Francesco Liberati

A model predictive control approach to the load shifting problem in a household equipped with an energy storage unit

This paper deals with the load shifting problem in a household equipped with smart appliances and an energy storage unit with conversion losses. The problem is faced by establishing an event driven Model Predictive Control framework aiming to meet the real life dynamics of a household and to keep low the impact of the control system on the total electric energy consumption. The proposed approach allows the consumer to minimize the daily energy cost in scenarios characterized by Time of Use tariffs and Demand Side Management, by dynamically evaluating the best time to run of the appliances and the optimal evolution of the battery level of charge. A proper set of realistic simulations validates the proposed approach, showing the relevance of the energy storage unit in the domestic load shifting architecture.

On-board stochastic control of Electric Vehicle recharging

This paper deals with the design of an on-board control strategy for Electric Vehicle recharging under the hypothesis of missing knowledge of the future energy price and the presence of vehicle to grid capability. For this purpose the charging session is modeled as a finite horizon Markov Decision Process and the optimal charging policy is computed according to Reinforcement Learning techniques, the learning phase makes use of the revenues received when taking actions in states represented by the current level of charge, the leftover charging time and the last realization of energy price. Simulation results show the effectiveness of the proposed approach with respect to the fulfillment of driver preferences in charging and the diversification of the control action during charging for the exploitation of the vehicle to grid concept.

Interdependency modeling and analysis of critical infrastructures based on Dynamic Bayesian Networks

This paper presents a novel approach to the critical infrastructure (CI) interdependencies analysis, based on the Dynamic Bayesian Network (DBN) formalism. Our original modeling procedure divides the DBN in three levels: an atomic events level, which models the adverse events impacting on the analyzed CIs, a propagation level, which captures CI interdependencies, and a services level, which allows to monitor the state of provided services. Three types of analyses can be performed: a reliability study, an adverse events propagation study, and a failure identification analysis. A case study provided by Israel Electric Corporation is considered, and explicative simulations are presented and discussed in detail.

Electric Vehicles charging control based on future internet generic enablers

In this paper a rationale for the deployment of Future Internet based applications in the field of Electric Vehicles (EVs) smart charging is presented. The focus is on the Connected Device Interface (CDI) Generic Enabler (GE) and the Network Information and Controller (NetIC) GE, which are recognized to have a potential impact on the charging control problem and the configuration of communications networks within reconfigurable clusters of charging points. The CDI GE can be used for capturing the driver feedback in terms of Quality of Experience (QoE) in those situations where the charging power is abruptly limited as a consequence of short term grid needs, like the shedding action asked by the Transmission System Operator to the Distribution System Operator aimed at clearing networks contingencies due to the loss of a transmission line or large wind power fluctuations. The NetIC GE can be used when a master Electric Vehicle Supply Equipment (EVSE) hosts the Load Area Controller, responsible for managing simultaneous charging sessions within a given Load Area (LA); the reconfiguration of distribution grid topology results in shift of EVSEs among LAs, then reallocation of slave EVSEs is needed. Involved actors, equipment, communications and processes are identified through the standardized framework provided by the Smart Grid Architecture Model (SGAM).

Optimal electric vehicles to grid power control for active demand services in distribution grids

In this paper we outline a novel approach for the design of an electric vehicle (EV) aggregator, a controller whose objective is to optimally manage the charging operations of an EV fleet. The control strategy we derive is based on model predictive control and allows to achieve costs minimization, also enabling the aggregator (hence, the EV fleet) to participate to the provisioning of active demand services to upper level market players. Explicative simulations are presented and discussed in order to show the effectiveness of the approach and also to investigate the role of vehicle to grid power.

Electric vehicles charging load reprofiling

This paper presents a reference architecture and a control scheme for the aggregation and management of electric vehicle (EV) load at medium voltage level. The focus is put on the problem of EV load reprofiling, aimed at the procurement of active demand (AD) services to interested grid/market actors. The proposed approach achieves AD product composition always guaranteeing the respect of grid constraints as well as user constraints on the charging processes. Simulations are presented to illustrate the effectiveness of the proposed approach.

TeNOR: Steps towards an orchestration platform for multi-PoP NFV deployment

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.

Model Predictive Control of Energy Storage Systems for Power Tracking and Shaving in Distribution Grids

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.

A proactive link-failure resilient routing protocol for MANETs based on reinforcement learning

Mobile-Ad-Hoc-Networks (MANET) are self-configuring networks of mobile nodes, which communicate through wireless links. One of the main issues in MANETs is the mobility of the network nodes: routing protocols should explicitly consider network changes into the algorithm design. MANETs are particularly suited to guarantee connectivity in disaster relief scenarios, which are often impaired by the absence of network infrastructures. This work proposes a proactive routing protocol, developed via Reinforcement Learning (RL) techniques, to dynamically choose the most stable path, basing on GPS information, among the feasible ones and to consequently increase resiliency to link failures. Simulations show the effectiveness of the proposed protocol, through comparison with the Optimized Link State Routing (OLSR) protocol.

Electric energy storage systems integration in distribution grids

This paper presents a real time control strategy for dynamically balancing electric demand and supply at local level, in a scenario characterized by a HV/MV substation with the presence of renewable energy sources in the form of photovoltaic generators and an electric energy storage system. The substation is connected to the grid and is powered by an equivalent traditional power plant playing the role of the bulk power system. A Model Predictive Control based approach is proposed, by which the active power setpoints for the traditional power plant and the storage are continually updated over the time, depending on generation costs, storages state of charge, foreseen demand and production from renewables. The proposed approach is validated on a simulation basis, showing its effectiveness in managing fluctuations of network demand and photovoltaic generation in test and real conditions.