基于气象相似日回归模型的微电网群负荷-碳协同分层调度方法

殷鸣; 马琎劼; 张航通; 王子成

殷鸣,马琎劼,张航通,王子成.基于气象相似日回归模型的微电网群负荷-碳协同分层调度方法[J].电测与仪表,2026,63(7):59-68.
YIN Ming,MA Jinjie,ZHANG Hangtong,WANG Zicheng.Load-carbon collaborative hierarchical scheduling method for micro-grid clusters based on meteorological similar daily regression model[J].Electrical Measurement & Instrumentation,2026,63(7):59-68.

基于气象相似日回归模型的微电网群负荷-碳协同分层调度方法

Load-carbon collaborative hierarchical scheduling method for micro-grid clusters based on meteorological similar daily regression model

DOI：10.19753/ j.issn1001-1390.2026.07.006

中文关键词: 灰色关联分析微电网分层调度碳排放量

英文关键词:grey correlation analysis, micro-grid, hierarchical scheduling, carbon emission

基金项目:国网江苏省电力有限公司科技项目资助(J2024164)

作者中文名	作者英文名	单位
殷鸣	YIN Ming	国网江苏省电力有限公司南京供电分公司, 南京 210001
马琎劼	MA Jinjie	国网江苏省电力有限公司南京供电分公司, 南京 210001
张航通	ZHANG Hangtong	国网江苏省电力有限公司南京供电分公司, 南京 210001
王子成	WANG Zicheng	国网江苏省电力有限公司南京市江宁区供电分公司, 南京 211100

摘要点击次数: 176

中文摘要:

针对微电网群整体协同优化调度复杂度高等问题,提出一种基于气象相似日回归模型的微电网群负荷-碳协同分层调度方法。文中建立了基于相似日负荷与气象变量的多元线性回归模型,为协同优化提供基础,构建了包含上层能碳协调层和下层负荷优化层组成的分层调度模型,将复杂全局优化问题分解为上层的碳交易与联络线协调层及下层的经济运行与负荷匹配层,以降低求解的复杂度,上下层通过动态反馈协同机制进行闭环交互,从而实现微电网群负荷-碳协同分层调度。实验结果表明,所述方法相对于模型预测控制调度方法和考虑发电时空相关性的调度方法,能够显著提高微电网群的协同运行效率,降低系统碳排放量并提升整体的经济性,同时,微电网群整体的可靠性在三类典型运行场景下均得到有效地提高。

英文摘要:

Regarding the problem of high complexity of collaborative optimization and scheduling of microgrids, this paper proposes a hierarchical load-carbon scheduling frame for micro-grid clusters based on meteorological similar daily regression model. Firstly, a multiple linear regression model is proposed based on similar days of load pattern and meteorological factors, which provides a foundation for collaborative optimization. Then, a hierarchical scheduling model consisting of an upper-layer with energy-carbon coordination and a lower-layer with load optimization is constructed. This method decomposes the global optimization problem into the upper-layer with carbon trading and tie-line coordination and the lower-layer with economic operation and load matching in order to reduce the complexity. The upper layer and lower layer interact with each other through dynamic feedback coordination for hierarchical load-carbon collaborative scheduling of micro-grid clusters. Experimental results demonstrate that, compared with the model predictive control method and method considering spatiotemporal correlation of solar and wind power, the proposed method significantly improves the collaborative operation efficiency of micro-grid clusters, reduces system carbon emissions, and enhances overall economic performance. Additionally, the reliability of the micro-grid cluster is effectively improved in three typical operational scenarios.

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