• Energy Research

Modern distribution mechanisms within the smart grid paradigm are considered both reliable in nature and interconnected in topology. In this paper, a multiple-criteria-based sustainable planning (MCSP) approach is presented that serves as a future planning tool for interconnected distribution mechanisms and aims to find a feasible solution among conflicting criteria of various genres. The proposed methodology is based on three stages. In the stage 1, a weighted voltage stability index (VSI_W) and loss minimization condition (LMC) based approach aims at optimal asset optimization (sitting and sizing). In this stage, an evaluation of alternatives (solutions) is carried out across four dimensions (technical, economic, environmental, and social) of performance metrics. The assets considered in the evaluations include distributed generation (DG), renewable DGs, i.e., photovoltaic (PV), wind, and distributed static compensator (D-STATCOM) units. In the stage 2, various multicriteria decision-making (MCDM) methodologies are applied to ascertain the best trade-off among the available solutions in terms of techno-cost (economic) (TCPE), environment-o-social (ESPE), and techno-economic-environmental-socio (TEES) performance evaluations (OPE). In the stage 3, the alternatives are evaluated across multiple load growth horizons of 5 years each. The proposed MCSP approach is evaluated across a mesh-configured 33-bus active distribution network (ADN) and an actual NUST (which is a university in Islamabad, Pakistan) microgrid (MG), with various variants of load growth. The numerical findings of the proposed MCSP approach are compared with reported works the literature supports its validity and can serve as an important planning tool for interconnected distribution mechanisms for researchers and planning engineers.

The integration of distributed energy resources (DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method (IIDM) using an intrinsic mode function (IMF) feature-based grey wolf optimized artificial neural network (GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

The concept of microgrids has emerged as an effective way to integrate distributed energy resources (DERs) into distribution networks. The presence of DERs in microgrids leads to challenges in the formulation of protection for microgrids. Protection problems arise in a microgrid due to varying fault current levels in different operating scenarios. In order to overcome the practical challenges arising from varying fault current levels leading to short-circuit faults in microgrids, this paper proposes a MagnetoResistive (MR) sensors-based protection scheme, with fault localization through SuperimposedReactiveEnergy (SRE). The process is initiated by employing highly sensitive non-intrusive magnetic sensors to detect the magnetic field at each end of the distribution line. The magnetic field is then used to calculate the total harmonic distortion and thus detect faults in microgrids. After detection of faults, the proposed scheme uses SRE to identify faulty zones in microgrids. Finally, SI components of the current are extracted for fault classification. Extensive simulations on the International Electro-technical Commission (IEC) microgrid are performed in MATLAB/Simulink to validate the efficacy of the proposed scheme. Simulation results show that the proposed scheme can effectively detect, classify and isolate different faults in microgrids, while operating under various modes with varying fault locations and resistances, with the efficiency of approximately 97–98%.

This paper proposes an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). The proposed method first extracts useful statistical features from one cycle of post-fault current signals retrieved from sending-end relays of microgrids using DOST. Then, the extracted features are normalized and fed to the MKELM as an input. The MKELM, which consists of multiple kernels in the hidden nodes of an extreme learning machine, is used for the classification and location of faults in microgrids. A genetic algorithm is employed to determine the optimum parameters of the MKELM. The performance of the proposed method is tested on the standard IEC microgrid test system for various operating conditions and fault cases, including different fault locations, fault resistance, and fault inception angles using the MATLAB/Simulink software. The test results confirm the efficacy of the proposed method for classifying and locating any type of fault in a microgrid with high performance. Furthermore, the proposed method has higher performance and is more robust to measurement noise than existing intelligent methods.

