4.8 Article

Intraday Residential Demand Response Scheme Based on Peer-to-Peer Energy Trading

期刊

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 3, 页码 1823-1835

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2929498

关键词

Load management; Peer-to-peer computing; Home appliances; Biological system modeling; Schedules; Energy resources; Demand response; distributed energy resources; electricity market; intraday market; peer-to-peer energy trading

资金

  1. Postdoctoral Innovation Talent Support Program of China [BX201700211]
  2. National Key Research and Development Program of China [2017YFB0903000]

向作者/读者索取更多资源

The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this article, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double-auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.

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