Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning

As the terminal of electricity consumption, the distribution network is a vital field to lower the carbon emission of the power system. With the integration of distributed energy resources, the flexibility of the distribution network has been promoted significantly where dispatch actions can be empl...
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Autor*in:	Han Cui [verfasserIn] Yujian Ye [verfasserIn] Qidong Tian [verfasserIn] Yi Tang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	decarbonization dispatch active distribution networks safe reinforcement learning renewable generation electricity storage

Übergeordnetes Werk:	In: Frontiers in Energy Research - Frontiers Media S.A., 2014, 10(2022)
Übergeordnetes Werk:	volume:10 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fenrg.2022.933011

Katalog-ID:	DOAJ03692279X

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source	In Frontiers in Energy Research 10(2022) volume:10 year:2022
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authorswithroles_txt_mv	Han Cui @@aut@@ Yujian Ye @@aut@@ Qidong Tian @@aut@@ Yi Tang @@aut@@
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author	Han Cui
spellingShingle	Han Cui misc decarbonization dispatch misc active distribution networks misc safe reinforcement learning misc renewable generation misc electricity storage misc General Works misc A Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning
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topic_title	Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning decarbonization dispatch active distribution networks safe reinforcement learning renewable generation electricity storage
topic	misc decarbonization dispatch misc active distribution networks misc safe reinforcement learning misc renewable generation misc electricity storage misc General Works misc A
topic_unstemmed	misc decarbonization dispatch misc active distribution networks misc safe reinforcement learning misc renewable generation misc electricity storage misc General Works misc A
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title	Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning
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title_full	Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning
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title_sort	security constrained dispatch for renewable proliferated distribution network based on safe reinforcement learning
title_auth	Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning
abstract	As the terminal of electricity consumption, the distribution network is a vital field to lower the carbon emission of the power system. With the integration of distributed energy resources, the flexibility of the distribution network has been promoted significantly where dispatch actions can be employed to lower carbon emissions without compromising the accessibility of reliable electricity. This study proposes a security constrained dispatch policy based on safe reinforcement learning for the distribution network. The researched problem is set up as a constrained Markov decision process, where continuous-discrete mixed action space and high-dimensional state space are in place. In addition, security-related rules are embedded into the problem formulation. To guarantee the generalization of the reinforcement learning agent, various scenarios are generated in the offline training stage, including randomness of renewables, scheduled maintenance, and different load profiles. A case study is performed on a modified version of the IEEE 33-bus system, and the numerical results verify the effectiveness of the proposed method in decarbonization.
abstractGer	As the terminal of electricity consumption, the distribution network is a vital field to lower the carbon emission of the power system. With the integration of distributed energy resources, the flexibility of the distribution network has been promoted significantly where dispatch actions can be employed to lower carbon emissions without compromising the accessibility of reliable electricity. This study proposes a security constrained dispatch policy based on safe reinforcement learning for the distribution network. The researched problem is set up as a constrained Markov decision process, where continuous-discrete mixed action space and high-dimensional state space are in place. In addition, security-related rules are embedded into the problem formulation. To guarantee the generalization of the reinforcement learning agent, various scenarios are generated in the offline training stage, including randomness of renewables, scheduled maintenance, and different load profiles. A case study is performed on a modified version of the IEEE 33-bus system, and the numerical results verify the effectiveness of the proposed method in decarbonization.
abstract_unstemmed	As the terminal of electricity consumption, the distribution network is a vital field to lower the carbon emission of the power system. With the integration of distributed energy resources, the flexibility of the distribution network has been promoted significantly where dispatch actions can be employed to lower carbon emissions without compromising the accessibility of reliable electricity. This study proposes a security constrained dispatch policy based on safe reinforcement learning for the distribution network. The researched problem is set up as a constrained Markov decision process, where continuous-discrete mixed action space and high-dimensional state space are in place. In addition, security-related rules are embedded into the problem formulation. To guarantee the generalization of the reinforcement learning agent, various scenarios are generated in the offline training stage, including randomness of renewables, scheduled maintenance, and different load profiles. A case study is performed on a modified version of the IEEE 33-bus system, and the numerical results verify the effectiveness of the proposed method in decarbonization.
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title_short	Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning
url	https://doi.org/10.3389/fenrg.2022.933011 https://doaj.org/article/046d87999a8042c080bcfb444ef7bd93 https://www.frontiersin.org/articles/10.3389/fenrg.2022.933011/full https://doaj.org/toc/2296-598X
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