A model predictive control-based energy management scheme for hybrid storage system in islanded microgrids

Model predictive control (MPC) facilitates online optimal resource scheduling in electrical networks, thermal systems, water networks, process industry to name a few. In electrical systems, the capability of MPC can be used not only to minimise operating costs but also to improve renewable energy utilisation and energy storage system degradation. This work assesses the application of MPC for energy management in an islanded microgrid with PV generation and hybrid storage system composed of battery, supercapacitor and regenerative fuel cell. The objective is to improve the utilisation of renewable generation, the operational efficiency of the microgrid and the reduction in rate of degradation of storage systems. The improvements in energy scheduling, achieved with MPC, are highlighted through comparison with a heuristic based method, like Fuzzy inference. Simulated behaviour of an islanded microgrid with the MPC and fuzzy based energy management schemes will be studied for the same. Apart from this, the study also carries out an analysis of the computational demand resulting from the use of MPC in the energy management stage. It is concluded that, compared to heuristic methods, MPC ensures improved performance in an islanded microgrid.This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska Curie under Grant 675318 (INCITE), in part by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI under Grant MDM-2016-0656, and in part by the Spanish National Project DOVELAR (MCIU/AEI/FEDER, UE) under Grant RTI2018-096001-B-C32.Peer ReviewedPostprint (published version

Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMicrogrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.Peer ReviewedPostprint (author's final draft

Supervisory control for power management of an islanded AC microgrid using frequency signalling-based fuzzy logic controller

In islanded AC microgrids consisting of renewable energy sources (RES), battery-based energy storage system (BESS), and loads, the BESS balances the difference between the RES power and loads by delivering/absorbing that difference. However, the state of charge (SOC) and charging/discharging power of the battery should be kept within their design limits regardless of variations in the load demand or the intermittent power of the RES. In this paper, a supervisory controller based on fuzzy logic is proposed to assure that the battery power and energy do not exceed their design limits and maintaining a stable power flow. The microgrid considered in this paper consists of a PV, battery, load and auxiliary supplementary unit. The fuzzy logic controller alters the AC bus frequency, which is used by the local controllers of the parallel units to curtail the power generated by the PV or to supplement the power from the auxiliary unit. The proposed FLC performance is verified by simulation and experimental results. IEE

Frequency Management Strategies for Local Power Generation Network

This paper presents an intelligent load frequency control technique based on ANFIS controller which is capable to restore system frequency within small fraction of time. Frequency deviations in microgrid occur when the system supply is not sufficient to match the demand. Efforts are required to keep the frequency deviation within acceptable limit. Using vehicle-to-grid technology, where electric vehicles are used as energy storage elements for load frequency control in microgrid. For generating the control action to electric vehicles and energy sources in microgrid, type-2 ANFIS has been employed for quick frequency stabilization in the presence of load and source disturbances. Diesel generator and wind generator are DG sources considered in this paper and electric vehicles are used as energy storage element. Optimal power sharing among the different generating units and electric vehicles is achieved by ANFIS controller. Adaptive nature of ANFIS makes it more suitable and highly robust controller for a complex inter-connected system. Simulation results demonstrate that ANFIS controller is highly efficient as compared to PID controller, fuzzy logic controller, and interval type-2 fuzzy logic controller

Microgrid, Its Control and Stability: The State of The Art

Some of the challenges facing the power industries globally include power quality and stability, diminishing fossil fuel, climate change amongst others. The use of distributed generators however is growing at a steady pace to address these challenges. When interconnected and integrated with storage devices and controllable load, these generators operate together in a grid, which has incidental stability and control issues. The focus of this paper, therefore, is on the review and discussion of the different control approaches and the hierarchical control on a microgrid, the current practice in the literature concerning stability and the control techniques deployed for microgrid control; the weakness and strength of the different control strategies were discussed in this work and some of the areas that require further research are highlighted

