Francesco Baccino

An Optimal Model-Based Control Technique to Improve Wind Farm Participation to Frequency Regulation

Summary form only given. This paper presents a model-based control technique to provide the contribution of wind power generators to primary frequency regulation in electric power systems. Models of individual wind power generators and wind farm (WF) as a whole are presented and the proposed control strategy is detailed. It consists of a central controller, a central Kalman filter (KF), and some local KFs, one for each wind turbine. The central controller is disabled in normal operation conditions and its task is to set the power reference for each wind turbine, overwriting the local reference, when a disturbance occurs. Central KF is in charge of estimating the external load variation, while each local KF estimates wind speed and the wind turbines dynamical state. The key feature of this approach is that each wind turbine can react to grid disturbances in a different way, which depends on wind speed as seen by the wind turbine itself and by its dynamical conditions. Real wind data and a large WF connected to the grid in a dedicated simulation environment have been used to test the effectiveness of the proposed control strategy.

Power and energy control strategies for a Vanadium Redox Flow Battery and wind farm combined system

The paper aims at describing two different control strategies for a combined system composed by a Vanadium Redox Flow Battery and a wind farm. A brief overview of the dynamic models used at describing the storage system and the wind turbines is presented. The focus is then devoted to the description of the two controllers, which task is to grant the desired power output at the point of connection of the system to the main network. The two control strategies called respectively Power Control and Energy Control are analyzed and their effectiveness is tested. The wind turbines are, in fact, fed with turbulent winds and the storage is controlled to perform a series of charges and discharges in order to have the desired global output. Their implementation and the dynamic simulations are performed in the Matlab-Simulink environment.

Frequency regulation by management of building cooling systems through Model Predictive Control

Frequency stability in power systems is a key driver for the maintenance of supply quality. Thermal loads, if properly managed, can play an important role in providing support to the frequency regulation. In this framework, the paper presents a control strategy to enable a load aggregator to manage a set of building cooling systems to contribute both to primary and secondary regulation. The proposed strategy uses the Model Predictive Control approach. Frequency support is provided without compromising the natural mission of the controlled loads, i.e., the end-user thermal comfort. The introduced method is tested by means of software-in-the-loop simulation studies. The implemented testing framework emulates real-time operation of a building aggregate within a benchmark network with high penetration of wind generation. Results show the ability of the control algorithm to optimally coordinate the contribution of thermal loads both to primary and secondary frequency regulation.

A Two-Stage Margin-Based Algorithm for Optimal Plug-in Electric Vehicles Scheduling

This paper proposes an optimal charging strategy for plug-in electric vehicles (PEVs) to be used in electric distribution networks. The optimization algorithm is made up of two phases: 1) an optimal power flow calculation; and 2) a linear optimization. The former, while taking into account the power system technical constraints, sets the upper bounds to the recharge power for each vehicle and the latter defines the recharge profiles of each PEV. In order to test the effectiveness of the optimization algorithm, a case study was set up. The connection of 300 PEVs to the Conseil International des Grands Réseaux Électriques (CIGRÉ) European low voltage benchmark network has been simulated. The proposed algorithm has been compared with a nonoptimal charging strategy, which assigns a flat charging profile by dividing the energy requested by the desired recharge time. The results show that the optimization algorithm both complies with the energy requests set by the end users and with the technical operation limits of the network. This allows for PEVs to provide a basic-although of paramount importance-service to the grid: the smart charge.

Model of a real medium voltage distribution network for analysis of distributed generation penetration in a SmartGrid scenario

The paper aims at simulating the behaviour of a real medium voltage electric network in order to analyse the effects of distributed generation penetration, in particular from solar source. Firstly the network model has been designed by the simulation tool DIgSILENT, using data obtained from the Distribution System Operator (DSO) to highlight, through subsequent checks, the likeliness between results from simulated scenarios and available measurements. Then static simulations were performed, with different scenarios of PV generation, in order to check the possibility to manage this generation. The behaviour of the network in compliance with the current national standards has been verified. Finally some dynamic simulations were performed in order to analyse transients due to typical operations of distribution systems.

