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

Development of a Converter-Based Testing Platform and Battery Energy Storage System (BESS) Emulator for Microgrid Controller Function Evaluation

The microgrid has attracted increasing research attention in the last two decades. Due to the development of renewable energy resources and power electronics technologies, the future microgrid will trend to be smarter and more complicated. The microgrid controller performs a critical role in the microgrid operation, which will also become more and more sophisticated to support the future microgrid. Before final field deployment and test, the evaluation and testing of the controller is an indispensable step in the controller development, which requires a proper testing platform. However, existing simulation-based platforms have issues with potential numerical oscillation and may require huge computation resources for complex microgrid controllers. Meanwhile, field test-based controller evaluation is limited to the test conditions. Existing digital simulation-based platforms and field test-based platforms have limitations for microgrid controller testing. To provide a practical and flexible controller evaluation, a converter-based microgrid hardware testbed is designed and implemented considering the actual microgrid architecture and topology information. Compared with the digital simulation-based platforms, the developed microgrid testing platform can provide a more practical testing environment. Compared to the direct field test, the developed platform is more flexible to emulate different microgrids. As one of the key components, a converter-based battery energy storage system (BESS) emulator is proposed to complete the developed testing platform based on the testing requirements of microgrid controller functions. Meanwhile, the microgrid controller testing under different microgrid conditions is also considered. Two controllers for the microgrid with dynamic boundaries are tested to demonstrate the capability of the developed platform as well as the BESS emulator. Different testing cases are designed and tested to evaluate the controller performance under different microgrid conditions

Contributions on DC microgrid supervision and control strategies for efficiency optimization through battery modeling, management, and balancing techniques

