Power grid management via semi-Markov afterstate Actor-Critic준 마코프 사후상태 액터크리틱을 활용한 전력망 운영

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dc.contributor.advisorKim, Kee-Eung-
dc.contributor.advisor김기응-
dc.contributor.authorYoon, Deunsol-
dc.date.accessioned2023-06-22T19:31:09Z-
dc.date.available2023-06-22T19:31:09Z-
dc.date.issued2022-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=997680&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/308174-
dc.description학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2022.2,[iii, 19 p. :]-
dc.description.abstractSafe and reliable electricity transmission in power grids is crucial for modern society. It is thus quite natural that there has been a growing interest in the automatic management of power grids, exemplified by the Learning to Run a Power Network Challenge (L2RPN), modeling the problem as a reinforcement learning (RL) task. However, it is highly challenging to manage a real-world scale power grid, mostly due to the massive scale of its state and action space. In this paper, we present an off-policy actor-critic approach that effectively tackles the unique challenges in power grid management by RL, adopting the hierarchical policy together with the afterstate representation. Our agent ranked first in the latest challenge (L2RPN WCCI 2020), being able to avoid disastrous situations while maintaining the highest level of operational efficiency in every test scenario. This paper provides a formal description of the algorithmic aspect of our approach, as well as further experimental studies on diverse power grids.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.titlePower grid management via semi-Markov afterstate Actor-Critic-
dc.title.alternative준 마코프 사후상태 액터크리틱을 활용한 전력망 운영-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :김재철AI대학원,-
dc.contributor.alternativeauthor윤든솔-
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