Key points are not available for this paper at this time.
The emergence of cloud computing has established a trend toward building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have a major impact on the electric power grid by significantly increasing the load at locations where they are built. Dynamic energy pricing policies in the recently proposed smart power grid technology can incentivize the cloud controller to shift the computation load toward data centers in regions with cheaper electricity or with excessive electricity generated by renewable energy sources, e.g., photovoltaic (PV) and wind power. On the other hand, distributed data centers in the cloud also provide opportunities to help the power grid with distributed renewable energy sources to improve robustness and load balancing. To shed some light into these opportunities, this paper considers an interaction system of the smart power grid with distributed PV power generation and the cloud computing system, jointly accounting for the service request dispatch and routing problem in the cloud with the power flow analysis in power grid. The Stackelberg (sequential) game formulation is provided for the interaction system under two different dynamic pricing scenarios: 1) real-time power-dependent pricing; and 2) time-ahead pricing. The two players in the Stackelberg games are the power grid controller that sets the pricing signal and the cloud controller that performs resource allocation among data centers. The objective of the power grid controller is to maximize its own profit and perform load balancing among power buses, i.e., minimizing the power flow from one power bus to the others, whereas the objective of the cloud computing controller is to maximize its own profit with respect to the location-dependent pricing signal. Based on the backward induction method, this paper derives the near-optimal or suboptimal strategies of the two players in Stackelberg game using convex optimization and simulated annealing techniques.
Wang et al. (Thu,) studied this question.