Aquesta tesi presenta equips, models i estratègies de control que han estat desenvolupats amb l'objectiu final de millorar el funcionament d'una microxarxa CC. Es proposen dues estratègies de control per a millorar l'eficiència dels convertidors CC-CC que interconnecten les unitats de potència de la microxarxa amb el bus CC. La primera estratègia, Control d'Optimització de Tensió de Bus centralitzat, administra la potència del Sistema d'Emmagatzematge d'Energia en Bateries de la microxarxa per aconseguir que la tensió del bus segueixi la referència dinàmica de tensió òptima que minimitza les pèrdues dels convertidors. La segona, Optimització en Temps Real de la Freqüència de Commutació, consisteix a operar localment cada convertidor a la seva freqüència de commutació òptima, minimitzant les seves pèrdues. A més, es proposa una nova topologia d'equilibrador actiu de bateries mitjançant un únic convertidor CC-CC i s'ha dissenyat la seva estratègia de control. El convertidor CC-CC transfereix càrrega cel·la a cel·la, emprant encaminament de potència a través d'un sistema d'interruptors controlats. L'estratègia de control de l'equalitzador aconsegueix un ràpid equilibrat del SOC evitant sobrecompensar el desequilibri. Finalment, es proposa un model simple de degradació d'una cel·la NMC amb elèctrode negatiu de grafit. El model combina la simplicitat d'un model de circuit equivalent, que explica la dinàmica ràpida de la cel·la, amb un model físic del creixement de la capa Interfase Sòlid-Electròlit (SEI), que prediu la pèrdua de capacitat i l'augment de la resistència interna a llarg termini. El model proposat quantifica la incorporació de liti al rang de liti ciclable necessària per a aconseguir els límits de OCV després de la pèrdua de liti ciclable en la reacció secundària. El model de degradació SEI pot emprar-se per a realitzar un control predictiu de bateries orientat a estendre la seva vida útil.Aquesta tesi presenta equips, models i estratègies de control que han estat desenvolupats amb l'objectiu final de millorar el funcionament d'una microxarxa CC. Es proposen dues estratègies de control per a millorar l'eficiència dels convertidors CC-CC que interconnecten les unitats de potència de la microxarxa amb el bus CC. La primera estratègia, Control d'Optimització de Tensió de Bus centralitzat, administra la potència del Sistema d'Emmagatzematge d'Energia en Bateries de la microxarxa per aconseguir que la tensió del bus segueixi la referència dinàmica de tensió òptima que minimitza les pèrdues dels convertidors. La segona, Optimització en Temps Real de la Freqüència de Commutació, consisteix a operar localment cada convertidor a la seva freqüència de commutació òptima, minimitzant les seves pèrdues. A més, es proposa una nova topologia d'equilibrador actiu de bateries mitjançant un únic convertidor CC-CC i s'ha dissenyat la seva estratègia de control. El convertidor CC-CC transfereix càrrega cel·la a cel·la, emprant encaminament de potència a través d'un sistema d'interruptors controlats. L'estratègia de control de l'equalitzador aconsegueix un ràpid equilibrat del SOC evitant sobrecompensar el desequilibri. Finalment, es proposa un model simple de degradació d'una cel·la NMC amb elèctrode negatiu de grafit. El model combina la simplicitat d'un model de circuit equivalent, que explica la dinàmica ràpida de la cel·la, amb un model físic del creixement de la capa Interfase Sòlid-Electròlit (SEI), que prediu la pèrdua de capacitat i l'augment de la resistència interna a llarg termini. El model proposat quantifica la incorporació de liti al rang de liti ciclable necessària per a aconseguir els límits de OCV després de la pèrdua de liti ciclable en la reacció secundària. El model de degradació SEI pot emprar-se per a realitzar un control predictiu de bateries orientat a estendre la seva vida útil.This dissertation presents a set of equipment, models and control strategies, that have been developed with the final goal of improving the operation of a DC microgrid. Two control strategies are proposed to improve the efficiency of the DC-DC converters that interface the microgrid’s power units with the DC bus. The first strategy is centralized Bus Voltage Optimization Control, which manages the power of the microgrid’s Battery Energy Storage System to make the bus voltage follow the optimum voltage dynamic reference that minimizes the converters’ losses. The second control strategy is Online Optimization of Switching Frequency, which consists in locally operating each converter at its optimum switching frequency, again minimizing power losses. The two proposed optimization strategies have been validated in simulations. Moreover, a new converter-based active balancing topology has been proposed and its control strategy has been designed. This equalizer topology consists of a single DC-DC converter that performs cell-to-cell charge transfer employing power routing via controlled switches. The control strategy of the equalizer has been designed to achieve rapid SOC balancing while avoiding imbalance overcompensation. Its performance has been validated in simulation. Finally, a simple degradation model of an NMC battery cell with graphite negative electrode is proposed. The model combines the simplicity of an equivalent circuit model, which explains the fast dynamics of the cell, with a physical model of the Solid-Electrolyte Interphase (SEI) layer growth process, which predicts the capacity loss and the internal resistance rise in the long term. The proposed model fine-tunes the capacity loss prediction by accounting for the incorporation of unused lithium reserves of both electrodes into the cyclable lithium range to reach the OCV limits after the side reaction has consumed cyclable lithium. The SEI degradation model can be used to perform predictive control of batteries oriented toward extending their lifetime

Review on Control of DC Microgrids and Multiple Microgrid Clusters

This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors' point of view, are also provided in the final concluding part

A DC Microgrid Coordinated Control Strategy Based on Integrator Current-Sharing

The DC microgrid has become a new trend for microgrid study with the advantages of high reliability, simple control and low losses. With regard to the drawbacks of the traditional droop control strategies, an improved DC droop control strategy based on integrator current-sharing is introduced. In the strategy, the principle of eliminating deviation through an integrator is used, constructing the current-sharing term in order to make the power-sharing between different distributed generation (DG) units uniform and reasonable, which can reduce the circulating current between DG units. Furthermore, at the system coordinated control level, a hierarchical/droop control strategy based on the DC bus voltage is proposed. In the strategy, the operation modes of the AC main network and micro-sources are determined through detecting the DC voltage variation, which can ensure the power balance of the DC microgrid under different operating conditions. Meanwhile, communication is not needed between different DG units, while each DG unit needs to sample the DC bus voltage, which retains the plug-and-play feature of the DC microgrid. The proposed control strategy is validated by simulation on a DC microgrid with permanent magnet synchronous generator-based wind turbines, solar arrays and energy storage batteries, which can be applied to small commercial or residential buildings

Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives