Affiliations: [a] Guangzhou Power Supply, GuangDong Power Grid Co., Ltd., Guangzhou, China | [b] State Key Lab of Control and Simulation of Power Systems and Generation Equipments, Department of Electrical Engineering, Tsinghua University, Haidian District Beijing, China | [c] WuXi University, Xishan District Wuxi, China | [d] La Consolacion University Philippines, Malolos, Philippines
Correspondence:
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Corresponding author: Hao Tian, La Consolacion University Philippines, Malolos 3000, Philippines. E-mail: [email protected].
Abstract: This paper examines the use of an Uninterruptible Power Supply (UPS) to enhance the operational efficiency of data centers. It focuses on developing an optimal energy scheduling strategy for a data center equipped with UPS, using the Markov decision process (MDP) framework. The MDP framework simulates the decision-making process involved in minimizing energy costs. Each unit’s available power output in the data center is treated as a Markov state, taking into account the uncertainty associated with renewable distributed generation. This uncertainty drives the system to transition to other Markov states in subsequent decision times. A recursive optimization model is established for each Markov state at each decision time to guide state-based operations, which includes determining the unit output while considering both current and future costs. The challenge of dealing with high dimensionality, arising from a substantial number of states and actions in the model, is effectively addressed by adopting an approximate dynamic programming (ADP) method. This approach incorporates decision-state and forwards dynamic algorithms to tackle the complexity of the MDP-based model. By employing ADP, the computational burden is reduced, enabling efficient and practical solutions to be obtained.
Keywords: Data center, Markov decision process, reinforcement learning, UPS
