Abstract
Demand Response Programs (DRPs) in Smart Grid (SG) are designed to encourage consumers to shift their loads to regions and hours with less load stress by alternating the price of electricity. On the other hand, with the emergence of cloud computing, the demand for cloud data centres increases resulting in high energy costs. In this paper, we address the problem of cloud data centre demand response in an SG area in presence of utility companies which compete to attract more customers. We formulate two optimization problems for cloud and data centres to minimise their costs by changing their energy demand. These problems are shown to be convex and can be readily solved by standard convex optimization techniques. We also propose an algorithm for cloud and data centres to participate in DRPs by concurrently performing regional and temporal workload management. The uncertainty of renewable energy generation in data centres is treated by training a multilayer perceptron to predict the generated energy. Numerical results show that the proposed algorithm outperforms other existing algorithms in terms of the energy cost. In addition, our algorithm flattens the energy demand profile of utility companies and balances the electric load across different locations.
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