Demand response for data centers in deregulated markets: A matching game approach

Abstract

With the fast development of deregulated electricity markets, a user can enter a contract with a utility company that offers the best rates among multiple competing utility companies. Meanwhile, a utility company is motivated to increase its market share by offering demand response programs with real-time pricing (RTP), which can help its customers to manage their energy usage and save money. In this paper, we focus on the demand response program in deregulated markets for data centers, which are often flexible in scheduling their workloads. We capture the stochastic workload process in a data center as a multiclass queuing system. We model the coupled decisions of utility company choices and workload scheduling of data centers as a many-to-one matching game with externalities. Analyzing such a game is challenging, as there does not exist a general algorithm that guarantees to find a stable outcome, where no player has an incentive to unilaterally change its strategy. We show that the data center matching game admits an exact potential function, whose local minima correspond to the stable outcomes of the game. We develop an algorithm that can guarantee to converge to a stable outcome. Compared with the scenario without utility company choices and demand response, simulations show that our proposed algorithm can reduce the cost of data centers by 15.4% and increase the revenue of those utility companies with lower tariffs by up to 82%. The peak-to-average ratio (PAR) of the customers' load demand is also reduced by 7.2%.