{"title":"A Two-Stage Hierarchical Energy Management System for Interconnected Microgrids Using ELM and GA","authors":"Negar Dehghani Mahmoudabadi, Mehran Khalaj, Davood Jafari, Ali Taghizadeh Herat, Parisa Mousavi Ahranjani","doi":"10.1049/rpg2.70087","DOIUrl":null,"url":null,"abstract":"<p>This paper presents a novel hierarchical two-layer energy management system for grid-connected microgrids in the presence of uncertainty. In the first stage, each microgrid separately optimises its own local scheduling with a combination of renewable and dispatchable energy resources. In the second stage, the energy trading among the microgrids is facilitated by a DSO through the application of a Genetic Algorithm (GA) for optimising overall operational costs and system flexibility. In order to tackle the natural variability of the renewable energy resources, an extreme learning machine (ELM) is used to generate probabilistic wind and solar power generation forecasts. The optimisation problem is formulated as a mixed integer nonlinear programming (MINLP) model with continuous and binary decision variables. A 30-day case study of three interconnected microgrids under normal and contingency scenarios is tested using this proposed framework. Simulation results display significant improvements in load shedding reduction, scheduling efficiency, and system flexibility. Also, the modularity of the framework enables scaling and integration of vehicle-to-grid (V2G) technologies, making it a suitable solution for real-world smart grid deployment.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70087","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Renewable Power Generation","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/rpg2.70087","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a novel hierarchical two-layer energy management system for grid-connected microgrids in the presence of uncertainty. In the first stage, each microgrid separately optimises its own local scheduling with a combination of renewable and dispatchable energy resources. In the second stage, the energy trading among the microgrids is facilitated by a DSO through the application of a Genetic Algorithm (GA) for optimising overall operational costs and system flexibility. In order to tackle the natural variability of the renewable energy resources, an extreme learning machine (ELM) is used to generate probabilistic wind and solar power generation forecasts. The optimisation problem is formulated as a mixed integer nonlinear programming (MINLP) model with continuous and binary decision variables. A 30-day case study of three interconnected microgrids under normal and contingency scenarios is tested using this proposed framework. Simulation results display significant improvements in load shedding reduction, scheduling efficiency, and system flexibility. Also, the modularity of the framework enables scaling and integration of vehicle-to-grid (V2G) technologies, making it a suitable solution for real-world smart grid deployment.
期刊介绍:
IET Renewable Power Generation (RPG) brings together the topics of renewable energy technology, power generation and systems integration, with techno-economic issues. All renewable energy generation technologies are within the scope of the journal.
Specific technology areas covered by the journal include:
Wind power technology and systems
Photovoltaics
Solar thermal power generation
Geothermal energy
Fuel cells
Wave power
Marine current energy
Biomass conversion and power generation
What differentiates RPG from technology specific journals is a concern with power generation and how the characteristics of the different renewable sources affect electrical power conversion, including power electronic design, integration in to power systems, and techno-economic issues. Other technologies that have a direct role in sustainable power generation such as fuel cells and energy storage are also covered, as are system control approaches such as demand side management, which facilitate the integration of renewable sources into power systems, both large and small.
The journal provides a forum for the presentation of new research, development and applications of renewable power generation. Demonstrations and experimentally based research are particularly valued, and modelling studies should as far as possible be validated so as to give confidence that the models are representative of real-world behavior. Research that explores issues where the characteristics of the renewable energy source and their control impact on the power conversion is welcome. Papers covering the wider areas of power system control and operation, including scheduling and protection that are central to the challenge of renewable power integration are particularly encouraged.
The journal is technology focused covering design, demonstration, modelling and analysis, but papers covering techno-economic issues are also of interest. Papers presenting new modelling and theory are welcome but this must be relevant to real power systems and power generation. Most papers are expected to include significant novelty of approach or application that has general applicability, and where appropriate include experimental results. Critical reviews of relevant topics are also invited and these would be expected to be comprehensive and fully referenced.
Current Special Issue. Call for papers:
Power Quality and Protection in Renewable Energy Systems and Microgrids - https://digital-library.theiet.org/files/IET_RPG_CFP_PQPRESM.pdf
Energy and Rail/Road Transportation Integrated Development - https://digital-library.theiet.org/files/IET_RPG_CFP_ERTID.pdf