Compelled by environmental and economic reasons and facilitated by modern technological advancements, the share of hybrid energy systems (HES) is increasing at modern smart house (SH) level. This work proposes an intelligent hybrid energy management system (IHEMS) for an SH connected to a power network that allows a bidirectional power flow. The SH has electrical and thermal power loops, and its main components include renewable energy from wind and photovoltaics, electric vehicle (EV), battery energy storage system, a fuel cell which serves as a micro-combined heat and power system, and a boiler. The proposed IHEMS models the components of the SH, defines their constraints, and develops an optimization model based on the real coded genetic algorithm. The key features of the developed IHEMS are highlighted under six simulation cases considering different configurations of the SH components. Moreover, the standard EV charging techniques are compared, and it is observed that the charging method which is flexible in timing and power injection to the EV is best suited for the economic operation of the SH. The simulation results reveal that the proposed IHEMS minimizes the 24-hour operational cost of the SH by optimally scheduling the energy resources and loads.

La demande croissante d'énergie dans les réseaux traditionnels devient de plus en plus complexe, moins faisable, nuisible, non économique et élevée en pertes de puissance. Ce document présente une approche de gestion de l'énergie efficace pour atténuer ces problèmes avec le micro-réseau intelligent (SMG) et vise une solution à la fois rentable et écologique, dans le cadre du paradigme du marché de l'énergie. Les objectifs sont atteints avec l'aide du contrôleur de gestion de l'énergie domestique (HEMC), du contrôleur de gestion du marché de l'énergie (EMMC) et de l'agent de contrôle (CA). La charge individuelle est gérée en présence de la production locale, du système de stockage, du confort de l'utilisateur, du SGD et des services publics au sein du paradigme du marché de l'énergie. Une approche de gestion de l'énergie à deux niveaux est proposée pour atteindre les objectifs concernés. La première consiste à gérer la charge et à planifier le stockage en ce qui concerne la production locale individuelle et la tarification du marché. La deuxième consiste à gérer le marché de l'énergie à l'aide de quatre types différents de priorités et d'entrée d'agent de contrôle. Le problème est résolu avec une variante de la méthode méta-heuristique, l'optimisation multi-objectif du loup gris (MOGWO), qui offre une solution plus complète en la comparant à l'optimisation de l'essaim de particules (PSO). La méthodologie proposée est mis en œuvre sur un système de test communautaire basé sur SMG. Les foyers au sein de cette communauté ont des conditions économiques et des priorités personnelles différentes. Les résultats de la simulation démontrent l'atteinte des objectifs visés dans le travail présenté. La creciente demanda de energía en las redes tradicionales se está volviendo más compleja, menos factible, dañina, antieconómica y con altas pérdidas de energía. Este documento presenta un enfoque de gestión energética eficiente para mitigar estos problemas con una microrred inteligente (SMG) y apunta a una solución que sea rentable y ecológica, dentro del paradigma del mercado energético. Los objetivos se logran con la ayuda de Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) y Control Agent (CA). La carga individual se gestiona en presencia. de generación local, sistema de almacenamiento, comodidad del usuario, DGS y Utilidad dentro del paradigma del mercado de la energía. Se propone un enfoque de gestión de energía de dos niveles para lograr los objetivos en cuestión. En primer lugar, es gestionar la carga y programar el almacenamiento con respecto a la generación local individual y los precios del mercado. En segundo lugar, es gestionar el mercado de la energía con la ayuda de cuatro tipos diferentes de prioridades y entrada de agentes de control. El problema se resuelve con una variante del método metaheurístico, la optimización multiobjetivo del lobo gris (MOGWO), que ofrece una solución más completa al compararlo con la optimización del enjambre de partículas (PSO). La metodología propuesta es implementado en un sistema de prueba comunitario basado en SMG. Los hogares dentro de esa comunidad tienen diferentes condiciones económicas y prioridades personales. Los resultados de la simulación demuestran el logro de los objetivos previstos en el trabajo presentado. The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses.This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and ecofriendly, within energy market paradigm.Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA).The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm.Two level energy management approach is proposed to achieve concerned goals.First is to manage load and schedule storage with respect to individual local generation and market pricing.Second is to manage energy market with the help of four different types of priorities and control agent input.The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO).The proposed methodology is implemented on a SMG based-community test system.Homes within that community have different economic conditions and personal priorities.Simulation results demonstrates achievement of aimed goals in presented work. يزداد الطلب المتزايد على الطاقة في الشبكات التقليدية تعقيدًا وأقل جدوى وضارة وغير اقتصادية ومرتفعة في فقدان الطاقة. تقدم هذه الورقة نهجًا فعالًا لإدارة الطاقة للتخفيف من هذه المشكلات باستخدام الشبكة الذكية الصغيرة (SMG) وتهدف إلى حل فعال من حيث التكلفة وصديق للبيئة، ضمن نموذج سوق الطاقة. يتم تحقيق الأهداف بمساعدة وحدة التحكم في إدارة الطاقة المنزلية (HEMC) ووحدة التحكم في إدارة سوق الطاقة (EMMC) ووكيل التحكم (CA). تتم إدارة الحمل الفردي في وجود من التوليد المحلي، ونظام التخزين، وراحة المستخدم، و DGS والمنفعة داخل نموذج سوق الطاقة. يقترح نهجان لإدارة الطاقة على مستوى واحد لتحقيق الأهداف المعنية. الأول هو إدارة الحمل وجدولة التخزين فيما يتعلق بالتوليد المحلي الفردي وتسعير السوق. الثاني هو إدارة سوق الطاقة بمساعدة أربعة أنواع مختلفة من الأولويات ومدخلات عامل التحكم. يتم حل المشكلة باستخدام متغير من الطريقة الاستدلالية، تحسين الذئب الرمادي متعدد الأهداف (MOGWO)، والذي يعطي حلاً أكثر شمولاً من خلال المقارنة مع تحسين سرب الجسيمات (PSO). المنهجية المقترحة هي يتم تنفيذها على نظام اختبار مجتمعي قائم على SMG. تتمتع المنازل داخل هذا المجتمع بظروف اقتصادية وأولويات شخصية مختلفة. توضح نتائج المحاكاة تحقيق الأهداف المستهدفة في العمل المقدم.

