Joint Optimal Scheduling of Power Grid and Internet Data Centers Considering Time-of-Use Electricity Price and Adjustable Tasks for Renewable Power Integration

Dengshan Hou, Li Wang, Yanru Ma, Longbiao Lyu, Weijie Liu and Shenghu Li ()
Additional contact information
Dengshan Hou: Institute of Economy and Technology, State Grid Anhui Electric Power Company, Hefei 230022, China
Li Wang: Institute of Economy and Technology, State Grid Anhui Electric Power Company, Hefei 230022, China
Yanru Ma: Institute of Economy and Technology, State Grid Anhui Electric Power Company, Hefei 230022, China
Longbiao Lyu: Institute of Economy and Technology, State Grid Anhui Electric Power Company, Hefei 230022, China
Weijie Liu: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Shenghu Li: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Sustainability, 2025, vol. 17, issue 8, 1-20

Abstract: The internet data center (IDC) has experienced rapid growth recently. Computing power tasks have the characteristic of flexible adjustment and can participate in demand-side response; thus, they are suitable for balancing stochastic wind and solar power. Existing studies lack research on joint optimization between the IDC and power grid. This paper proposes a joint optimization scheduling approach for IDC and power systems, focusing on the response of computing tasks. Based on the adjustment characteristics of computing tasks, tasks are categorized, and operational constraints for each category are defined. The bi-level optimization model for the IDC and power grid is established, taking into account the task constraints, as well as the operational limits of power generation units and the IDC. A novel elasticity coefficient matrix for time-of-use (TOU) electricity pricing is proposed, considering the load characteristics of IDC tasks. The IDC’s demand response volume is determined using the elasticity coefficient matrix. The enhanced Benders decomposition method is then employed, incorporating the IDC’s demand response capacity and the constraints of the bi-level optimization model, to solve the optimal planning problem. To achieve scenario reduction, the K-means algorithm is utilized to derive the typical daily load profiles of the IDC. The simulation results validate the effectiveness and accuracy of the proposed method and show that the approach effectively reduces the operational costs of the IDC power system and enhances the sustainable integration of renewable energy.

Keywords: enhanced benders decomposition; internet data center; time-of-use prices; bi-level model; demand response; elasticity coefficient matrix; renewable energy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/8/3374/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/8/3374/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:8:p:3374-:d:1631733

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().