
1.智能配用电装备与系统全国重点实验室(天津大学),天津市 300072
2.天津市智慧能源与信息技术重点实验室(天津大学),天津市 300072
3.中国电力科学研究院有限公司,北京市 100192
Received:17 November 2025,
Revised:2026-02-13,
Online First:21 May 2026,
Published:25 October 2026
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吴志军,穆云飞,郭浩辰等.考虑内部动态碳价的园区综合能源系统碳能协同运行优化[J].电力系统自动化,2026,50(20):30-44.
WU Zhijun,MU Yunfei,GUO Haochen,et al.Carbon-Energy Coordinated Operation Optimization of Park-level Integrated Energy Systems Considering Internal Dynamic Carbon Prices[J].Automation of Electric Power Systems,2026,50(20):30-44.
吴志军,穆云飞,郭浩辰等.考虑内部动态碳价的园区综合能源系统碳能协同运行优化[J].电力系统自动化,2026,50(20):30-44. DOI: 10.7500/AEPS20251117008.
WU Zhijun,MU Yunfei,GUO Haochen,et al.Carbon-Energy Coordinated Operation Optimization of Park-level Integrated Energy Systems Considering Internal Dynamic Carbon Prices[J].Automation of Electric Power Systems,2026,50(20):30-44. DOI: 10.7500/AEPS20251117008.
园区综合能源系统(PIES)内部多能负荷蕴含着丰富的碳减排潜力。目前,PIES低碳调控主要依赖日级固定碳价,但内部用户的用能行为与碳排放量间存在紧密互动,仅依赖固定碳价难以捕捉不同时段用能的碳属性差异,无法有效引导用户低碳用能。为此,提出考虑动态内部碳价(DICP)的PIES碳能协同运行方法:首先,构建PIES碳能协同枢纽(CESH)模型,刻画PIES内部的碳能交互机理;其次,利用CESH模型计算多能负荷动态碳排放因子,量化用户在不同时段用能产生的碳排放量,并将其映射为随其正相关变化的内部价格信号,从而建立PIES的DICP模型;进而,以包括DICP计算的内部碳成本的年度总成本最小化为目标,考虑碳能平衡、设备运行边界及履约周期碳交易约束,建立PIES低碳经济运行双层优化模型。算例验证结果表明,所提方法可引导用户在DICP高位时减少用能、低位时增加用能,充分挖掘PIES内多能负荷碳减排潜力。相较于仅考虑外部固定碳价的PIES运行优化方法,所提方法能够进一步降低3.23%的运行成本和7.22%的碳排放量。
Park-level integrated energy systems (PIESs) contain substantial carbon emission reduction potential in their internal multi-energy loads. At present
the low-carbon regulation of PIESs mainly relies on daily fixed carbon prices. However
the energy consumption behaviors of internal users are closely interacted with carbon emissions
and a fixed carbon price alone cannot capture the temporal differences in carbon attributes of energy consumption or effectively guide users toward low-carbon energy consumption. To address this issue
this paper proposes a carbon-energy coordinated operation method for PIESs considering dynamic internal carbon pricing (DICP). First
a carbon-energy synergistic hub (CESH) model is established to characterize the internal carbon-energy interaction mechanism of PIESs. Then
the CESH model is used to calculate dynamic carbon emission factors of multi-energy loads
quantify the carbon emissions caused by users’ energy consumption during various periods
and map them into internal price signals that vary positively with the emission factors
thereby establishing the DICP model for PIESs. Furthermore
with the objective of minimizing the annual total cost
including the internal carbon cost calculated by DICP
a bi-level low-carbon economic operation optimization model for PIESs is developed
considering carbon-energy balance
equipment operating limits
and carbon trading constraints over the compliance cycle. Case studies show that the proposed method can guide users to reduce energy consumption when DICP is high and increase energy consumption when DICP is low
thereby fully exploiting the carbon emission reduction potential of multi-energy loads in PIESs. Compared with the PIES operation optimization method considering only an external fixed carbon price
the proposed method further reduces operating cost and carbon emissions by 3.23% and 7.22%
respectively.
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