Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks

Rao Fu, Guofeng Xia, Sining Hu, Yuhao Zhang, Handaoyuan Li and Jiachuan Shi ()
Additional contact information
Rao Fu: School of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, China
Guofeng Xia: School of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, China
Sining Hu: School of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, China
Yuhao Zhang: School of Electrical Engineering, Shandong University, 17923 Jingshi Rd., Jinan 250063, China
Handaoyuan Li: School of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, China
Jiachuan Shi: School of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, China

Mathematics, 2025, vol. 13, issue 15, 1-22

Abstract: Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility.

Keywords: distribution networks; carbon emission flow; topology reconfiguration; distributed generation; Forward/Backward Sweep; Q-learning enhanced Moth Flame Optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/15/2395/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/15/2395/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:15:p:2395-:d:1710192

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().