node information and unknown node parameters in the integrated energy system. Finally

through case analysis

it is verified that the proposed unified multi-energy flow model can be applied to different compressor working conditions. By comparing with traditional probabilistic multi-energy flow calculation methods

it is shown that the proposed data-driven probabilistic multi-energy flow calculation method has higher computational accuracy and efficiency.

关键词

Keywords

references

SUN H B, PAN Z G, GUO Q L. Energy management for multi-energy flow: challenges and prospects[J]. Automation of Electric Power Systems, 2016, 40(15): 1-8.

WANG S P, ZHANG S X, CHENG H Z, et al . Improved multi-energy flow calculation method for integrated energy system considering initial value optimization of natural gas system[J ] . Electric Power Automation Equipment, 2022, 42(1): 28-36.

SHABANPOUR-HAGHIGHI A, SEIFI A R. An integrated steady-state operation assessment of electrical, natural gas, and district heating networks[J]. IEEE Transactions on Power Systems, 2016, 31(5): 3636-3647.

MASSRUR H R, NIKNAM T, AGHAEI J, et al . Fast decomposed energy flow in large-scale integrated electricity-gas-heat energy systems[J ] . IEEE Transactions on Sustainable Energy, 2018, 9(4): 1565-1577.

CHEN Y, ZHAO J, MA J. Fast decoupled multi-energy flow calculation for integrated energy system[J]. Journal of Modern Power Systems and Clean Energy, 2020, 8(5): 951-960.

WANG Y R, ZENG B, GUO J, et al . Multi-energy flow calculation method for integrated energy system containing electricity, heat and gas[J ] . Power System Technology, 2016, 40(10): 2942-2950.

LI J H, HUANG Y J, ZHU M S. Gradient descent iterative method for energy flow of integrated energy system considering multiple modes of compressors[J]. Energy Conversion and Management, 2020, 207: 112534.

ZHONG J J, LI Y, ZENG Z L, et al . Quasi-steady-state analysis and calculation of multi-energy flow for integrated energy system[J ] . Electric Power Automation Equipment, 2019, 39(8): 22-30.

MIN Q J, MA Z X, MA Y Z. Energy flow calculation of integrated power-gas energy system based on PSO-newton method[J]. Electric Power Engineering Technology, 2021, 40(1): 33-39.

SHABANPOUR-HAGHIGHI A, SEIFI A R. Simultaneous integrated optimal energy flow of electricity, gas, and heat[J]. Energy Conversion and Management, 2015, 101: 579-591.

CHEN S, WEI Z N, SUN G Q, et al . Probabilistic energy flow analysis in integrated electricity and natural-gas energy systems[J ] . Proceedings of the CSEE, 2015, 35(24): 6331-6340.

CHEN S, WEI Z N, SUN G Q, et al . Multi-linear probabilistic energy flow analysis of integrated electrical and natural-gas systems[J ] . IEEE Transactions on Power Systems, 2017, 32(3): 1970-1979.

ZHANG R F, JIANG T, LI G Q, et al . Maximum entropy based probabilistic energy flow calculation for integrated electricity and natural gas systems[J ] . Proceedings of the CSEE, 2019, 39(15): 4430-4441.

YANG J H. Probabilistic multi-energy flow calculation for regional integrated energy system containing cooling, heating, electricity and gas[J]. Power System Technology, 2019, 43(1): 74-83.

YUAN J, WENG Y. Support matrix regression for learning power flow in distribution grid with unobservability[J]. IEEE Transactions on Power Systems, 2021, 37(2): 1151-1161.

XU Y, HU Z, MILI L, et al . Probabilistic power flow based on a gaussian process emulator[J ] . IEEE Transactions on Power Systems, 2020, 35(4): 3278-3281.

LIU X. Combined analysis of electricity and heat networks[D]. Cardiff: Cardiff University, 2013.

LI Q, AN S, GEDRA T W. Solving natural gas loadflow problems using electric load flow techniques [C]//Proceedings of the North American Power Symposium. Rolla: NAPS, 2003: 1-7.

WEI Z N, ZHONG L L, XUE M F, et al . Linearization flow calculation for integrated electricity-heat energy system based on data-driven[J ] . Electric Power Automation Equipment, 2019, 39(8): 31-37.

TIAN H, ZHAO H, LIU C, et al . A dual-driven linear modeling approach for multiple energy flow calculation in electricity-heat system[J ] . Applied Energy, 2022, 314: 118872.

CHEN Q, ZHANG S X, CHENG H Z, et al . Probabilistic energy flow calculation for integrated energy systems based on radial basis function-stochastic response surface method[J ] . Proceedings of the CSEE, 2022, 42(22): 8075-8089.

江茂泽, 徐羽镗, 王寿喜, 等. 输配气管网的模拟与分析[M]. 北京: 石油工业出版社, 1995: 205-219.

ZENG Q, FANG J K, LI J H, et al . Steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion[J ] . Applied Energy, 2016, 184: 1483-1492.

LI Y Z, WU Q H. Downside risk constrained probabilistic optimal power flow with wind power integrated[J]. IEEE Transactions on Power Systems, 2015, 31(2): 1649-1650.

WEI B H, BAO G, LI Z H. Short-term electricity load forecasting based on support vector regression forecasting model considering weather factors and time-of-use tariff factors[J]. Power System and Clean Energy, 2023, 39(11): 9-19.

朱宇迪. 基于数据驱动的潮流非线性回归建模及其灵敏度解析[D]. 吉林: 东北电力大学, 2024.

LI Y, LI W, YAN W, et al . Probabilistic optimal power flow considering correlations of wind speeds following different distributions[J ] . IEEE Transactions on Power Systems, 2014, 29(4): 1847-1854.

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