Andrea Mercurio

Optimal Planning and Routing in Medium Voltage PowerLine Communications Networks

This paper deals with the problem of deploying a PowerLine Communication (PLC) network over a medium voltage (MV) power grid. The PLC network is used to connect the end nodes (ENs) of the MV grid to the service provider by means of PLC network nodes enabled as access points. In particular, a network planning problem is faced wherein we require to define the PLC network topology by deciding which MV network nodes are to be enabled as access points. An optimization problem is then formulated, which minimizes the cost of enabling the access points and maximizes the reliability of PLC network paths in a multi-objective optimization fashion. This work also considers resiliency (i.e., it guarantees the PLC network connectivity even in case of link faults) and capacity constraints (i.e., it checks that there are enough resources to transmit the estimated amount of traffic over the PLC network paths). As a byproduct, the optimization algorithm also returns the optimal routing. Simulations based on realistic MV network topologies validate the proposed approach.

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.

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.

Distributed control approach for Community Energy Management Systems

In this paper we present a system architecture and suitable control methodologies for the management and control of Distributed Generation (DG) units, Renewable Energy Resources (RES) and Active Demand (AD). Within the proposed platform, control methodologies allow to adapt unit generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at residential level a Smart Home Controller (SHC) monitors and controls smart appliances while at higher level a Community Energy Management System (CEMS) coordinates generation units, set of SHCs and power grid energy withdrawals. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed algorithm.

Distributed control approach for community energy management systems in presence of storage

In this paper we present a system architecture and suitable control methodologies for the management and control of Distributed Generation (DG) units, Renewable Energy Resources (RES), Active Demand (AD) and storage units, being these Electric Vehicles (EV) or Uninterruptible Power Supply (UPS). Within the proposed platform, control methodologies allow to adapt unit generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at residential level a Smart Home Controller (SHC) monitors and controls smart appliances while at higher level a Community Energy Management System (CEMS) coordinates generation units, negotiates consumption with SHCs and sets power grid energy withdrawals. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed solution of a dynamic programming problem.

Local energy management system: Control scheme and loads modeling

In this work we present a general control scheme for the management of an energy community and a load modeling approach. We adopt a multilevel control scheme in which the first level computes the energy consumption set point towards the second level and the generation planning towards the energy sources, the second level is in charge of local loads scheduling and micro-generation planning.

Regulation of angular speed and reactive power for a wind turbine applying robust feedback linearization and H ∞ control

This paper deals with the robust regulation of reactive power and rotor angular speed in a wind turbine driven Doubly Fed Induction Generator, which constitutes one of the key functionalities for the implementation of the future Smart Grids. The focus of the work is the application of a recent development in the theory of robust control of nonlinear systems, which combines robust feedback linearization and H∞ linear control. The derived robust control system is compared to a traditional one making use of classical feedback linearization and PI controllers. Simulations show the effectiveness of the new approach in extending the performances of the classical feedback linearization based regulator from nominal parameters condition to the perturbed one.

Generation Portfolio Optimization by NPV formulation, Monte Carlo Estimators and Genetic Algorithms

In this paper we present the Portfolio Optimization Problem in the electricity generation framework. We consider traditional and fully controllable energy sources together with wind source, strongly supported by economical benefits but exposed to intermittent generation volatility. A new formulation in terms of uNPV (unit Net Present Value) is proposed and analysed, due to the statistical uncertainty about parameters, we formalize the optimization problem in a probabilistic sense in terms of Monte Carlo Estimators and structured in terms of Risk Aversion factor. The optimization routine is implemented with a Genetic Algorithm.