Journal of Modern Power Systems and Clean Energy

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Home > Archive>Volume 11, Issue 1, 2023 >345-354. DOI:10.35833/MPCE.2021.000130
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Optimal Pricing Strategy for Data Center Considering Demand Response and Renewable Energy Source Accommodation
Author:
Affiliation:

1.School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2.Economic and Technology Research Institute, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310000, China
3.UNSW Business School, The University of New South Wales, Sydney, Australia
4.Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Estonia
5.Jiaxing Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Jiaxing 314000, China

Fund Project:

This work was supported in part by National Natural Science Foundation of China (No. U1910216) and in part by Science and Technology Project of State Grid Zhejiang Electric Power Co., Ltd. (No. 5211JY19000T).

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    Abstract:

    With the continuous development of information technology, data centers (DCs) consume significant and ever-growing amounts of electrical energy. Renewable energy sources (RESs) can act as clean solutions to meet this requirement without polluting the environment. Each DC serves numerous users for their data service demands, which are regarded as flexible loads. In this paper, the willingness to pay and time sensitivities of DC users are firstly explored, and the user-side demand response is then devised to improve the overall benefits of DC operation. Then, a Stackelberg game between a DC and its users is proposed. The upper-level model aims to maximize the profit of the DC, in which the time-varying pricing of data services is optimized, and the lower-level model addresses user ’s optimal decisions for using data services while balancing their time and cost requirements. The original bi-level optimization problem is then transformed into a single-level problem using the Karush-Kuhn-Tucker optimality conditions and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are conducted to demonstrate the feasibility and effectiveness of the proposed method, as well as the effects of the time-varying data service price mechanism on the RES accommodation.

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History
  • Received:February 19,2021
  • Revised:July 13,2021
  • Online: January 28,2023

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