인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
With the increasing complexity of modern power systems due to the access of large-scale renewable energy sources, minimizing operational costs while achieving stable grid operation has become a core challenge in power scheduling and optimization. Energy dispatch, topology control and emergency load shedding are key measures to improve power system stability and flexibility. However, the outputs of their traditional control policies rely on predefined rules or mathematical optimization models, which are prone to computational bottlenecks and response lags in high-dimensional dynamic environments, making it difficult to meet the demands of smart grids. In recent years, deep reinforcement learning (DRL) has gradually become a cutting-edge technology for power system scheduling and control by virtue of its powerful adaptive learning and decision optimization capabilities. According to the existing research, DRL can improve the flexibility and anti-interference ability of the power grid by learning the optimal policies through autonomous interaction, surpassing the real-time decision-making ability of traditional optimization methods in high-dimensional state space. In this paper, we systematically review the applications of DRL in energy dispatch, topology control and emergency load shedding, focus on its optimization policies, technological breakthroughs and applicability, and analyze the current challenges and future research directions.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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