1.南京工程学院电力工程学院,江苏省南京市 211167
2.国网江苏省电力有限公司电力科学研究院,江苏省南京市 211103
3.中国电力企业联合会标准化中心,北京市 100761
王梓承(2002—),男,硕士研究生,主要研究方向:车网互动与低碳优化调度技术。E-mail:wzc_ee@163.com
王明深(1990—),男,通信作者,博士,高级工程师,产业教授,硕士生导师,主要研究方向:规模化车网互动优化控制与智慧运营决策技术。E-mail:wmshtju@163.com
袁晓冬(1979—),男,研究员级高级工程师,主要研究方向:电动汽车充换电高效运营管控技术及装备。E-mail:xiaodongyuan_dky@163.com
收稿:2025-12-31,
修回:2026-06-02,
网络首发:2026-06-18,
纸质出版:2026-08-10
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王梓承,王明深,袁晓冬等.考虑电动汽车生命周期碳配额的车网时空协同低碳优化调度[J].电力系统自动化,2026,50(15):183-198.
WANG Zicheng,WANG Mingshen,YUAN Xiaodong,et al.Optimal Low-carbon Scheduling with Vehicle-Grid Spatio-temporal Coordination Considering Carbon Quotas for Life Cycle of Electric Vehicles[J].Automation of Electric Power Systems,2026,50(15):183-198.
王梓承,王明深,袁晓冬等.考虑电动汽车生命周期碳配额的车网时空协同低碳优化调度[J].电力系统自动化,2026,50(15):183-198. DOI: 10.7500/AEPS20251231002.
WANG Zicheng,WANG Mingshen,YUAN Xiaodong,et al.Optimal Low-carbon Scheduling with Vehicle-Grid Spatio-temporal Coordination Considering Carbon Quotas for Life Cycle of Electric Vehicles[J].Automation of Electric Power Systems,2026,50(15):183-198. DOI: 10.7500/AEPS20251231002.
电动汽车低碳调度对电网碳减排与经济运行具有重要意义。现有电动汽车低碳调度中存在碳排放核算缺乏全生命周期的考量、低碳引导信号与电网碳减排目标协同性不足等问题,难以充分释放电动汽车参与电网低碳调度的潜力。针对上述问题,首先,考虑电池充放电功率爬坡特性与差异化需求构建3类充电行为控制模型;其次,结合碳排放流理论与生命周期评估方法,实现生命周期视角下电动汽车充电行为碳排放时空差异的量化,建立了电动汽车生命周期碳配额分配机制与碳导向分时电价协同低碳引导体系;然后,构建车网低碳互动双层优化模型,实现电网低碳经济调度与电动汽车聚合商低碳需求响应的深度耦合;最后,算例分析表明,所提调度策略可充分挖掘电动汽车降碳潜力,有效降低系统碳排放量与电动汽车用户成本。
Low-carbon scheduling of electric vehicles (EVs) is of great significance for carbon emission reduction and economic operation of power grids. Existing low-carbon scheduling for EVs suffers from problems including insufficient full-life-cycle consideration for carbon emission accounting and poor coordination between low-carbon guidance signals and grid carbon reduction targets
so the potential of EVs participating in power grid low-carbon scheduling cannot be fully exploited. To address the above issues
firstly
three types of charging behavior control models are established by considering the charging-discharging power ramp characteristics and differentiated demand of batteries. Secondly
combined with the carbon emission flow theory and the life cycle assessment method
the spatio-temporal differences in carbon emissions caused by EV charging behaviors are quantified from the life-cycle perspective
and a life-cycle carbon quota allocation mechanism as well as a synergistic low-carbon guidance system oriented by carbon-based time-of-use electricity prices are formulated. Then
a bi-level optimization model for low-carbon vehicle-to-grid (V2G) is constructed to realize the in-depth coupling of grid low-carbon economic dispatch and low-carbon demand response of EV aggregators. Finally
numerical case results verify that the proposed scheduling strategy can fully tap the carbon reduction potential of EVs
and effectively reduce the system carbon emissions and the costs of EV users.
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