4.6 Article

Data Center Aggregators' Optimal Bidding and Benefit Allocation Strategy Considering the Spatiotemporal Transfer Characteristics

Journal

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Volume 57, Issue 5, Pages 4486-4499

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2021.3090342

Keywords

Data centers; Batteries; Generators; Power systems; Companies; State of charge; Spatiotemporal phenomena; Benefit allocation; data center aggregators (DCAs); demand response (DR); optimal bidding; Shapley value

Funding

  1. National Key R&D Program of China [2018YFE0122200]
  2. Major Science and Technology Achievements Conversion Project of Hebei Province [19012112Z]
  3. Science and Technology Project of State Grid Hebei Electric Power, Co., Ltd. [SGHE0000DKWT2000464]

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Aggregators, such as data center aggregators, are emerging entities in the electricity market that gather small flexible resources and optimize bidding strategies for participation in demand response programs, benefiting both the aggregators and the data centers.
The aggregator, an emerging entity in the electricity market, gathers and creates market power for the small flexible resources, which traditionally contain distributed generation, electric storage, and flexible load. Recently, the exploding growth of information technology demand gives rise to the data centers, whose participation in demand response (DR) is becoming increasingly significant for its remarkable performance in spatiotemporal transfer characteristics compared to conventional facilities. The high-quality DR potential of data centers provides a new opportunity for the profit-seeking data center aggregators (DCAs) to optimize their bidding plans in the day-ahead electricity market. Therefore, this article targets the formulation of the optimal bidding strategy of demand response aggregators (DRAs) to achieve the maximization of the joint benefit of both DCA and data centers. A spatiotemporal transfer characteristics-based optimization strategy is formulated to obtain the electricity purchasing scheme and arrangement of data centers. Meanwhile, the Shapley value method is introduced to design the appropriate and equitable DR reward mechanism that each DCA should compensate its customers, which would serve as a guarantee to ensure data centers' active participation in DR events and lay a solid foundation for the implementation of the DCA's optimal bidding strategy. Numerical tested results demonstrate the effectiveness of the proposed optimal bidding model and the benefit allocation mechanism, which profit both DRA and data centers when participating in DR programs while preserving their daily power demand.

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