Optimal allocation and operation of droop controlled islanded microgrids: a review

Copyright: © 2021 by the authors. This review paper provides a critical interpretation and analysis of almost 150 dedicated optimization research papers in the field of droop-controlled islanded microgrids. The significance of optimal microgrid allocation and operation studies comes from their importance for further deployment of renewable energy, reliable and stable autonomous operation on a larger scale, and the electrification of rural and isolated communities. Additionally, a comprehensive overview of islanded microgrids in terms of structure, type, and hierarchical control strategy was presented. Furthermore, a larger emphasis was given to the main optimization problems faced by droop-controlled islanded microgrids such as allocation, scheduling and dispatch, reconfiguration, control, and energy management systems. The main outcome of this review in relation to optimization problem components is the classification of objective functions, constraints, and decision variables into 10, 9 and 6 distinctive categories, respectively, taking into consideration the multi-criteria decision problems as well as the optimization with uncertainty problems in the classification criterion. Additionally, the optimization techniques used were investigated and identified as classical and artificial intelligence algorithms with the latter gaining popularity in recent years. Lastly, some future trends for research were put forward and explained based on the critical analysis of the selected papers

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Optimal distribution network reconfiguration using meta-heuristic algorithms

Finding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into GA enhances the efficiency of the parallel GA by adaptively modifying the migration rates between different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed which automatically generates radial topologies and prevents the construction of infeasible radial networks during the optimization process. The main shortcoming of the proposed algorithm in Chapter 2 is that it identifies only one single solution. It means that the system operator will not have any option but relying on the found solution. That is why a novel hybrid optimization algorithm is proposed in the third chapter of this dissertation that determines Pareto frontiers, as candidate solutions, for multi-objective distribution network reconfiguration problem. Implementing this model, the system operator will have more flexibility in choosing the best configuration among the alternative solutions. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance (FPD) with shuffled frog leaping algorithm (SFLA) to recognize non-dominated suboptimal solutions identified by SFLA. The local search step of SFLA is also customized for power systems applications so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure which significantly increases the convergence speed of the algorithm. In the fourth chapter, the problem of optimal network reconfiguration is solved for the case in which the system operator is going to employ an optimization algorithm that is automatically modifying its parameters during the optimization process. Defining three fuzzy functions, the probability of crossover and mutation will be adaptively tuned as the algorithm proceeds and the premature convergence will be avoided while the convergence speed of identifying the optimal configuration will not decrease. This modified genetic algorithm is considered a step towards making the parallel GA, presented in the second chapter of this dissertation, more robust in avoiding from getting stuck in local optimums. In the fifth chapter, the concentration will be on finding a potential smart grid solution to more high-quality suboptimal configurations of distribution networks. This chapter is considered an improvement for the third chapter of this dissertation for two reasons: (1) A fuzzy logic is used in the partitioning step of SFLA to improve the proposed optimization algorithm and to yield more accurate classification of frogs. (2) The problem of system reconfiguration is solved considering the presence of distributed generation (DG) units in the network. In order to study the new paradigm of integrating smart grids into power systems, it will be analyzed how the quality of suboptimal solutions can be affected when DG units are continuously added to the distribution network. The heuristic optimization algorithm which is proposed in Chapter 3 and is improved in Chapter 5 is implemented on a smaller case study in Chapter 6 to demonstrate that the identified solution through the optimization process is the same with the optimal solution found by an exhaustive search

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Integration of distributed generation along with energy storage system to reduce the high penetration impacts of renewable energy sources into the power grid.

