[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124800-en":3,"doc-seo-124800-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124800,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Enhancing DC microgrid performance through machine learning-optimized droop control - read online","A machine learning-based optimized droop method is proposed to simultaneously reduce production cost and power line losses in direct current microgrids. It addresses limitations of conventional hybrid droop coordination, where the weighting coefficients must be arbitrarily tuned to achieve targeted reductions. The approach uses artificial intelligence to predict both cost and losses, then applies gradient-descend optimization to independently tune both objectives under varying operating scenarios. Comparative results against classical and hybrid schemes validate effectiveness, including during rapid load changes.","Received: 9 January 2024  Revised: 16 March 2024  Accepted: 30 March 2024  IET Generation, Transmission & Distribution  \nDOI: 10.1049/gtd2.13169  \nORIGINAL RESEARCH  \nEnhancing DC microgrid performance through machine learning-optimized droop control  \nYounes Saeidinia1   Mohammadreza Arabshahi1   Mohammad Aminirad2  Miadreza Shafie-khah3  \n1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran  \n2Faculty of Technology and Engineering, Iran University of Science and Technology (IUST), Tehran, Iran  \n3 School of Technology and Innovations, University of Vaasa, Vaasa, Finland  \nCorrespondence  \nYounes Saeidinia, Faculty of Electrical Engineering, Shahid Beheshti University, AC, Tehran, Iran. [Email:](Email: y.saeidiniya@alumni.sbu.ac.ir)[ y.saeidiniya@alumni.sbu.ac.ir](Email: y.saeidiniya@alumni.sbu.ac.ir)  \nAbstract  \nA machine learning-based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs) . Traditionally, a communication-less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLLin a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.  \n1  INTRODUCTION  \nRenewable energies, including solar, wind, hydro, and biomass, are sources of electricity generation that do not rely on fossil fuels [1] . By replacing carbon-intensive energy sources, they play a crucial role in significantly mitigating greenhouse gas emissions and addressing climate change. The transition to renewable energy is essential for achieving global climate targets, as outlined in agreements like the Paris Agreement. In this context, microgrids (MGs) offer a promising architecture to meet the zero-carbon targets.  \nMGs are generally classified into three types: direct current (DC) MGs, AC MGs, and hybrid [2] . Among these, DC MGshave gained considerable interest due to their distinctive features. A DC MG typically incorporates local energy sources, such as solar panels, wind turbines, batteries, or fuel cells, along with loads and energy storage devices. The MG can be interconnected with the main power grid or operate autonomously, providing electricity to a specific area or building [3] .  \nWhile DC MGs offer promising advantages, including efficiency, reliability, and incorporation of renewable energy, they also encounter challenges, particularly when there is a substantial increase in the penetration of renewable energy sources (RESs) . These challenges arise from the need to effectively manage and control the fluctuating nature of renewable energy generation within the MG. The intermittent nature of renewable sources can lead to voltage fluctuations, necessitating the implementation of advanced control and power management systems to maintain stability and ensure grid reliability. To design a suitable power management strategy in the presence of various distributed generations (DGs), many attempts have been made by the researchers.  \nSome researchers have explored control plans to ensure the efficient, coordinated, and reliable operation of multiple RESsand ESSs. The focus of these control strategies is to optimize the distribution of loads and minimize generation costs, particularly when aiming for cost-effective operation and determining the m","cbCailIV7cJsGVDb","https://ap.wps.com/l/cbCailIV7cJsGVDb","pdf",3328436,1,16,"English","en",105,"# Introduction\n## Renewable energy and microgrids\n## DC microgrids: types and challenges\n## Control strategies and coordination methods\n## Economic dispatch and distributed control","[{\"question\":\"What problem does the proposed method address in DC microgrids?\",\"answer\":\"It targets simultaneous reduction of production cost and power line losses while avoiding arbitrary tuning of weighting coefficients used in conventional hybrid droop methods.\"},{\"question\":\"How does the method optimize production cost and power line losses?\",\"answer\":\"It uses machine learning to predict both production cost and power line losses, then applies a gradient descent optimization process to independently optimize each objective per scenario.\"},{\"question\":\"How is the proposed approach validated?\",\"answer\":\"Effectiveness is confirmed via a comparative study against classical and hybrid droop coordination schemes across scenarios including rapid load changes.\"}]","Enhancing DC microgrid performance through machine learning-optimized droop control - 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