{"title":"SiPhyR:基于端到端学习的动态网格重构优化框架","authors":"Rabab Haider;Anuradha Annaswamy;Biswadip Dey;Amit Chakraborty","doi":"10.1109/TSG.2024.3458438","DOIUrl":null,"url":null,"abstract":"Distribution grids are rapidly transforming with increasing penetration of distributed renewable generation, storage, and electric vehicles. These devices introduce new dynamic signatures including intermittency and changing demand patterns. To maintain a safe and reliable power grid, new operating paradigms are required for fast and accurate decision making. It is in this regard that we leverage machine learning for grid operations. We propose SiPhyR, a physics-informed machine learning framework that accomplishes end-to-end learning-based optimization for distribution grid reconfiguration. The reconfiguration problem optimizes the topology of the grid and power flows from distributed devices to reduce line losses, improve voltage profiles, and increase renewable energy utilization. To address the computational complexities of NP-hardness of binary decision variables, we propose a physics-informed rounding approach that explicitly embeds discrete decisions into an end-to-end differentiable framework. This enables Grid-SiPhyR to learn to simultaneously optimize grid topology and generator dispatch with certified satisfiability of safety-critical constraints. Our results are shown on three canonical distribution grids.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1248-1260"},"PeriodicalIF":8.6000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SiPhyR: An End-to-End Learning-Based Optimization Framework for Dynamic Grid Reconfiguration\",\"authors\":\"Rabab Haider;Anuradha Annaswamy;Biswadip Dey;Amit Chakraborty\",\"doi\":\"10.1109/TSG.2024.3458438\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Distribution grids are rapidly transforming with increasing penetration of distributed renewable generation, storage, and electric vehicles. These devices introduce new dynamic signatures including intermittency and changing demand patterns. To maintain a safe and reliable power grid, new operating paradigms are required for fast and accurate decision making. It is in this regard that we leverage machine learning for grid operations. We propose SiPhyR, a physics-informed machine learning framework that accomplishes end-to-end learning-based optimization for distribution grid reconfiguration. The reconfiguration problem optimizes the topology of the grid and power flows from distributed devices to reduce line losses, improve voltage profiles, and increase renewable energy utilization. To address the computational complexities of NP-hardness of binary decision variables, we propose a physics-informed rounding approach that explicitly embeds discrete decisions into an end-to-end differentiable framework. This enables Grid-SiPhyR to learn to simultaneously optimize grid topology and generator dispatch with certified satisfiability of safety-critical constraints. Our results are shown on three canonical distribution grids.\",\"PeriodicalId\":13331,\"journal\":{\"name\":\"IEEE Transactions on Smart Grid\",\"volume\":\"16 2\",\"pages\":\"1248-1260\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Smart Grid\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10677392/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Smart Grid","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10677392/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
SiPhyR: An End-to-End Learning-Based Optimization Framework for Dynamic Grid Reconfiguration
Distribution grids are rapidly transforming with increasing penetration of distributed renewable generation, storage, and electric vehicles. These devices introduce new dynamic signatures including intermittency and changing demand patterns. To maintain a safe and reliable power grid, new operating paradigms are required for fast and accurate decision making. It is in this regard that we leverage machine learning for grid operations. We propose SiPhyR, a physics-informed machine learning framework that accomplishes end-to-end learning-based optimization for distribution grid reconfiguration. The reconfiguration problem optimizes the topology of the grid and power flows from distributed devices to reduce line losses, improve voltage profiles, and increase renewable energy utilization. To address the computational complexities of NP-hardness of binary decision variables, we propose a physics-informed rounding approach that explicitly embeds discrete decisions into an end-to-end differentiable framework. This enables Grid-SiPhyR to learn to simultaneously optimize grid topology and generator dispatch with certified satisfiability of safety-critical constraints. Our results are shown on three canonical distribution grids.
期刊介绍:
The IEEE Transactions on Smart Grid is a multidisciplinary journal that focuses on research and development in the field of smart grid technology. It covers various aspects of the smart grid, including energy networks, prosumers (consumers who also produce energy), electric transportation, distributed energy resources, and communications. The journal also addresses the integration of microgrids and active distribution networks with transmission systems. It publishes original research on smart grid theories and principles, including technologies and systems for demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and electric vehicle integration. Additionally, the journal considers surveys of existing work on the smart grid that propose new perspectives on the history and future of intelligent and active grids.