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

Control and management of energy storage systems in microgrids

The rate of integration of the renewable energy sources in modern grids have significantly increased in the last decade. These intermittent, non-dispatchable renewable sources, though environment friendly tend to be grid unfriendly. This is precisely due to the issues pertaining to grid congestion, voltage regulation and stability of grids being reported as a result of the incorporation of renewable sources. In this scenario, the use of energy storage systems (ESS ) in electric grids is being widely proposed to overcome these issues. However, integrating energy storage systems alone will not compensate for the issue created by renewable generation. The control and management of the ESS should be done optimally so that their full capabilities are exploited to overcome the issues in the power grids and to ensure their lower cost of investment by prolonging ESS lifetime through minimising degradation. Motivated by this aspect this Ph.D work focusses on developing an efficient, optimal control and management strategy for ESS in a microgrid, especially hybrid ESS. The Ph.D work addresses this issue by proposing a hierarchical control scheme comprising of a lower power management and higher energy management stage with contributions in each stage. In the power management stage this work focusses on improving aspects of real time control of power converters interfacing ESS to grid and the microgrid system as whole. The work proposes control systems with improved dynamic behaviour for power converters based on the reset control framework. In the microgrid control the work presents a primary+secondary control scheme with improved voltage regulation performance under disturbances, using an observer. The real time power splitting strategies among hybrid ESS accounting for the ESS operating efficiencies and degradation mechanisms will also be addressed in the primary+secondary control of power management stage. The design criteria, stability and robustness analysis will be carried out, along with simulation or experimental verifications. In the higher level energy management stage, the contribution of this work involves application of an economic MPC framework for the management of ESS in microgrids. The work specifically addresses the problems of mitigating grid congestion from renewable power feed-in, minimising ESS degradation and maximising self consumption of generated renewable energy using the MPC based energy management system. A survey of the forecasting methods that can be used for MPC will be carried out and a neural network based forecasting unit for time series prediction will be developed. The practical issue of accounting for forecasting error in the decision making of MPC will be addressed and impact of the resulting conservative decision making on the system performance will be analysed. The improvement in performance with the proposed energy management scheme will be demonstrated and quantified.La integración de las fuentes de energía renovables en las redes modernas ha aumentado significativamente en la última década. Estas fuentes renovables, aunque muy convenientes para el medio ambiente son de naturaleza intermitente, y son no panificables, cosa que genera problemas en la red de distribución. Esto se debe precisamente a los problemas relacionados con la congestión de la red y la regulación del voltaje. En este escenario, el uso de sistemas de almacenamiento de energía (ESS) en redes eléctricas está siendo ampliamente propuesto para superar estos problemas. Sin embargo, la integración de sistemas de almacenamiento de energía por sí solos no compensará el problema creado por la generación renovable. El control y la gestión del ESS deben realizarse de manera óptima, de modo que se aprovechen al máximo sus capacidades para superar los problemas en las redes eléctricas, garantizar un coste de inversión razonable y prolongar la vida útil del ESS minimizando su degradación. Motivado por esta problemática, esta tesis doctoral se centra en desarrollar una estrategia de control y gestión eficiente para los ESS integrados en una microrred, especialmente cuando se trata de ESS de naturaleza. El trabajo de doctorado propone un esquema de control jerárquico compuesto por un control de bajo nivel y una parte de gestión de energía operando a más alto nivel. El trabajo realiza aportaciones en los dos campos. En el control de bajo nivel, este trabajo se centra en mejorar aspectos del control en tiempo real de los convertidores que interconectan el ESS con la red y el sistema de micro red