A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids
Abstract As the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG‐CES is presented, taking into account d...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Energy Systems Integration |
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| Online Access: | https://doi.org/10.1049/esi2.12135 |
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| author | Haobo Rong Jianhui Wang Honghai Kuang |
| author_facet | Haobo Rong Jianhui Wang Honghai Kuang |
| author_sort | Haobo Rong |
| collection | DOAJ |
| description | Abstract As the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG‐CES is presented, taking into account distributed generation under uncertainty. The proposed model follows a two‐stage, four‐layer ‘min‐min‐max‐min’ structure. In the first stage, the initial layer ‘min’ addresses the distributed cooperation problem between MG and CES, while the second stage employs ‘min‐max‐min’ to optimise the scheduling of MG. To enhance the solution process and expedite convergence, the authors introduce a column‐constrained generation algorithm with alternating iterations of U and D variables (CCG‐UD) specifically designed for the three‐layer structure in the second stage. This algorithm effectively decouples subproblems, contributing to accelerated solutions. To tackle the convergence challenges posed by the non‐convex MG‐CES model, the authors integrate the Bregman alternating direction method with multipliers (BADMM) with CCG‐UD in the final solution step. Real case tests are conducted using three zone‐level MGs to validate the efficacy of the proposed model and methodology. The results demonstrate the practical utility and efficiency of the developed approach in addressing distributed cooperation challenges in microgrid systems with energy storage. |
| format | Article |
| id | doaj-art-286f5b1ad902450a8e8a98e81d3e6144 |
| institution | Kabale University |
| issn | 2516-8401 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj-art-286f5b1ad902450a8e8a98e81d3e61442024-12-23T18:59:41ZengWileyIET Energy Systems Integration2516-84012024-12-016440642010.1049/esi2.12135A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgridsHaobo Rong0Jianhui Wang1Honghai Kuang2Department of Electrical and Control Engineering Nanjing Polytechnic Institute Nanjing ChinaDepartment of Electrical and Information Engineering Lanzhou University of Technology Lanzhou ChinaDepartment of Electrical and Information Engineering Hunan University of Technology Zhuzhou ChinaAbstract As the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG‐CES is presented, taking into account distributed generation under uncertainty. The proposed model follows a two‐stage, four‐layer ‘min‐min‐max‐min’ structure. In the first stage, the initial layer ‘min’ addresses the distributed cooperation problem between MG and CES, while the second stage employs ‘min‐max‐min’ to optimise the scheduling of MG. To enhance the solution process and expedite convergence, the authors introduce a column‐constrained generation algorithm with alternating iterations of U and D variables (CCG‐UD) specifically designed for the three‐layer structure in the second stage. This algorithm effectively decouples subproblems, contributing to accelerated solutions. To tackle the convergence challenges posed by the non‐convex MG‐CES model, the authors integrate the Bregman alternating direction method with multipliers (BADMM) with CCG‐UD in the final solution step. Real case tests are conducted using three zone‐level MGs to validate the efficacy of the proposed model and methodology. The results demonstrate the practical utility and efficiency of the developed approach in addressing distributed cooperation challenges in microgrid systems with energy storage.https://doi.org/10.1049/esi2.12135coolingdistributed power generationdistribution networksenergy management systemsgame theorymatlab |
| spellingShingle | Haobo Rong Jianhui Wang Honghai Kuang A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids IET Energy Systems Integration cooling distributed power generation distribution networks energy management systems game theory matlab |
| title | A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids |
| title_full | A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids |
| title_fullStr | A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids |
| title_full_unstemmed | A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids |
| title_short | A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids |
| title_sort | two stage four layer robust optimisation model for distributed cooperation in multi microgrids |
| topic | cooling distributed power generation distribution networks energy management systems game theory matlab |
| url | https://doi.org/10.1049/esi2.12135 |
| work_keys_str_mv | AT haoborong atwostagefourlayerrobustoptimisationmodelfordistributedcooperationinmultimicrogrids AT jianhuiwang atwostagefourlayerrobustoptimisationmodelfordistributedcooperationinmultimicrogrids AT honghaikuang atwostagefourlayerrobustoptimisationmodelfordistributedcooperationinmultimicrogrids AT haoborong twostagefourlayerrobustoptimisationmodelfordistributedcooperationinmultimicrogrids AT jianhuiwang twostagefourlayerrobustoptimisationmodelfordistributedcooperationinmultimicrogrids AT honghaikuang twostagefourlayerrobustoptimisationmodelfordistributedcooperationinmultimicrogrids |