DC & Hybrid Micro-Grids

This book is a printed version of the papers published in the Special Issue “DC & Hybrid Microgrids” of Applied Sciences. This Special Issue, co-organized by the University of Pisa, Italy and Østfold University College in Norway, has collected nine papers and the editorial, from 28 submitted, with authors from Asia, North America and Europe. The published articles provide an overview of the most recent research advances in direct current (DC) and hybrid microgrids, exploiting the opportunities offered by the use of renewable energy sources, battery energy storage systems, power converters, innovative control and energy management strategies

Contributions for microgrids dynamic modelling and operation

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Management and modelling of battery storage systems in microGrids and virtual power plants

In the novel smart grid configuration of power networks, Energy Storage Systems (ESSs) are emerging as one of the most effective and practical solutions to improve the stability, reliability and security of electricity power grids, especially in presence of high penetration of intermittent Renewable Energy Sources (RESs). This PhD dissertation proposes a number of approaches in order to deal with some typical issues of future active power systems, including optimal ESS sizing and modelling problems, power ows management strategies and minimisation of investment and operating costs. In particular, in the first part of the Thesis several algorithms and methodologies for the management of microgrids and Virtual Power Plants, integrating RES generators and battery ESSs, are proposed and analysed for four cases of study, aimed at highlighting the potentialities of integrating ESSs in different smart grid architectures. The management strategies here presented are specifically based on rule-based and optimal management approaches. The promising results obtained in the energy management of power systems have highlighted the importance of reliable component models in the implementation of the control strategies. In fact, the performance of the energy management approach is only as accurate as the data provided by models, batteries being the most challenging element in the presented cases of study. Therefore, in the second part of this Thesis, the issues in modelling battery technologies are addressed, particularly referring to Lithium-Iron Phosphate (LFP) and Sodium-Nickel Chloride (SNB) systems. In the first case, a simplified and unified model of lithium batteries is proposed for the accurate prediction of charging processes evolution in EV applications, based on the experimental tests on a 2.3 Ah LFP battery. Finally, a dynamic electrical modelling is presented for a high temperature Sodium-Nickel Chloride battery. The proposed modelling is developed from an extensive experimental testing and characterisation of a commercial 23.5 kWh SNB, and is validated using a measured current-voltage profile, triggering the whole battery operative range

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Energy-Delay Tradeoff and Dynamic Sleep Switching for Bluetooth-Like Body-Area Sensor Networks

Wireless technology enables novel approaches to healthcare, in particular the remote monitoring of vital signs and other parameters indicative of people's health. This paper considers a system scenario relevant to such applications, where a smart-phone acts as a data-collecting hub, gathering data from a number of wireless-capable body sensors, and relaying them to a healthcare provider host through standard existing cellular networks. Delay of critical data and sensors' energy efficiency are both relevant and conflicting issues. Therefore, it is important to operate the wireless body-area sensor network at some desired point close to the optimal energy-delay tradeoff curve. This tradeoff curve is a function of the employed physical-layer protocol: in particular, it depends on the multiple-access scheme and on the coding and modulation schemes available. In this work, we consider a protocol closely inspired by the widely-used Bluetooth standard. First, we consider the calculation of the minimum energy function, i.e., the minimum sum energy per symbol that guarantees the stability of all transmission queues in the network. Then, we apply the general theory developed by Neely to develop a dynamic scheduling policy that approaches the optimal energy-delay tradeoff for the network at hand. Finally, we examine the queue dynamics and propose a novel policy that adaptively switches between connected and disconnected (sleeping) modes. We demonstrate that the proposed policy can achieve significant gains in the realistic case where the control "NULL" packets necessary to maintain the connection alive, have a non-zero energy cost, and the data arrival statistics corresponding to the sensed physical process are bursty.Comment: Extended version (with proofs details in the Appendix) of a paper accepted for publication on the IEEE Transactions on Communication

Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins

Given harsh operating conditions and physical constraints in reactors, nuclear applications cannot afford to equip the physical asset with a large array of sensors. Therefore, it is crucial to carefully determine the placement of sensors within the given spatial limitations, enabling the reconstruction of reactor flow fields and the creation of nuclear digital twins. Various design considerations are imposed, such as predetermined sensor locations, restricted areas within the reactor, a fixed number of sensors allocated to a specific region, or sensors positioned at a designated distance from one another. We develop a data-driven technique that integrates constraints into an optimization procedure for sensor placement, aiming to minimize reconstruction errors. Our approach employs a greedy algorithm that can optimize sensor locations on a grid, adhering to user-defined constraints. We demonstrate the near optimality of our algorithm by computing all possible configurations for selecting a certain number of sensors for a randomly generated state space system. In this work, the algorithm is demonstrated on the Out-of-Pile Testing and Instrumentation Transient Water Irradiation System (OPTI-TWIST) prototype vessel, which is electrically heated to mimic the neutronics effect of the Transient Reactor Test facility (TREAT) at Idaho National Laboratory (INL). The resulting sensor-based reconstruction of temperature within the OPTI-TWIST minimizes error, provides probabilistic bounds for noise-induced uncertainty and will finally be used for communication between the digital twin and experimental facility