Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/15/2395 |
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| author | Rao Fu Guofeng Xia Sining Hu Yuhao Zhang Handaoyuan Li Jiachuan Shi |
| author_facet | Rao Fu Guofeng Xia Sining Hu Yuhao Zhang Handaoyuan Li Jiachuan Shi |
| author_sort | Rao Fu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3379e432092f49ed8caecb7bbca04906 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-3379e432092f49ed8caecb7bbca049062025-08-20T03:36:34ZengMDPI AGMathematics2227-73902025-07-011315239510.3390/math13152395Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution NetworksRao Fu0Guofeng Xia1Sining Hu2Yuhao Zhang3Handaoyuan Li4Jiachuan Shi5School of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, ChinaSchool of Electrical Engineering, Shandong University, 17923 Jingshi Rd., Jinan 250063, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, 1000 Fengming Rd., Jinan 250101, ChinaAddressing 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.https://www.mdpi.com/2227-7390/13/15/2395distribution networkscarbon emission flowtopology reconfigurationdistributed generationForward/Backward SweepQ-learning enhanced Moth Flame Optimization |
| spellingShingle | Rao Fu Guofeng Xia Sining Hu Yuhao Zhang Handaoyuan Li Jiachuan Shi Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks Mathematics distribution networks carbon emission flow topology reconfiguration distributed generation Forward/Backward Sweep Q-learning enhanced Moth Flame Optimization |
| title | Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks |
| title_full | Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks |
| title_fullStr | Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks |
| title_full_unstemmed | Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks |
| title_short | Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks |
| title_sort | integrated carbon flow tracing and topology reconfiguration for low carbon optimal dispatch in dg embedded distribution networks |
| topic | distribution networks carbon emission flow topology reconfiguration distributed generation Forward/Backward Sweep Q-learning enhanced Moth Flame Optimization |
| url | https://www.mdpi.com/2227-7390/13/15/2395 |
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