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창의적인 신지식 창출과 산업계와의 협력적 네트워크 구축

HIGHLY CITED PAPERS (HCP)

  • 전임교원이 최근 5년간 출판한 논문 중, 연도별 카테고리별 피인용 상위 1%에 달성된 논문
  • 매월 1회 업데이트
SCIE Article
A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach
Author
Lu, RZ; Hong, SH; Zhang, XF
Corresponding Author Info
Prof. Hong, Seung HO
Professor
E-mail
이메일 shhong@hanyang.ac.kr
Document Type
Source
APPLIED ENERGY 2018,15, 220-230
Times Cited
101 (2021.09.08.)
External Information
https://www.sciencedirect.com/science/article/pii/S0306261918304112
Abstract
- 7월/8월 2020부로, 이 인용 빈도가 높은 논문의 인용 횟수가 분야와 출판 연도에 대해 Engineering 관련 학술 분야에서 상위 1%에 올랐습니다.
- JUN 2018

<Abstract>
With the modern advanced information and communication technologies in smart grid systems, demand response (DR) has become an effective method for improving grid reliability and reducing energy costs due to the ability to react quickly to supply-demand mismatches by adjusting flexible loads on the demand side. This paper proposes a dynamic pricing DR algorithm for energy management in a hierarchical electricity market that considers both service provider’s (SP) profit and customers’ (CUs) costs. Reinforcement learning (RL) is used to illustrate the hierarchical decision-making framework, in which the dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP), and Q-learning is adopted to solve this decision-making problem. Using RL, the SP can adaptively decide the retail electricity price during the on-line learning process where the uncertainty of CUs’ load demand profiles and the flexibility of wholesale electricity prices are addressed. Simulation results show that this proposed DR algorithm, can promote SP profitability, reduce energy costs for CUs, balance energy supply and demand in the electricity market, and improve the reliability of electric power systems, which can be regarded as a win-win strategy for both SP and CUs.
Web of Science Categories
Energy & Fuels; Engineering
Funding
Language
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