Open AccessConceptual Microgrid Management Framework Based on Adaptive and Autonomous Multi-Agent Systems
Author(s) -
Diego Fernando Lizondo,
Victor Adrian Jimenez,
Pedro Bernabé Araujo,
Adrián Will
Publication year - 2022
Publication title -
journal of computer science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 48
eISSN - 1666-6046
pISSN - 1666-6038
DOI - 10.24215/16666038.22.e01
Subject(s) - microgrid , computer science , smart grid , distributed computing , distributed generation , granularity , sizing , genetic algorithm , energy management , energy management system , energy consumption , artificial intelligence , control (management) , energy (signal processing) , machine learning , art , mathematics , quantum mechanics , visual arts , biology , operating system , ecology , power (physics) , statistics , physics
The Smart Grids paradigm emerged as a response to the need to modernize the electric grid and address problems related to the demand for better quality energy. However, there are no fully developed and implemented smart grids, but only some minor scale tests to prove the concepts. Centralized systems are still common, with a low granularity of control and reduced monitoring capacity, especially in low-voltage networks. In this work, we propose a framework for Microgrid Management, addressing problems such as determining how to control the energy demand and peak loads, the effect of the energy consumption in the network, and the amount of energy required. We proposed a solution based on autonomous and distributed systems for the following problems: Peak Load addressed with AIN-DSM distributed algorithm, transformer lifespan estimation using a thermal model adjusted by Genetic Algorithms, and Short-Term Load Forecasting based on Artificial Neural Networks and Genetic Algorithms. The distributed paradigm of the Organization Centered Multi-Agent Systems methodology was applied for the framework's modeling and development. The results obtained by using these solutions in the Tucumán province, Argentina, show the system's capabilities and the relevance of the information produced from the framework.