연구진흥

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
[Excluded] Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Author
Lu, Renzhi; Hong, Hong, Seung Ho;
Corresponding Author Info
Prof. Hong, Seung Ho (전자공학부 홍승호 교수)
Professor
E-mail
이메일 shhong@hanyang.ac.kr
Document Type
Source
APPLIED ENERGY 2019, 236, 937-949
Times Cited
Excluded
External Information
https://doi.org/10.1016/j.apenergy.2018.12.061
Abstract
[History]
- 5월/6월 2020부로, 이 인용 빈도가 높은 논문의 인용 횟수가 분야와 출판 연도에 대해 인용 빈도가 높은 임계값을 기반으로 Engineering 관련 학술 분야에서 상위 1%에 올랐습니다.

[Abstract]
Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch between energy supply and demand would increase costs to both the service provider and customers and may even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid reliability. In particular, to overcome the future uncertainties, deep neural network is used to predict the unknown prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive rates for different customers considering the profits of both service provider and customers. Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitabilities, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service provider and customers.
Web of Science Categories
Energy & Fuels; Engineering, Chemical
Funding
Language
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[Excluded] Incentive-based demand response for smart grid with reinforcement learning and deep neural network
  • Author(s)
    Lu, Renzhi; Hong, Hong, Seung Ho;
  • Source
    APPLIED ENERGY 2019, 236, 937-949
  • Time Cited
    Excluded