Recently, the concept of the microgrid (MG) has been developed to assist the penetration of large numbers of distributed energy resources (DERs) into distribution networks. However, the integration of DERs in the form of MGs disturbs the operating codes of traditional distribution networks. Consequently, traditional protection strategies cannot be applied to MG against short-circuit faults. This paper presents a novel intelligent protection strategy (NIPS) for MGs based on empirical wavelet transform (EWT) and long short-term memory (LSTM) networks. In the proposed NIPS, firstly, the three-phase current signals measured by protective relays are decomposed into empirical modes (EMs). Then, various statistical features are extracted from the obtained EMs. Afterwards, the extracted features along with the three-phase current measurement are input to three different LSTM network to obtain exact fault type, phase, and location information. Finally, a trip signal based on the obtained fault information is generated to disconnect the faulty portion from the rest of the MG. The significant feature of the proposed NIPS is that it does not need adaptive relaying and communication networks. Moreover, it is independent of the operating scenario and hence fault current magnitude. To evaluate the efficacy of the proposed NIPS, exhaustive simulations are performed on an international electro-technical commission (IEC) MG. The simulation results confirm the efficiency of the proposed NIPs in terms of accuracy, dependability, and security. Moreover, comparisons with existing intelligent protection schemes validate that the proposed NIPS is highly accurate, secure, and dependable.

The increasing penetration of electric vehicles (EVs) in the distribution grid has established them as a prospective resource for ancillary services. These services require adequate control strategies for prompt and efficient operation. In this study, an energy management scheme (EMS) has been proposed to employ an off-board EV smart charger to support the grid during short-term variance of renewables and reactive load onset. The scheme operates by calculating power references for the charger instantaneously. The EMS incorporates a proportional power division methodology, proposed to allocate power references to the individual EVs connected to the charger DC-bus. This methodology considers the state-of-charge and battery sizes of the EVs, and it can aggregate energy from various types of EVs. The proposed scheme is compared with another power allocation method, and the entire EMS is tested under the scenarios of power mismatch and voltage sag/swell events. The results show that the proposed scheme achieves the goal of the aggregation of EVs at the charger level to support the grid. The EMS also fulfills the objectives of voltage regulation and four-quadrant operation of the smart charger.