Compte tenu du comportement aléatoire et fluctuant des sources d'énergie renouvelable (SER), l'équilibre entre la génération et la demande ne sont pas faciles à contrôler. Par conséquent, la stabilité dynamique du flux d'énergie et le contrôle de la fréquence deviennent de plus en plus difficiles en raison des impacts de la pénétration élevée des SER dans les micro-réseau électrique. Des stratégies de contrôle des convertisseurs/onduleurs avec filtre sont nécessaires pour maintenir l'alimentation électrique appropriée dans l'ensemble du micro-réseau. L'objectif de notre travail est d'explorer les aspects critiques de la génération distribuée (GD), de l'intégration des énergies renouvelables et des systèmes de stockage de l'énergie, en mettant l'accent sur l'amélioration de l'efficacité du réseau électrique tout en minimisant la pollution atmosphérique. Cette thèse reconnaît les avantages environnementaux et économiques de la GD tout en soulignant les défis inhérents à la gestion des sources d'énergie renouvelable fluctuantes. Un algorithme de contrôle pour un système de stockage d'énergie hybride diesel-éolien à forte pénétration est conçu pour maintenir la stabilité dynamique du flux d'énergie et le contrôle de la fréquence du réseau. Les principaux résultats comprennent la réduction efficace du temps de transition dans le flux d'énergie éolienne et des fluctuations de fréquence. D'autre part, cette étude répond aux défis posés par la nature intermittente des SER et leur impact sur la stabilité dynamique et le contrôle de la fréquence. Nous avons introduit un algorithme de contrôle utilisant la logique floue pour un système de stockage d'énergie éolienne en utilisant la méthode de partage de puissance. En comparant cette approche au contrôleur conventionnel, l'algorithme proposé a démontré des améliorations substantielles dans la réduction du temps de transition dans le flux d'énergie éolienne et des fluctuations de fréquence. Dans le cadre de cette thèse, une étude complète de divers convertisseurs statiques est réalisée afin de déterminer le dispositif de stockage d'énergie le plus approprié pour les applications de réseaux intelligents. Ce système de stockage joue un rôle essentiel dans le maintien de la stabilité du réseau tout en minimisant les pertes d'énergie. L'objectif est d'identifier le dispositif de stockage d'énergie le plus adapté à cette application. Les avantages de cette technologie sont d'une grande efficacité et fiabilité, qui peuvent connecter diverses sources d'énergie et réduire les pertes de conduction dans les convertisseurs de puissance. On a analysé l'efficacité et la fiabilité de différents convertisseurs et évalué leur performance dans des conditions de charge et de décharge du système de stockage. Les plages de fonctionnement des convertisseurs élévateur-abaisseur, abaisseur-élévateur et abaisseur-élévateur (-Vout) ont été analysées pour optimiser le système de stockage d'énergie. Cette thèse présente également une analyse complète d'un schéma de simulation qui exploite un système solaire composé de panneaux photovoltaïques intégrés au réseau électrique, à diverses charges, et à un dispositif de stockage d'énergie. Après la modélisation des panneaux photovoltaïques et de leurs caractéristiques opérationnelles, un filtre adaptatif est développé pour atténuer les fluctuations du courant d'entrée. On a exploré en outre l'efficacité et les mécanismes de contrôle des convertisseurs de puissance et des onduleurs, facilitant ainsi l'intégration du système de stockage d'énergie avec le réseau électrique. Plusieurs techniques de contrôle non linéaires sont utilisées pour évaluer les performances du système avec différentes configurations, y compris un onduleur simple, un filtre multi-variable, un filtre passe bande et une configuration sans filtre. Cette recherche nous a permis de proposer une régulation efficace du bus DC au sein du réseau électrique. L'avantage clé de ces régulateurs non linéaires est leur capacité à compenser la puissance réactive et les courants harmoniques, ce qui se traduit par un réseau électrique sans perturbations et une réduction du taux de distorsion harmonique totale (DHT) des onduleurs, améliorant finalement l'efficacité globale du réseau électrique. Cette thèse apporte des connaissances précieuses pour optimiser les performances des systèmes éoliens et solaires ainsi que du dispositif de stockage d'énergie, et leur intégration au réseau grâce à des techniques de contrôle et de filtrage avancées, avec des implications significatives pour l'amélioration de la stabilité et de la fiabilité des sources d'énergie renouvelable dans le réseau électrique. Abstract Being the fluctuation behavior of Renewable Energy Sources (RESs), generation, balance, and demand are not easy tasks to control because it is not desirable to have constant power generation from RESs due to natural