Risk-based stochastic day-ahead operation for data centre virtual power plants

Data centres (DCs) have rapidly expanded in recent years due to increasing demand for cloud services. The costs incurred by electricity and water consumption contribute to a major portion of DC operating costs. This article addresses the day-ahead operation of a DC in the context of a virtual power plant (VPP) under the grid-connected mode. The day-ahead operation problem is formulated as a two-stage stochastic mixed integer linear programming (MILP) model that considers workload schedules among server clusters, water consumption, and uncertainties of onsite renewable energy and electricity prices. In addition, the conditional value at risk (CVaR) is utilised to manage the risk caused by various uncertainties that challenge DCs, especially electricity prices. The impacts of the risk level, the water price, and uncertainty on the day-ahead operation of the DC are studied. The numerical test results show that the proposed model can efficiently generate optimal day-ahead schedules.