Distributed robust scheduling of distribution-microgrid based on deep learning method integration
Aiming at the problems such as the uncertainty of distributed power output and the low efficiency of operation in the coupled system scheduling of distribution network and microgrid, an optimized scheduling model of Branch-bar operation with chance constraint based on the integration of deep learnin...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-06-01
|
| Series: | Diance yu yibiao |
| Subjects: | |
| Online Access: | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20230214013&flag=1&journal_id=dcyyben&year_id=2025 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850166037525299200 |
|---|---|
| author | WANG Yihong LIU Jichun QIU Gao ZHOU Hao HE Peixin |
| author_facet | WANG Yihong LIU Jichun QIU Gao ZHOU Hao HE Peixin |
| author_sort | WANG Yihong |
| collection | DOAJ |
| description | Aiming at the problems such as the uncertainty of distributed power output and the low efficiency of operation in the coupled system scheduling of distribution network and microgrid, an optimized scheduling model of Branch-bar operation with chance constraint based on the integration of deep learning method for distribution network and microgrid interconnection system is proposed. The uncertainty probability set of renewable energy and load of microgrid is constructed based on probabilistic output support vector machine, Bayesian neural network and deep belief network. The D-S evidence theory information integration framework is established, the evidence correction method based on Kappa coefficient and accuracy weight is proposed, the evidence is revised from the output and load power of renewable energy, and then, the uncertainty probability set with higher precision is obtained, and the probability distribution fuzzy set of source load power is obtained. The two-stage rolling scheduling optimization model of multi-microgrid is established, namely, the first-stage pre-scheduling model and the second-stage real-time regulation model. In the first stage, the energy pre-allocation is carried out to achieve the optimal global operation economy of multi-microgrid region. The second stage is the real-time operation control stage. Considering the uncertainty of the real-time output of new energy in the microgrid, the proposed two-stage robust economic dispatching model adopts column-and-constraint generation (C&CG) and alternating direction multiplier method (ADMM) combines the column and constraint generation algorithm and joint target cascade analysis algorithm for distributed solution. The simulation results show that the safe and reliable operation of the distribution-microgrid market can be effectively improved under the uncertainty of the source load prediction, and the new energy consumption rate and economic benefits of the interconnected system can be improved. |
| format | Article |
| id | doaj-art-7f4d61c5e283410faf1303f1cfb6eb0d |
| institution | OA Journals |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-7f4d61c5e283410faf1303f1cfb6eb0d2025-08-20T02:21:34ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-06-01626354410.19753/j.issn1001-1390.2025.06.0041001-1390(2025)06-0035-10Distributed robust scheduling of distribution-microgrid based on deep learning method integrationWANG Yihong0LIU Jichun1QIU Gao2ZHOU Hao3HE Peixin4School of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaSchool of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaAiming at the problems such as the uncertainty of distributed power output and the low efficiency of operation in the coupled system scheduling of distribution network and microgrid, an optimized scheduling model of Branch-bar operation with chance constraint based on the integration of deep learning method for distribution network and microgrid interconnection system is proposed. The uncertainty probability set of renewable energy and load of microgrid is constructed based on probabilistic output support vector machine, Bayesian neural network and deep belief network. The D-S evidence theory information integration framework is established, the evidence correction method based on Kappa coefficient and accuracy weight is proposed, the evidence is revised from the output and load power of renewable energy, and then, the uncertainty probability set with higher precision is obtained, and the probability distribution fuzzy set of source load power is obtained. The two-stage rolling scheduling optimization model of multi-microgrid is established, namely, the first-stage pre-scheduling model and the second-stage real-time regulation model. In the first stage, the energy pre-allocation is carried out to achieve the optimal global operation economy of multi-microgrid region. The second stage is the real-time operation control stage. Considering the uncertainty of the real-time output of new energy in the microgrid, the proposed two-stage robust economic dispatching model adopts column-and-constraint generation (C&CG) and alternating direction multiplier method (ADMM) combines the column and constraint generation algorithm and joint target cascade analysis algorithm for distributed solution. The simulation results show that the safe and reliable operation of the distribution-microgrid market can be effectively improved under the uncertainty of the source load prediction, and the new energy consumption rate and economic benefits of the interconnected system can be improved.http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20230214013&flag=1&journal_id=dcyyben&year_id=2025deep learning integrationdistribution networkmulti-microgriddistributed robustnessopportunity constraintscheduling optimization |
| spellingShingle | WANG Yihong LIU Jichun QIU Gao ZHOU Hao HE Peixin Distributed robust scheduling of distribution-microgrid based on deep learning method integration Diance yu yibiao deep learning integration distribution network multi-microgrid distributed robustness opportunity constraint scheduling optimization |
| title | Distributed robust scheduling of distribution-microgrid based on deep learning method integration |
| title_full | Distributed robust scheduling of distribution-microgrid based on deep learning method integration |
| title_fullStr | Distributed robust scheduling of distribution-microgrid based on deep learning method integration |
| title_full_unstemmed | Distributed robust scheduling of distribution-microgrid based on deep learning method integration |
| title_short | Distributed robust scheduling of distribution-microgrid based on deep learning method integration |
| title_sort | distributed robust scheduling of distribution microgrid based on deep learning method integration |
| topic | deep learning integration distribution network multi-microgrid distributed robustness opportunity constraint scheduling optimization |
| url | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20230214013&flag=1&journal_id=dcyyben&year_id=2025 |
| work_keys_str_mv | AT wangyihong distributedrobustschedulingofdistributionmicrogridbasedondeeplearningmethodintegration AT liujichun distributedrobustschedulingofdistributionmicrogridbasedondeeplearningmethodintegration AT qiugao distributedrobustschedulingofdistributionmicrogridbasedondeeplearningmethodintegration AT zhouhao distributedrobustschedulingofdistributionmicrogridbasedondeeplearningmethodintegration AT hepeixin distributedrobustschedulingofdistributionmicrogridbasedondeeplearningmethodintegration |