Ilaria Bertini

Efficient Energy Management in Smart Micro-Grids: ZERO Grid Impact Buildings

In a smart micro-grid (MG) each generator or load has to take part in the network management, joining in reactive power supply/voltage control, active power supply/frequency control, fault ride-through capability, and power quality control. This paper includes a new concept for building integration in MGs with zero grid-impact so improving the MG efficiency. These aims are shown to be achievable with an intelligent system, based on a dc/ac converter connected to the building point of coupling with the main grid. This system can provide active and reactive power services also including a dc link where storage, generation, and loads can be installed. The system employed for validation is a prototype available at ENEA Laboratories (Italian National Agency for New Technologies). A complete and versatile model in MATLAB/SIMULINK is also presented. The simulations results and the experimental test validation are included. The trial confirms the model goodness and the system usefulness in MG applications.

A flexible Customer Power Device for energy management in a real Smart Micro-Grid

In the present paper an overview on Customer Power Devices (CPDs) for application in Smart Micro-Grid (MG) is reported. A particular attention is given to a real CPD equipped with an Energy Storage System, consisting in Lithium batteries, able to perform also functions of Distribution Static Compensators (D-STATCOM). Its typical layout, its main components and its possible functions are reported. A special focus is on its control modes and strategies for the energy management in a Smart MG and on its Battery Management System (BMS). The main figures of a real Smart MG, where the CDP is located, for active and reactive power service, are finally reported.

Reactive power control for an energy storage system

In last years, the power system operators are tackling many challenges for the renewable energies integration on the grid. Further, the expected increase of electrical demand due to the uncoordinated contemporary charging of a huge number of Electric Vehicles (EVs) can create chaotic phenomena with a negative impact especially on the distribution network. Help can be offered by the deployment of Smart Grid technologies, such as Smart Metering Systems (SMSs), Information and Communications Technology (ICT) and Energy Storage Systems (ESSs). In particular, in Micro-Grids, Battery ESSs (BESSs) can play a fundamental role and can become fundamental for the integration of EV fast charging stations and distributed generations. In this case the storage can have peak shaving, load shifting and power quality functions. The ESSs can provide ancillary services also on the grid as the reactive control to adjust the power factor. In the present paper, a monitoring control program to manage the reactive power of a real ESS in a Micro-Grid has been implemented. The system is a prototype, designed, implemented and now available at ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) labs. A wide experimental activity has been performed on the prototype system in order to test this functionality for the integration in a bigger Smart Grid available at the same ENEA labs including the Micro-grid. The integration has been possible, thanks to the free ICT protocols used by the researchers and which are described here. The results of the experimental tests show that the system can have good performance to adjust the power factor in respect to the main distribution grid and an EV charging station.

Start-Up optimisation of a combined cycle power plant with multiobjective evolutionary algorithms

In this paper we present a study of the application of Evolutionary Computation methods to the optimisation of the start-up of a combined cycle power plant. We propose a multiobjective approach considering different objectives for the optimisation in order to reduce the pollution emissions and to maximise the efficiency of the plant. We compare a multiobjective evolutionary algorithm (NSGA-II) with 2 and 5 objectives on a software simulator and then we use different metrics to measure the performances. We show that NSGA-II algorithm is able to provide a set of solutions, defined as Pareto Front, that represent the best trade-off on the different objectives among those the decision maker can choose.

A comparison of two innovative customer power devices for Smart Micro-Grids

The paper presents a comparison of two prototypes of innovative Customer Power Devices designed for applications in Smart Micro-Grids. The devices are both equipped with Energy Storage Systems, consisting in Lithium batteries and have been assembled, independently, by the DIAEE of the University of Rome “La Sapienza” and the DEIM of the University of Palermo in collaboration with the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA). In the paper, the characteristics and the operating modes of the devices are examined and some consideration on their application in Low Voltage Smart Micro-Grids are provided.

Evolving Complex Neural Networks

Complex networks like the scale-free model proposed by Barabasi-Albert are observed in many biological systems and the application of this topology to artificial neural network leads to interesting considerations. In this paper, we present a preliminary study on how to evolve neural networks with complex topologies. This approach is utilized in the problem of modeling a chemical process with the presence of unknown inputs (disturbance). The evolutionary algorithm we use considers an initial population of individuals with differents scale-free networks in the genotype and at the end of the algorithm we observe and analyze the topology of networks with the best performances. Experimentation on modeling a complex chemical process shows that performances of networks with complex topology are similar to the feed-forward ones but the analysis of the topology of the most performing networks leads to the conclusion that the distribution of input node information affects the network performance (modeling capability).

ZERO network-impact buildings and smart storage systems in micro-grids

The integration of intelligent storage systems in smart micro-grids (MGs) is necessary for the optimal management of the energy flows and to make the grids efficient and self-sustainable systems, further able to provide ancillary service to the main network. The present paper includes a new concept for integrating buildings equipped with intelligent storage systems in MG, making them zero grid-impact and so improving the efficiency of the MG including them. These aims are shown to be achievable with the use of an intelligent system managing the storage, based in a DC/AC converter connected to the point of coupling of the building with the main grid. The system is a prototype available at ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) labs in Rome. The authors describes it in details, including the control functions. A complete and versatile model in MatLab-Simulink is proposed and the results of simulations are shown.

Evolving feed-forward neural networks through evolutionary mutation parameters

In this paper we show a preliminary work on evolutionary mutation parameters in order to understand whether it is possible or not to skip mutation parameters tuning. In particular, rather than considering mutation parameters as global environmental features, we regard them as endogenous features of the individuals by putting them directly in the genotype. In this way we let the optimal values emerge from the evolutionary process itself. As case study, we apply the proposed methodology to the training of feed-forward neural netwoks on nine classification benchmarks and compare it to other five well established techniques. Results show the effectiveness of the proposed appraoch to get very promising results passing over the boring task of off-line optimal parameters tuning.

Rotor Imbalance Detection in Gas Turbines Using Fuzzy Sets

The paper focuses on the application of fuzzy sets in fault detection. The objective is to detect faults to an industrial gas turbine, with emphasis on the imbalance occurred in the rotor of the gas turbine. Such a fault has a certain degree of uncertainty and an index based on fuzzy sets has been developed in order to provide a fault confidence degree (0 meaning no fault, 1 the fault has been detected by all the sensors). Experimentation has been carried out on three real industrial turbines and it has shown the reliability and effectiveness of the methodology.

On-line Identification of a Municipal Solid Waste Incinerator by Fully Tuned RBF Neural Networks

Abstract The paper describes an on-line identification algorithm to estimate the steam production of a municipal solid waste incinerator. The algorithm has to learn on-line the system dynamics due to the heavy disturbances acting on the incineration process. The learning algorithm is based on radial basis function networks and combines the growth criterion of the resource allocating network technique with an adaptive extended Kalman filter to update all the parameters of the networks.