DC4Cities power planning: sensitivity to renewable energy forecasting errors

Abstract

Data centers are among the largest and fastest growing consumers of electricity in the world. Furthermore, the rapid growth of digital content, big data, e-commerce, and internet traffic will create the need for an even higher number of DCs. On other side, in spite of the variability of renewable resources, due to characteristic weather fluctuations, the significant progress has been made in the renewable energy generation industry in terms of reducing installation cost and increasing integration into the power grid represents a good motive to tune data center software execution load in such a way that power consumption matches renewable energy availability (about 5%--30% of total DC load can be shifted [20]). This is especially viable in the context of smart cities, where the existence of a demand side management scheme can be assumed. In the context of the European project "DC4Cities", a similar scheme has been developed which consists of two phases. In the first phase, a concrete guidelines on power use for participating consumers to be followed is calculated. In the second phase, the control systems should find using this guidelines the best desired power values in terms of renewable percentage and SLAs. In this paper, an algorithm to calculate the aforementioned concrete guidelines by a component named "Max/Ideal Power Planner", based on smart city goals and renewable power availability forecasts, is proposed. In addition, the robustness of complete control system, particularly the Max/Ideal Power Planner, is estimated by evaluating the impact of renewable forecast accuracy on the scheduling of jobs in the data center via the proposed control system. Two types of errors in renewable forecasting are discussed: constant error and random error.