A Stackelberg Game for Co-Optimization of Distribution System Operator Revenue and Virtual Power Plant Costs with Integrated Data Center Flexibility

Qi Li, Shihao Liu, Bokang Zou, Yulong Jin, Yi Ge, Yan Li, Qirui Chen (), Xinye Du, Feng Li and Chenyi Zheng
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Qi Li: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Shihao Liu: School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Bokang Zou: NARI Technology Co., Ltd., Nanjing 211106, China
Yulong Jin: NARI Technology Co., Ltd., Nanjing 211106, China
Yi Ge: State Grid Jiangsu Economic Research Institute, Nanjing 210000, China
Yan Li: State Grid Jiangsu Economic Research Institute, Nanjing 210000, China
Qirui Chen: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Xinye Du: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Feng Li: School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Chenyi Zheng: College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China

Energies, 2025, vol. 18, issue 15, 1-18

Abstract: The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency.

Keywords: distributed renewable energy; virtual power plant (VPP); data center; dynamic pricing; flexible load scheduling; distributed renewable energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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