IEEE Access (Jan 2021)

Optimal Energy Scheduling for Data Center With Energy Nets Including CCHP and Demand Response

  • Dongxiao Wang,
  • Changhong Xie,
  • Runji Wu,
  • Chun Sing Lai,
  • Xuecong Li,
  • Zhuoli Zhao,
  • Xueqing Wu,
  • Yi Xu,
  • Loi Lei Lai,
  • Jinxiao Wei

DOI
https://doi.org/10.1109/ACCESS.2020.3049066
Journal volume & issue
Vol. 9
pp. 6137 – 6151

Abstract

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Internet data centers are growing rapidly in recent years and they operate with intensive energy activity. Combined cooling, heating and power (CCHP) brings new opportunities for reducing the electricity cost in internet data centers. The main objective of this study is to optimize the energy resources scheduling in the data center coupled energy nets considering the involvement of CCHP and different demand response techniques. In this paper, internet data center coupled energy nets are proposed, where power grid, solar photovoltaic, CCHP, and battery energy storage systems are the primary energy sources. The adjunct residential buildings and commercial buildings near the internet data centers are also included in the proposed energy nets, where different types of load and demand response characteristics are utilized. A two-stage optimized energy management model considering the coordinated operation of CCHP and demand response technologies is established for internet data center coupled energy nets. In the day-ahead stage, the control objective is to minimize system cost while satisfying various constraints. Consider the electricity tariff chance between day-ahead market and real-time market, real-time control is implemented to minimize the imbalance cost between two electricity markets. Case studies are conducted on a practical internet data center coupled energy nets in Foshan City, China. It is observed that the proposed control framework can optimally schedule the energy resources in the energy network to meet system demand and improve the energy efficiency. The economic evaluation demonstrates that the proposed control scheme reduces system daily cost by 22.01%.

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