In this paper, a model predictive control (MPC) strategy is proposed to control the energy flows in a distribution network node (e.g., a distribution substation) equipped with an electric storage system (ESS) and serving a portion of the grid with high penetration of renewable energy sources (RES). The aim is to make the power flow at node level more controllable in spite of the presence of fluctuating distributed energy resources. In particular, the proposed control strategy is such that the controlled power flow at node level tracks the profile established on a day-ahead basis for efficient operation of the grid. That is achieved by letting the MPC controller decide the current storage power setpoint based on the forecasts of the demand and of the RES output. Theoretical results are reported on the stability of the proposed control scheme in a simplified setting foreseeing zero forecasting error. The performance of the system in the general case is then evaluated on a simulation basis. Simulations show the effectiveness in managing RES fluctuations in realistic settings.

One of the primary challenges associated with network functions virtualization (NFV) is the automated management of the service lifecycle. In this paper, we present a full software-based management and orchestration (MANO) stack which operates with OpenStack and OpenDaylight controllers and has the in-built functionality to automate the key phases of the NFV service lifecycle, namely resource discovery and matching, service mapping, service deployment, and monitoring. The MANO stack is being implemented by the EU FP7 project T-NOVA, with the components being released as open-source software. Service mapping and service deployment solutions developed in the scope of T-NOVA are presented in detail. As a proof-of-concept, we evaluate the performance of a virtualized traffic classifier network function, demonstrating the gains of virtualized hardware acceleration.

This paper deals with the problem of synchronizing the outputs of a set of nonlinear agents that exchange information through a time-varying communication network in a leader-follower configuration. The dynamics of the individual followers may differ from each other and from that of the leader. The information exchange between the leader and (a limited fraction of) the followers as well as between neighboring followers only consists of the relative values of the output variables that are to be synchronized. The theory of output regulation for nonlinear systems is used to design decentralized controllers embedding an internal model of the leader dynamics. Then, under mild connectivity hypotheses, it is shown how synchronization between the local control loops can be achieved.

Network Functions Visualization is focused on migrating traditional hardware-based network functions to software-based appliances running on standard high volume severs. There are a variety of challenges facing early adopters of Network Function Virtualizations; key among them are resource and service mapping, to support virtual network function orchestration. Service providers need efficient and effective mapping capabilities to optimally deploy network services. This paper describes TeNOR, a micro-service based network function virtualisation orchestrator capable of effectively addressing resource and network service mapping. The functional architecture and data models of TeNOR are described, as well as two proposed approaches to address the resource mapping problem. Key evaluation results are discussed and an assessment of the mapping approaches is performed in terms of the service acceptance ratio and scalability of the proposed approaches.

The new challenge in power systems design and operation is to organize and control smart micro grids supplying aggregation of users and special loads as electric vehicles charging stations. The presence of renewable and storage can help the optimal operation only if a good control manages all the elements of the grid. New models of green buildings and energy communities are proposed. For a real application they need an appropriate and advanced power system equipped with a building automation control system. This paper presents an economic model predictive control approach to the problem of managing in a coordinated way the electric and heating resources in a smart building, for the purpose of achieving in real time nearly zero energy consumption and automated participation to demand response programs. The proposed control, leveraging a mixed integer quadratic programming problem, allows to meet manifold thermal and electric users' requirements and react to inbound demand response signals, while still guaranteeing stable operation of the building's electric and thermal storage equipment. The simulation results, performed for a real case study in Italy, highlight the peculiarities of the proposed approach in the joint handling of electric and thermal building flexibility.