Alessandro Di Giorgio

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.

An event driven Smart Home Controller enabling cost effective use of electric energy and automated Demand Side Management

This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as a binary linear programming problem, the output of which specifies the best time to run of Smart Household Appliances, under a Virtual Power Threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here optimization of economic saving in case of multi-tariff contract and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manifacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.

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.

Resilient planning of PowerLine Communications networks over Medium Voltage distribution grids

In this paper a network planning problem aiming to enable underground Medium Voltage (MV) power grids to resilient PowerLine Communications (PLCs) is faced. The PLC network is used to connect PLC End Nodes (ENs) located into the secondary substations to the energy management system of the utility by means of PLC network nodes enabled as Access Points. An optimization problem is formulated, aiming to optimally allocate the Access Points to the substations and the repeaters to the MV feeders. A multi-objective optimization approach is used, in order to keep in balance the needs of minimizing the cost of equipment allocation and maximizing the reliability of PLC network paths. Resiliency and capacity constraints are properly modeled, in order to guarantee the communications even under faulted link conditions. As a byproduct, the optimization algorithm also returns the optimal routing. Simulations performed on a realistic underground MV distribution grid validate the proposed approach.

Optimal fully electric vehicle load balancing with an ADMM algorithm in Smartgrids

In this paper we present a system architecture and a suitable control methodology for the load balancing of Fully Electric Vehicles at Charging Station (CS). Within the proposed architecture, control methodologies allow to adapt Distributed Energy Resources (DER) generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at local level a Load Area Controller (LAC) optimally calculates the FEVs charging sessions, while at higher level a Macro Load Area Aggregator (MLAA) provides DER with energy production profiles, and LACs with energy withdrawal profiles. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed algorithm.

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.

A feedback linearization based Wind turbine control system for ancillary services and standard steady state operation

In this paper the control problem of the wind turbine driven doubly fed induction generator (DFIG) is faced, both in wind park operator and system operator perspective. Two control schemes are proposed, based on feedback linearization theory for MIMO systems and PI controllers: the first one for simultaneous active and reactive power regulation, the second one for simultaneous propeller angular speed and reactive power regulation. They are shown to allow the deliver of ancillary services to the system operator and the maximization of wind park operator profitability respectively.

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.