{"title":"多微网分布式合作的两阶段四层稳健优化模型","authors":"Haobo Rong, Jianhui Wang, Honghai Kuang","doi":"10.1049/esi2.12135","DOIUrl":null,"url":null,"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.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids\",\"authors\":\"Haobo Rong, Jianhui Wang, Honghai Kuang\",\"doi\":\"10.1049/esi2.12135\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":33288,\"journal\":{\"name\":\"IET Energy Systems Integration\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Energy Systems Integration\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1049/esi2.12135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Energy Systems Integration","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/esi2.12135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
随着微电网(MG)与储能技术的不断融合,MG 与共用储能技术(CES)之间的高效分布式合作变得至关重要。考虑到不确定情况下的分布式发电,本文提出了一种用于 MG-CES 分布式合作的稳健优化模型。该模型采用两阶段四层 "最小-最大-最小 "结构。在第一阶段,初始层 "最小 "解决 MG 和 CES 之间的分布式合作问题,第二阶段采用 "最小-最大-最小 "优化 MG 的调度。为了改进求解过程并加快收敛速度,作者在第二阶段引入了专为三层结构设计的列约束生成算法(CCG-UD),交替迭代 U 和 D 变量。该算法有效地解耦了子问题,有助于加速求解。为了解决非凸 MG-CES 模型带来的收敛难题,作者在最后求解步骤中将带乘数的布雷格曼交替方向法(BADMM)与 CCG-UD 相结合。使用三个区级 MG 进行了实际案例测试,以验证所提模型和方法的有效性。结果表明,所开发的方法在应对带储能的微电网系统中的分布式合作挑战方面具有实用性和高效性。
A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids
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.