Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility

Author:

Charbonnier FloraORCID,Peng BeiORCID,Vienne JulieORCID,Stai Eleni,Morstyn Thomas,McCulloch MalcolmORCID

Funder

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Reference96 articles.

1. Household electricity demand, the intrinsic flexibility index and UK wholesale electricity market prices;Torriti;Environ Econ Policy Stud,2022

2. O’Neill D, Levorato M, Goldsmith A, Mitra U. Residential Demand Response Using Reinforcement Learning. In: 2010 first IEEE international conference on smart grid communications. 2010, p. 409–14.

3. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review;A;Renew Sustain Energy Rev,2020

4. Demand response and smart technology in theory and practice: Customer experiences and system actors;Darby;Energy Policy,2020

5. Indirect customer-to-customer energy trading with reinforcement learning;Chen;IEEE Trans Smart Grid,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3