prospects. As a result, the dynamic stability of power flow and control of frequency is becoming more challenging due to the high penetration impacts of RESs. Control strategies of converter/inverter with filter are also required to maintain the proper power supply in the entire microgrid where energy storage device plays crucial roles. The objective of this study is to explore critical aspects of distributed generation (DG), renewable energy integration, and energy storage systems, focusing on enhancing power network efficiency while minimizing power losses and environmental air pollution. This doctoral thesis acknowledges the environmental and economic benefits of distributed generation (DG) while highlighting the inherent challenges in managing fluctuating renewable energy sources (RESs). A control algorithm for a high-penetration hybrid diesel-wind-based energy storage system is designed to maintain dynamic stability in power flow and control network frequency. The key findings include the effective reduction of transient time in wind power flow and frequency fluctuations through the use of an integral-derivative (I-D) controller. On the other hand, it recognizes the challenges posed by the intermittent nature of renewable energy sources (RESs) and their impact on dynamic stability and frequency control. This thesis introduced a control algorithm employed with a Fuzzy Logic (FL) controller for a wind-based energy storage system using the power-sharing method. By comparing this approach to the traditional Proportional Integral Derivative (PID) controller, the study demonstrated substantial improvements in reducing transient time in wind power flow and frequency fluctuations. A storage system (battery) plays a crucial role in maintaining network stability while minimizing energy losses. As a part of this thesis, a comprehensive survey of various DC-DC converters is done to determine the most suitable energy storage device for smart grid applications. The main objective is to identify this application's most appropriate energy storage device. The advantages of this technology are high efficiency and reliability, which can connect various energy sources and reduce conduction losses in the power converters. The study analyzed the efficiency and reliability of different converters and evaluated their performance in charging and discharging conditions of a battery. The operating ranges of boost-buck, buck-boost, and buck-boost (-Vout) converters are analyzed to optimize the energy storage system. This doctoral thesis also presents a comprehensive analysis of a simulation scheme that leverages a solar system composed of photovoltaic (PV) panels integrated with the electrical grid, various loads, and an energy storage device. The research begins by investigating the modeling of PV panel cells and their operational characteristics. Subsequently, an adaptive notch filter synthesis is developed to mitigate input current fluctuations. The research further explores the efficiency and control mechanisms of power converters and inverters, facilitating the seamless integration of the energy storage system with the electrical grid. Multiple simulations are conducted, employing nonlinear control techniques to evaluate the performance of the system with different configurations, including a simple inverter, a multi-variable filter, a notch filter, and a filter-less setup. The research aims to achieve effective regulation of the DC bus within the proposed grid. The key advantage of these nonlinear controllers is their ability to compensate for reactive power and harmonic currents, resulting in a disturbance-free power network and a reduction in the Total Harmonic Distortion (THD) rate of the inverters, ultimately enhancing the overall efficiency of the power grid. This thesis contributes valuable insights into optimizing the performance of wind and solar systems along with energy storage device and their integration with the grid through advanced control and filtering techniques, with significant implications for improving the stability and reliability of renewable energy sources in the power grid

Advanced Modeling and Research in Hybrid Microgrid Control and Optimization

This book presents the latest solutions in fuel cell (FC) and renewable energy implementation in mobile and stationary applications. The implementation of advanced energy management and optimization strategies are detailed for fuel cell and renewable microgrids, and for the multi-FC stack architecture of FC/electric vehicles to enhance the reliability of these systems and to reduce the costs related to energy production and maintenance. Cyber-security methods based on blockchain technology to increase the resilience of FC renewable hybrid microgrids are also presented. Therefore, this book is for all readers interested in these challenging directions of research