Optimal Pricing Strategy for Data Center Considering Demand Response and Renewable Energy Source Accommodation

Abstract

With the continuous development of information technology, data centers (DCs) consume significant and evergrowing amounts of electrical energy. Renewable energy sources (RESs) can act as clean solutions to meet this requirement without polluting the environment. Each DC serves numerous users for their data service demands, which are regarded as flexible loads. In this paper, the willingness to pay and time sensitivities of DC users are firstly explored, and the user-side demand response is then devised to improve the overall benefits of DC operation. Then, a Stackelberg game between a DC and its users is proposed. The upper-level model aims to maximize the profit of the DC, in which the time-varying pricing of data services is optimized, and the lower-level model addresses user' s optimal decisions for using data services while balancing their time and cost requirements. The original bi-level optimization problem is then transformed into a single-level problem using the Karush-Kuhn-Tucker optimality conditions and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are conducted to demonstrate the feasibility and effectiveness of the proposed method, as well as the effects of the time-varying data service price mechanism on the RES accommodation.

Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

“…As a result, the remaining IWs such as online trading and web browsing workloads, make up a significant portion of the demand-side resources in developed regions. Despite their flexibility, most existing studies only focus on minimizing energy costs for data centers, neglecting their potential contributions to supporting grid voltages and managing congestion [16], which would adversely affect power system security [17].…”

“…The electric power load (EL) of these DCBs consists of IT energy consumption, e.g., servers, memory, communication, and storage devices, as well as other ancillary energy consumption, e.g., air conditioning, lighting. Power usage efficiency (PUE) is generally used to illustrate the relationship between IT energy consumption and ancillary energy consumption, which is defined as the ratio of total energy consumption to IT energy consumption [16]. In addition, to satisfy the reactive power loads of DCBs, the SVG and reactive capacity of PV are considered at the upper level.…”

Analyzing Power Decisions in Data Center Powered by Renewable Sources

“…Several works pursue ways to deal with RES uncertainties. However, they introduce, in many cases, grid (brown) connections or just one level of management (online or offline) [10], [11], [12], [13], [14], [15]. In work [14], the authors created an offline optimization framework using a model to capture the randomness of the RES.…”

“…In work [14], the authors created an offline optimization framework using a model to capture the randomness of the RES. The authors in [10] propose an offline mechanism to vary the price according to the grid and RES price. They modeled a Stackelberg game to stimulate users to join the Demand-Response's load shift.…”

Mixing Offline and Online Electrical Decisions in Data Centers Powered by Renewable Sources

“…Several works seek ways to deal with RES uncertainties but, in many cases, with grid (brown) connections or just one level of management (online or offline) [8], [9], [10], [11], [12], [13]. The authors in [8] propose an offline data service price mechanism to vary the price according to the grid and RES price. They implemented a Stackelberg game to stimulate users to participate in the Demand-Response's load shift.…”