数据驱动的综合能源系统运行优化研究

doi:10.3969/j.issn.2097-0706.2026.01.004

摘要/Abstract

摘要：

近年来，物联网、大数据和人工智能等数字化技术的快速发展给综合能源系统（IES）运行优化带来了新方法。提出了基于数据驱动的IES运行优化方法，针对北方某自备能源站的产业园区，采用深度学习长短期记忆神经网络模型进行多元负荷联合预测和光伏发电功率预测，为能源站运行优化提供精准依据；通过数据驱动的机器学习算法对主要供能设备进行全工况建模；分别以能效、经济和综合效益指标为优化目标，利用粒子群优化算法求解，得到典型日运行优化结果。能效指标最优情况下，系统综合能源利用率达83.0%，运行成本为64 802元；经济指标最优情况下，系统运行成本低至64 590元，综合能源利用率为79.3%；综合效益最优情况下，与能源站实际运行情况相比，综合能源利用率提升了7.5%，运行成本节约了6 444元。结果表明，本运行优化方法对指导IES运行优化具有实际应用意义。

关键词: 综合能源系统, 多元负荷联合预测, 光伏发电功率预测, 数据驱动建模, 运行优化

Abstract:

In recent years， the rapid development of digital technologies such as the Internet of Things， big data， and artificial intelligence has provided new methods for the operation optimization of integrated energy systems（IES）. A data-driven operation optimization method for IES was proposed. For an industrial park with a self-contained energy station in northern China， a deep learning long short-term memory neural network model was used for multi-load joint forecasting and photovoltaic power forecasting， providing accurate support for the operation optimization of the energy station. The main energy supply equipment was modeled under full operating conditions through data-driven machine learning algorithms. Taking energy efficiency， economic， and comprehensive benefit indicators as optimization objectives， the particle swarm optimization algorithm was applied to obtain typical daily operation optimization results. Under the condition of optimal energy efficiency indicator， the comprehensive energy utilization rate of the system reached 83.0%， and the operating cost was 64 802 yuan. Under the condition of optimal economic indicator， the system operating cost was as low as 64 590 yuan， and the comprehensive energy utilization rate reached 79.3%. Under the condition of optimal comprehensive benefit indicator， compared to the actual operation of the energy station， the comprehensive energy utilization rate increased by 7.5%， and the operating cost was reduced by 6 444 yuan. The results indicate that this operation optimization method has practical significance for guiding the operation optimization of IES.

Key words: integrated energy system, multi-load joint forecasting, photovoltaic power forecasting, data-driven modeling, operation optimization

中图分类号:

TK01：TM732

徐聪, 徐静静, 江婷, 薛东, 闫立辰. 数据驱动的综合能源系统运行优化研究[J]. 综合智慧能源, 2026, 48(1): 34-42.

XU Cong, XU Jingjing, JIANG Ting, XUE Dong, YAN Lichen. Research on data-driven operation optimization of integrated energy systems[J]. Integrated Intelligent Energy, 2026, 48(1): 34-42.

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采集数据	单位
空调水瞬时热量	GJ/h
生活热水瞬时热量	GJ/h
园区总电负荷	kW
园区上/下网电量	kW·h
^#1/^#2内燃机燃气流量	m³/h
^#1/^#2内燃机发电功率	kW
^#1/^#2空调水制热量	kW
^#1/^#2直燃机空调水供/回水温度	℃
^#1/^#2直燃机空调水流量	t/h
^#1/^#2直燃机燃气流量	m³/h
^#2直燃机生活热水供/回水温度	℃
^#2直燃机生活热水流量	t/h
^#1/^#2生活热水板换进/出口温度	℃
^#1/^#2生活热水板换流量	t/h
^#1/^#2烟气热水板换进/出口温度	℃
^#1/^#2烟气热水板换流量	t/h

参数	设定值	参数	设定值
epochs	100	batch_size	8
num_layers	2	gamma	0.5
hidden_size	64	step_size	5
lr	0.1

参数	数值
epochs	100
num_layers	1
hidden_size	64
lr	0.01
batch_size	16

内燃机	MSE/kW²	RMSE/kW	MAE/kW	MAPE/%
^#1	517.287	22.744	18.12	2.961
^#2	420.895	20.516	16.18	2.696

直燃机	MSE/kW²	RMSE/kW	MAE/kW	MAPE/%
^#1	430.474	20.748	11.972	9.806
^#2	1 277.652	35.744	20.846	13.380