en su conjunto. El trabajo propone sistemas de control con comportamiento dinámico mejorado para convertidores de potencia desarrollados en el marco del control de tipo reset. En el control de microrred, el trabajo presenta un esquema de control primario y uno secundario con un rendimiento de regulación de voltaje mejorado bajo perturbaciones, utilizando un observador. Además, el trabajo plantea estrategias de reparto del flujo de potencia entre los diferentes ESS. Durante el diseño de estos algoritmos de control se tienen en cuenta los mecanismos de degradación de los diferentes ESS. Los algoritmos diseñados se validarán mediante simulaciones y trabajos experimentales. En el apartado de gestión de energía, la contribución de este trabajo se centra en la aplicación del un control predictivo económico basado en modelo (EMPC) para la gestión de ESS en microrredes. El trabajo aborda específicamente los problemas de mitigar la congestión de la red a partir de la alimentación de energía renovable, minimizando la degradación de ESS y maximizando el autoconsumo de energía renovable generada. Se ha realizado una revisión de los métodos de predicción del consumo/generación que pueden usarse en el marco del EMPC y se ha desarrollado un mecanismo de predicción basado en el uso de las redes neuronales. Se ha abordado el análisis del efecto del error de predicción sobre el EMPC y el impacto que la toma de decisiones conservadoras produce en el rendimiento del sistema. La mejora en el rendimiento del esquema de gestión energética propuesto se ha cuantificado.La integració de les fonts d'energia renovables a les xarxes modernes ha augmentat significativament en l’última dècada. Aquestes fonts renovables, encara que molt convenients per al medi ambient són de naturalesa intermitent, i són no panificables, cosa que genera problemes a la xarxa de distribució. Això es deu precisament als problemes relacionats amb la congestió de la xarxa i la regulació de la tensió. En aquest escenari, l’ús de sistemes d'emmagatzematge d'energia (ESS) en xarxes elèctriques està sent àmpliament proposat per superar aquests problemes. No obstant això, la integració de sistemes d'emmagatzematge d'energia per si sols no compensarà el problema creat per la generació renovable. El control i la gestió de l'ESS s'han de fer de manera _optima, de manera que s'aprofitin al màxim les seves capacitats per superar els problemes en les xarxes elèctriques, garantir un cost d’inversió raonable i allargar la vida útil de l'ESS minimitzant la seva degradació. Motivat per aquesta problemàtica, aquesta tesi doctoral es centra a desenvolupar una estratègia de control i gestió eficient per als ESS integrats en una microxarxa, especialment quan es tracta d'ESS de natura híbrida. El treball de doctorat proposa un esquema de control jeràrquic compost per un control de baix nivell i una part de gestió d'energia operant a més alt nivell. El treball realitza aportacions en els dos camps. En el control de baix nivell, aquest treball es centra a millorar aspectes del control en temps real dels convertidors que interconnecten el ESS amb la xarxa i el sistema de microxarxa en el seu conjunt. El treball proposa sistemes de control amb comportament dinàmic millorat per a convertidors de potència desenvolupats en el marc del control de tipus reset. En el control de micro-xarxa, el treball presenta un esquema de control primari i un de secundari de regulació de voltatge millorat sota pertorbacions, utilitzant un observador. A més, el treball planteja estratègies de repartiment de el flux de potència entre els diferents ESS. Durant el disseny d'aquests algoritmes de control es tenen en compte els mecanismes de degradació dels diferents ESS. Els algoritmes dissenyats es validaran mitjanant simulacions i treballs experimentals. En l'apartat de gestió d'energia, la contribució d'aquest treball se centra en l’aplicació de l'un control predictiu econòmic basat en model (EMPC) per a la gestió d'ESS en microxarxes. El treball aborda específicament els problemes de mitigar la congestió de la xarxa a partir de l’alimentació d'energia renovable, minimitzant la degradació d'ESS i maximitzant l'autoconsum d'energia renovable generada. S'ha realitzat una revisió dels mètodes de predicció del consum/generació que poden usar-se en el marc de l'EMPC i s'ha desenvolupat un mecanisme de predicció basat en l’ús de les xarxes neuronals. S'ha abordat l’anàlisi de l'efecte de l'error de predicció sobre el EMPC i l'impacte que la presa de decisions conservadores produeix en el rendiment de el sistema. La millora en el rendiment de l'esquema de gestió energètica proposat s'ha quantificat

