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This article presents an approach for the participation of datacenters and supercomputing facilities in smart electricity markets. This is a relevant problem in modern smart grid systems to implement demand response strategies for a better use of resources to guarantee energy efficiency. The proposed approach includes a datacenter model based on empirical information to determine the power consumption of CPU-intensive and memory-intensive tasks. A negotiation approach between the datacenter and its tenants and a heuristic planning method for energy reduction optimization are proposed. The experimental evaluation is performed over realistic problem instances modeling the operation of the National Supercomputing Center in Uruguay. The obtained results indicate that the proposed approach is effective to provide appropriate demand response actions according to monetary incentives. Accurate results are reported for realistic problem instances and different types of tenants.

In this article, we studied a negotiation approach for the participation of datacenters and super-computing facilities in smart electricity markets providing ancillary services, an important problem in modern smart grid systems. Different demand response strategy were studied for colocation datacenters to commit power reductions during a sustained period, according to offers proposed to tenants. The negotiation algorithm and a heuristic planning method for energy reduction optimization were experimentally validated over realistic problem instances that model different problem dimensions and flexibility of the datacenter clients. The obtained results indicate that the proposed approach is effective to provide appropriate frequency reserves control according to monetary incentives.

In this chapter, a multi-layer decentralized model predictive control (ML-DMPC) approach is proposed and designed for its application to large-scale networked systems (LSNS). This approach is based on the periodic nature of the system disturbance and the availability of both static and dynamic models of the LSNS. Hence, the topology of the controller is structured in two layers. First, an upper layer is in charge of achieving the global objectives from a set O of control objectives given for the LSNS. This layer works with a sampling time Δt1, corresponding to the disturbances period. Second, a lower layer, with a sampling time Δt2, Δt1 > Δt2, is in charge of computing the references for the system actuators in order to satisfy the local objectives from the set of control objectives O. A system partitioning allows to establish a hierarchical flow of information between a set C of controllers designed based on model predictive control (MPC). Therefore, the whole proposed ML-DMPC strategy results in a centralized optimization problem for considering the global control objectives, followed of a decentralized scheme for reaching the local control objectives. The proposed approach is applied to a real case study: the water transport network of Barcelona (Spain). Results obtained with selected simulation scenarios show the effectiveness of the proposed ML-DMPC strategy in terms of system modularity, reduced computational burden and, at the same time, the admissible loss of performance with respect to a centralized MPC (CMPC) strategy. © Springer Science+Business Media Dordrecht 2014., This work has been partially funded by the Spanish Ministry of Science and Technology through the WATMAN project (DPI2009-13744).

In this paper, a new active fault tolerant control (FTC) strategy for linear parameter varying (LPV) systems is proposed. The key contribution of the proposed approach is an integrated FTC design procedure of the fault identification and fault-tolerant control schemes using LPV techniques. Fault identification is based on the use of an observer. Once the fault have been identified, the FTC controller is implemented as a state feedback controller. This controller is designed such that it can stabilize the faulty plant using LPV techniques and linear matrix inequalities (LMIs). To assess the performance of the proposed approach a two degree of freedom helicopter is used.