Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory
In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon meas...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/22/5610 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850145499261173760 |
|---|---|
| author | Jiang Bian Yang Wang Zhaoshuai Dang Tianchun Xiang Zhiyong Gan Ting Yang |
| author_facet | Jiang Bian Yang Wang Zhaoshuai Dang Tianchun Xiang Zhiyong Gan Ting Yang |
| author_sort | Jiang Bian |
| collection | DOAJ |
| description | In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case. |
| format | Article |
| id | doaj-art-30aa3043c1034dabac2b96ed3dbb6203 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-30aa3043c1034dabac2b96ed3dbb62032025-08-20T02:28:05ZengMDPI AGEnergies1996-10732024-11-011722561010.3390/en17225610Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow TheoryJiang Bian0Yang Wang1Zhaoshuai Dang2Tianchun Xiang3Zhiyong Gan4Ting Yang5Electric Power Science Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, ChinaState Grid Tianjin Electric Power Company, Tianjin 300010, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaState Grid Tianjin Electric Power Company, Tianjin 300010, ChinaElectric Power Science Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaIn the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case.https://www.mdpi.com/1996-1073/17/22/5610<i>Index Terms</i>—artificial neural networksdecision support systemsgreen cleaningpower system control |
| spellingShingle | Jiang Bian Yang Wang Zhaoshuai Dang Tianchun Xiang Zhiyong Gan Ting Yang Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory Energies <i>Index Terms</i>—artificial neural networks decision support systems green cleaning power system control |
| title | Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory |
| title_full | Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory |
| title_fullStr | Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory |
| title_full_unstemmed | Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory |
| title_short | Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory |
| title_sort | low carbon dispatch method for active distribution network based on carbon emission flow theory |
| topic | <i>Index Terms</i>—artificial neural networks decision support systems green cleaning power system control |
| url | https://www.mdpi.com/1996-1073/17/22/5610 |
| work_keys_str_mv | AT jiangbian lowcarbondispatchmethodforactivedistributionnetworkbasedoncarbonemissionflowtheory AT yangwang lowcarbondispatchmethodforactivedistributionnetworkbasedoncarbonemissionflowtheory AT zhaoshuaidang lowcarbondispatchmethodforactivedistributionnetworkbasedoncarbonemissionflowtheory AT tianchunxiang lowcarbondispatchmethodforactivedistributionnetworkbasedoncarbonemissionflowtheory AT zhiyonggan lowcarbondispatchmethodforactivedistributionnetworkbasedoncarbonemissionflowtheory AT tingyang lowcarbondispatchmethodforactivedistributionnetworkbasedoncarbonemissionflowtheory |