
1.华北电力大学 能源动力与机械工程学院,北京 102206
2.青岛科技大学 机电工程学院,山东 青岛 266061
Received:30 September 2025,
Revised:2025-11-03,
Online First:07 January 2026,
Published:25 June 2026
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许娟,李超,翟融融.基于风光气互补的综合能源系统典型日性能分析[J].综合智慧能源,2026,48(06):105-114.
XU Juan,LI Chao,ZHAI Rongrong.Typical daily performance analysis of a wind-photovoltaic-gas complementary integrated energy system[J].BLASTING,2026,48(06):105-114.
许娟,李超,翟融融.基于风光气互补的综合能源系统典型日性能分析[J].综合智慧能源,2026,48(06):105-114. DOI: 10.3969/j.issn.2097-0706.2026.06.009.
XU Juan,LI Chao,ZHAI Rongrong.Typical daily performance analysis of a wind-photovoltaic-gas complementary integrated energy system[J].BLASTING,2026,48(06):105-114. DOI: 10.3969/j.issn.2097-0706.2026.06.009.
针对现有风光气综合能源系统(IES)研究缺乏系统性分时运行策略及统一量化评价框架的问题,通过构建包含风电、光伏、燃料电池的IES模型,提出一种能够适应不同季节负荷特性的优化运行策略,以提升系统的经济性、环保性和能源利用效率。首先,利用Matlab软件搭建了风光气互补的IES数学模型,并建立了涵盖热力性能(能耗)、经济性能(运行费用)及环保性能(CO₂排放)的三维综合性能评价指标。其次,以甘肃某工业园区为研究对象,选取旺季、过渡季及淡季的典型日,对比分析了“电跟随”与“热跟随”两种运行模式下的系统性能。最后,深入探讨了电制冷负荷分配系数(
β
)在不同运行模式下对系统能耗的影响规律。仿真结果表明,系统运行策略具有显著的季节依赖性。在旺季与过渡季,电跟随运行模式在能耗、运行费用及CO₂排放方面均优于热跟随模式。旺季时电跟随模式的能耗峰值(114.7 MW)显著低于热跟随模式(175.0 MW),且CO₂排放最大值减少了约38.6%。在淡季,系统呈现出明显的时段特征:10:00 —18:00,热跟随运行模式在经济性和能耗上表现更优;而其他时段选取电跟随运行模式更具优势。此外,参数分析显示:在电跟随模式下,随着电制冷负荷分配系数
β
的增大,系统能耗呈线性趋势且变化平缓;而在热跟随运行模式下,随着
β
的增大,能耗总体随之逐渐增大。研究结果证实了单一运行模式无法适应IES全年的负荷变化,采用分时段的混合运行策略是实现系统最优运行的关键。具体而言,旺季与过渡季应全程采用电跟随模式,淡季则应采取“分时段切换”策略。该策略不仅能有效降低系统运行成本,还能显著减少碳排放;同时,电跟随模式对电制冷负荷分配系数的变化具有更好的鲁棒性,为同类系统的参数设定与优化调度提供了理论依据。
To address the lack of systematic time-of-use (TOU) operational strategies
and unified quantitative evaluation frameworks in existing research on wind-solar-gas integrated energy systems (IES), this study proposed an optimized operational strategy adaptable to seasonal load characteristics by constructing an IES model incorporating wind power, photovoltaics, and fuel cells. The strategy aimed to enhance the system's economic viability, environmental sustainability, and energy utilization efficiency. Initially, a mathematical IES model was developed using Matlab. A three-dimensional comprehensive performance evaluation system was established, encompassing indicators for thermal performance (energy consumption), economic performance (operational costs), and environmental performance (CO
2
emissions). Subsequently, taking an industrial park in Gansu Province as the case study, typical days representing peak, transition, and off-peak seasons were selected to comparatively analyze the system performance under "electricity-following-heat" (EFH) and "heat-following-electricity" (HFE) operational modes. Furthermore, the impact of the cooling load allocation coefficient (
β
) on system energy consumption under different operational modes was thoroughly investigated. Simulation results indicated that the system's operational strategy exhibited significant seasonal dependence. During the peak and transition seasons, the EFH mode demonstrated superior performance over the HFE mode in terms of energy consumption, operational costs, and CO
2
emissions. During the peak season, the peak energy consumption under the EFH mode (114.7 MW) was substantially lower than that under the HFE mode (175.0 MW), with the maximum CO₂ emissions reduced by approximately 38.6%. During the off-peak season, the system displayed distinct time-varying characteristics: the HFE mode performed better between 10:00 and 18:00 economically and energetically, whereas the EFH mode was more advantageous during the remaining periods. Additionally, parameter analysis revealed that under the EFH mode
, system energy consumption decreased linearly and smoothly as
β
increased. Conversely, under the HFE mode, energy consumption generally rose with a larger
β
. The findings confirm that a single operational mode cannot accommodate the IES load variations over a whole year. Implementing a complementary operational strategy based on time segments is crucial for achieving optimal system performance. Specifically, the EFH mode should be applied continuously during the peak and transition seasons, while a TOU strategy should be adopted during the off-peak season. This strategy not only effectively reduces operational costs but also significantly curtails carbon emissions. Meanwhile, the EFH mode exhibits superior robustness against variations in the electric cooling load allocation coefficient, providing a theoretical basis for parameter setting and optimized dispatch in similar systems.
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