Sistemas de gestión de energía para microrredes: evolución y desafíos en el marco de la transición energética

Context: Microgrids have been gaining space and credibility in terms of research and real applications. Technological maturity and new regulations have allowed these types of systems to position themselves as a real alternative to increase the coverage of the energy service and improve its quality. One of the biggest challenges of microgrids is the management of resources and their synchronization with conventional grids. In order to overcome the inconvenience of synchronizing and managing the components of a microgrid, research on management systems has been conducted, which usually consist of a set of modules and control strategies that manage the available resources. However, these studies have not reached unanimity on the best method to perform these tasks, which is why it is necessary to perform a systematic collection of information and clearly define the state of research in energy systems management for this type of network. Method: Based on the above, a systematic mapping was carried out in this article, wherein a significant number of papers that have contributed to this area were compiled. Taxonomies were generated based on the nature of the variables collected. These variables correspond to the data or information that enters and/or leaves the microgrid management system, such as meteorological variables, power, priority loads, intelligent loads, economic, operating states, and binary outputs. Conclusions: It was observed that, despite the advances in studying different techniques and strategies microgird control and management, other factors that may affect performance have not been covered in a relevant way, such as the nature of variables and microgrid topology, among others.     Contexto: Las microrredes eléctricas han venido ganando espacio y credibilidad a nivel de investigación y aplicaciones reales. La madurez tecnológica y las nuevas regulaciones han permitido que este tipo de sistemas se posicionen como una alternativa real para aumentar la cobertura del servicio de energía y mejorar su calidad. Uno de los mayores retos de las microrredes es la gestión de los recursos y su sincronización con la red convencional. Con el fin de superar el inconveniente de sincronizar y gestionar los componentes de la microrred, se ha investigado sobre sistemas de gestión, los cuales normalmente consisten en un conjunto de módulos y estrategias de control que administran los recursos disponibles. Sin embargo, estas investigaciones no han llegado a una unanimidad sobre el mejor método para realizar estas tareas, por lo cual se hace necesario realizar una recopilación sistemática de información y definir claramente el estado de la investigación en gestión de sistemas de energía para este tipo de redes. Método: Con base en lo anterior, en este artículo se realizó un mapeo sistemático, donde se recopiló un importante número de artículos que han aportado a este campo. Se generaron taxonomías basadas en la naturaleza de las variables que se recopilaron. Dichas variables corresponden a los datos o información que entran y/o salen del sistema de gestión de la microrred, tales como variables meteorológicas, potencia, cargas prioritarias, cargas inteligentes, económicas, estados de operación y salidas binarias. Conclusiones: Se observa que, a pesar de los avances en el estudio de las diferentes técnicas y estrategias de control y gestión de microrredes, no se han cubierto de forma relevante otros factores que pueden afectar al rendimiento, como la naturaleza de las variables y la topología de la microrred, entre otros

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Hybrid energy storage system control analogous to power quality enhancement operation of interlinking converters

Increasing nonlinear loads and power electronic converters lead to various power quality issues in microgrids (MGs). The interlinking converters (ILCs) can participate in these systems to harmonic control and power quality enhancement. However, ILC participation deteriorates the dc link voltage, system stability, and storage lifetime due to oscillatory current phenomena. To address these problems, a new control strategy for a hybrid energy storage system (HESS) is proposed to eliminate the adverse effects of the harmonic control operation of ILC. Specifically, battery and super-capacitor (SC) are used as HESSs that provide low and high power frequency load, respectively. The proposed strategy tries to compensate the current oscillation imposed by ILC with fuzzy control of HESS. In this method, a proportional-resonant (PR) controller integrated with harmonic compensator (HC) is employed to control the ILC for power quality enhancement and oscillatory current elimination. The main advantages of the proposed strategy are to reduce DGs power fluctuations, precise DC bus voltage regulation for generation and load disturbances, improved grid power quality under nonlinear load and transition conditions. The performance of the proposed method for isolated and grid-connected modes is verified using simulation studies in the MATLAB software environment