Management strategy for unbalanced LV distribution network with electric vehicles, heat pumps and domestic photovoltaic penetration

In the last years several players in the electric energy industry have been promoting the all-electric concept in different contexts, for instance the all-electric ship, the all-electric city and the all-electric house. The end user electricity consumptions are likely to increase with the related risk of stressing the distribution infrastructures and reducing the quality of service. Some devices that can realize a significant shift toward the all-electric concept in the private users domain are reversible heat pumps (HPs) and plugin electric vehicles (PEVs). On the other hand, the severity of the increase in the electric load might be somehow compensated by the penetration of distributed generation (DG) from renewable energy sources (RES) such as domestic photovoltaic (PV). To take the best advantage of the flexibility of the available resources they have to be carefully coordinated. In this paper a two-steps algorithm for the optimal management of unbalanced low voltage (LV) distribution networks with significant penetration of HPs, PEVs and PVs is described and simulations are performed to test the algorithm operation.

Domestic heat load aggregation strategies for wind following in electric distribution systems

This paper investigates the operation of a domestic heat load aggregator on the Swedish island of Gotland. In the considered business case the aim of the aggregator is to minimize the losses on the HVDC connection with the mainland matching local wind generation and load. A network model is implemented and static power flow simulations are performed to evaluate the aggregator profit and the effects of its actions on the network behavior. Various aggregator strategies are simulated in different wind penetration scenarios, several indices are calculated to compare the different cases on a quantitative base.

An architecture for implementing state estimation application in Distribution Management System (DMS)

This work aims to propose a state estimation procedure for electric distribution networks and to implement a simulation architecture in order to test it under several scenarios. Measures, obtained from the simulated field through a communication layer, and pseudo-measures, defined according to load and generation models, are combined in different shares to observe the algorithm performances and the effects on the estimation quality. The work also investigates the necessity of a correct load modeling of Medium Voltage/Low Voltage (MV/LV) substations where distributed generation significantly contributes in altering the net power injection at the LV side. The implemented State Estimation (SE) procedure will be installed in a real MV network located in the North of Italy (Sanremo) within a research project.

Optimal charging strategy algorithm for PEVs: A Monte Carlo validation

This paper proposes the validation of an optimal charging strategy for plug-in electric vehicles (PEVs). The algorithm is composed of two phases and is intended to be used by Distribution System Operator (DSO) to properly manage the network. The effectiveness of the optimization algorithm, described in a previous work, is tested through Monte Carlo simulations. The proposed algorithm has evaluated and compared with a dumb charging strategy. The low voltage CIGRE benchmark grid for 5 years was simulated considering 300 PEVs. In addition, a different set of Monte Carlo simulations was run with random-though reasonable-load profiles in order to test the robustness of the algorithm. The traditional and newly proposed power-systemrelated indices were calculated for all of the simulation sets.

A two-stage margin-based algorithm for optimal plug-in electric vehicles scheduling

Summary form only given. This paper proposes an optimal charging strategy for plug-in electric vehicles (PEVs) to be used in electric distribution networks. The optimization algorithm is made up of two phases: 1) an optimal power flow calculation; and 2) a linear optimization. The former, while taking into account the power system technical constraints, sets the upper bounds to the recharge power for each vehicle and the latter defines the recharge profiles of each PEV. In order to test the effectiveness of the optimization algorithm, a case study was set up. The connection of 300 PEVs to the Conseil International des Grands Réseaux Électriques (CIGRÉ) European low voltage benchmark network has been simulated. The proposed algorithm has been compared with a nonoptimal charging strategy, which assigns a flat charging profile by dividing the energy requested by the desired recharge time. The results show that the optimization algorithm both complies with the energy requests set by the end users and with the technical operation limits of the network. This allows for PEVs to provide a basic-although of paramount importance-service to the grid: the smart charge.