A coordinated scheduling optimization method for integrated energy systems with data centres based on deep reinforcement learning

Abstract

As an emerging multi-energy consumption subject, data centres (DCs) are bound to become crucial energy users for integrated energy systems (IES). Therefore, how to fully tap the potential of the collaborative operation between DCs and IES to improve total energy efficiency and economic performance is becoming a pressing need. In this article, the authors research an optimization coordinated by the energy scheduling and information service provision within the scenario of an integrated energy system with a data centre (IES-DC). The mathematical model of IES-DC is first established to reveal the energy conversion process of the electricity-heat-gas IES and the DC's energy consumption affected by the scale of active IT equipment. For dynamical providing multi-energy and computing service by coordinating scheduling energy and information equipment, the formulations of IES-DC scheduling, which is described as a Markov decision process (MDP), are presented, and it is solved by introducing the twin-delayed deep deterministic policy gradient (TD3), which is a model-free deep reinforcement learning (DRL) algorithm. Finally, the numerical studies show that compared with benchmarks, the proposed method based on the TD3 algorithm can effectively control the operation of energy conversion equipment and the number of active servers in IES-DC.