Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning

Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy lev...

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Autores principales:	Mohammad Sadeghi, Shahram Mollahasani, Melike Erol-Kantarci
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning Bayesian reinforcement learning microgrid smart grid Technology T
Acceso en línea:	https://doaj.org/article/b05a42816f72405c912ee02346bf27d0
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id	oai:doaj.org-article:b05a42816f72405c912ee02346bf27d0
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spelling	oai:doaj.org-article:b05a42816f72405c912ee02346bf27d02021-11-25T17:25:54ZCost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning10.3390/en142274811996-1073https://doaj.org/article/b05a42816f72405c912ee02346bf27d02021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7481https://doaj.org/toc/1996-1073Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model.Mohammad SadeghiShahram MollahasaniMelike Erol-KantarciMDPI AGarticlemachine learningBayesian reinforcement learningmicrogridsmart gridTechnologyTENEnergies, Vol 14, Iss 7481, p 7481 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning Bayesian reinforcement learning microgrid smart grid Technology T
spellingShingle	machine learning Bayesian reinforcement learning microgrid smart grid Technology T Mohammad Sadeghi Shahram Mollahasani Melike Erol-Kantarci Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning
description	Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model.
format	article
author	Mohammad Sadeghi Shahram Mollahasani Melike Erol-Kantarci
author_facet	Mohammad Sadeghi Shahram Mollahasani Melike Erol-Kantarci
author_sort	Mohammad Sadeghi
title	Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning
title_short	Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning
title_full	Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning
title_fullStr	Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning
title_full_unstemmed	Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning
title_sort	cost-optimized microgrid coalitions using bayesian reinforcement learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b05a42816f72405c912ee02346bf27d0
work_keys_str_mv	AT mohammadsadeghi costoptimizedmicrogridcoalitionsusingbayesianreinforcementlearning AT shahrammollahasani costoptimizedmicrogridcoalitionsusingbayesianreinforcementlearning AT melikeerolkantarci costoptimizedmicrogridcoalitionsusingbayesianreinforcementlearning
_version_	1718412369194909696

Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning

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