1. 国网河北省电力有限公司,河北省,石家庄市,050000
2. 国网河北省电力有限公司经济技术研究院,河北省,石家庄市,050000
[ "李洪涛(1980),男,硕士,高级工程师,从事配电网规划、微电网运行调度" ]
[ "韩璟琳(1989),男,硕士,工程师,从事配电网规划、微电网运行调度,E-mail:jinglinoliver@163.com" ]
[ "侯若松(1995),男,硕士,工程师,从事配电网规划" ]
[ "胡平(1968),男,硕士,教授级高级工程师,从事配电网规划管理" ]
[ "冯喜春(1970),男,本科,高级工程师,从事配电网规划管理" ]
纸质出版:2025
移动端阅览
李洪涛, 韩璟琳, 侯若松, 等. 考虑阶梯式碳交易和多利益主体的配电网优化调度[J]. 现代电力, 2025,(6):1182-1192.
LI Hongtao, HAN Jinglin, HOU Ruosong, et al. Optimal Dispatching for Distribution Networks Considering Stepped Carbon Emission Trading and Multi Stakeholders[J]. 2025, (6): 1182-1192.
李洪涛, 韩璟琳, 侯若松, 等. 考虑阶梯式碳交易和多利益主体的配电网优化调度[J]. 现代电力, 2025,(6):1182-1192. DOI: 10.19725/j.cnki.1007-2322.2023.0298.
LI Hongtao, HAN Jinglin, HOU Ruosong, et al. Optimal Dispatching for Distribution Networks Considering Stepped Carbon Emission Trading and Multi Stakeholders[J]. 2025, (6): 1182-1192. DOI: 10.19725/j.cnki.1007-2322.2023.0298.
双碳目标背景下,大力发展新能源、降低碳排放将是未来能源发展的主线。提出了一种考虑阶梯式碳交易和多利益主体的配电网优化调度方法。首先,引入碳交易机制,建立碳交易量化模型。然后,建立配电网优化调度模型,分别建立源层、网层与荷层的目标函数和约束条件,在模型中计及阶梯式碳交易成本。进而,构建多利益主体博弈框架,以分布式电源投资运营商、电网企业和用户为参与者建立基于博弈论的配电网多利益主体协调优化调度模型,并通过逆向归纳法进行模型求解。最后,通过仿真算例验证所提方法的有效性。
In the context of the dual carbon goals
vigorously developing new energy and reducing carbon emissions has been the main focus of future energy development. In this paper we propose an optimal dispatching method for distribution networks considering stepped Carbon emission trading and multi stakeholders. The first step involves introducing the carbon emission trading mechanism and developing a quantitative model of carbon emission trading. The distribution network optimal dispatching model is then established. Meanwhile
the objective functions and constraints of the source layer
network layer and load layer are respectively established with the stepped carbon emission trading cost taken into account in the model. Subsequently
a multi stakeholder game framework is constructed
and a game theory based distribution network multi stakeholder coordination and optimization dispatching model is established with distributed generation investment operators
grid enterprises and users as participants. The model is then solved by reverse induction method. Finally
the effectiveness of the proposed method is verified through simulation examples.
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