Non-cooperative game-based electricity pricing method for data centers

Abstract

Data centers are characterized by high energy consumption, with operating costs being extremely sensitive to electricity prices. Therefore, modern data centers are often equipped with microgrids for power supply, which adjust their operational strategies based on the given electricity prices to minimize costs. However, existing research has overlooked the flexible pricing potential of electricity retailers, prompting this study to propose a non-cooperative game theory-based optimization method for data center electricity procurement negotiation. A two-layer optimization model is established for data center electricity pricing. The upper layer focuses on electricity price optimization, modeling price negotiation as a Stackelberg game and adopting a weighted average cost approach to describe the electricity procurement prices. The lower layer addresses data center operational optimization, formulated as a nonlinear programming problem. To enable rapid solution convergence, a hybrid problem-solving method combining a genetic algorithm and branch-and-cut algorithm is proposed. Finally, a simulation is conducted using a data center located in California, United States, to validate the proposed method. The results demonstrate that the proposed method can reduce data center operational costs by 3% and increase the revenue for electricity retailers by 17%, achieving a win–win outcome.