• Energy Research

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

Un système de gestion de l'énergie domestique opérationnel et polyvalent est proposé pour développer et mettre en œuvre des projets de réponse à la demande (DR). Ceux-ci sont sous la génération hybride du système de stockage d'énergie (ESS), du photovoltaïque (PV) et des véhicules électriques (EV) dans le réseau intelligent (SG). Les systèmes de gestion de l'énergie domestique existants ne peuvent pas offrir à ses utilisateurs un choix pour assurer le confort de l'utilisateur (UC) et ne pas fournir une solution durable en termes d'émissions de carbone réduites. Pour résoudre ces problèmes, ce travail de recherche propose un contrôleur de gestion de l'énergie programmable basé sur l'heuristique (HPEMC) pour gérer la consommation d'énergie dans les bâtiments résidentiels afin de minimiser les factures d'électricité, de réduire les émissions de carbone, de maximiser la CU et de réduire le rapport pic/moyenne (par). Nous avons utilisé notre algorithme hybride d'optimisation des essaims de particules génétiques (HGPO) et les algorithmes existants tels qu'un algorithme génétique (GA), un algorithme d'optimisation des essaims de particules binaires (BPSO), une optimisation des colonies de fourmis (ACO), un algorithme d'optimisation éolien (WDO), un algorithme de recherche de nourriture bactérienne (BFA) pour planifier les appareils intelligents de manière optimale afin d'atteindre nos objectifs souhaités. Dans le modèle proposé, les consommateurs utilisent des panneaux solaires pour produire leur énergie à partir de micro-réseaux. Nous effectuons également des simulations Matlab pour valider notre HGPO-HPEMC proposé (HHPEMC), et les résultats confirment l'efficacité et la productivité de notre stratégie basée sur HPEMC proposée. L'algorithme proposé a réduit le coût de l'électricité de 25,55 %, le PAIR de 36,98 % et les émissions de carbone de 24,02 % par rapport au cas sans planification. Se propone un sistema de gestión de energía doméstica operativo y versátil para desarrollar e implementar proyectos de respuesta a la demanda (DR). Estos están bajo la generación híbrida del sistema de almacenamiento de energía (ESS), fotovoltaica (PV) y vehículos eléctricos (EV) en la red inteligente (SG). Los sistemas de gestión de energía doméstica existentes no pueden ofrecer a sus usuarios una opción para garantizar la comodidad del usuario (UC) y no proporcionar una solución sostenible en términos de reducción de emisiones de carbono. Para abordar estos problemas, este trabajo de investigación propone un controlador de gestión de energía programable basado en heurística (HPEMC) para administrar el consumo de energía en edificios residenciales para minimizar las facturas de electricidad, reducir las emisiones de carbono, maximizar la UC y reducir la relación pico a promedio (par). Utilizamos nuestro algoritmo de optimización de enjambre de partículas genéticas híbridas (HGPO) propuesto y los algoritmos existentes como un algoritmo genético (GA), algoritmo de optimización de enjambre de partículas binarias (BPSO), optimización de colonias de hormigas (ACO), algoritmo de optimización impulsado por el viento (WDO), algoritmo de forrajeo bacteriano (BFA) para programar aparatos inteligentes de manera óptima para alcanzar nuestros objetivos deseados. En el modelo propuesto, los consumidores utilizan paneles solares para producir su energía a partir de microrredes. También realizamos simulaciones MATLAB para validar nuestro HGPO-HPEMC propuesto (HHPEMC), y los resultados confirman la eficiencia y productividad de nuestra estrategia basada en HPEMC propuesta. El algoritmo propuesto redujo el costo de electricidad en un 25.55%, PAR en un 36.98% y la emisión de carbono en un 24.02% en comparación con el caso de sin programación. An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects.These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG).Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission.To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR).We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives.In the proposed model, consumers use solar panels to produce their energy from microgrids.We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy.The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling. يُقترح نظام فعال ومتعدد الاستخدامات لإدارة الطاقة المنزلية لتطوير وتنفيذ مشاريع الاستجابة للطلب (DR). وهي تحت التوليد الهجين لنظام تخزين الطاقة (ESS)، والطاقة الكهروضوئية (PV)، والمركبات الكهربائية (EVs) في الشبكة الذكية (SG). لا يمكن لأنظمة إدارة الطاقة المنزلية الحالية أن توفر لمستخدميها خيارًا لضمان راحة المستخدم (UC) وعدم توفير حل مستدام من حيث تقليل انبعاثات الكربون. لمعالجة هذه المشاكل، يقترح هذا العمل البحثي وحدة تحكم في إدارة الطاقة القابلة للبرمجة القائمة على الاستدلال (HPEMC) لإدارة استهلاك الطاقة في المباني السكنية لتقليل فواتير الكهرباء وتقليل انبعاثات الكربون وتعظيم UC وتقليل نسبة الذروة إلى المتوسط (PAR). استخدمنا خوارزمية تحسين سرب الجسيمات الوراثية الهجينة المقترحة (HGPO) والخوارزميات الحالية مثل الخوارزمية الوراثية (GA)، وخوارزمية تحسين سرب الجسيمات الثنائية (BPSO)، وتحسين مستعمرة النمل (ACO)، وخوارزمية التحسين التي تحركها الرياح (WDO)، وخوارزمية البحث عن البكتريا (BFA) لجدولة الأجهزة الذكية على النحو الأمثل لتحقيق أهدافنا المرجوة. في النموذج المقترح، يستخدم المستهلكون الألواح الشمسية لإنتاج طاقتهم من الشبكات الدقيقة. نقوم أيضًا بإجراء عمليات محاكاة MATLAB للتحقق من صحة تؤكد نتائجنا المقترحة HGPO - HPEMC (HHPEMC) كفاءة وإنتاجية استراتيجيتنا المقترحة القائمة على HPEMC. خفضت الخوارزمية المقترحة تكلفة الكهرباء بنسبة 25.55 ٪، PAR بنسبة 36.98 ٪، وانبعاث الكربون بنسبة 24.02 ٪ مقارنة بحالة بدون جدولة.

Energy balancing in smart microgrid plays a vital role to improve the reliability and resolves the load shedding problem to ensure consistent energy supply. However, energy balancing is challenging due to uncertain and intermittent nature of renewable energy integrated in smart microgrid. To solve such problems, dynamic energy pricing mechanism is developed that maintain energy balance for overcoming the gap between demand and supply. Thus, the particle swarm optimization based super twisting sliding mode controller (PSO-STSMC) is developed which uses dynamic energy pricing to control renewable energy resources’ generation according to the consumers’ demand for real time closed loop energy balancing in an energy market. The proposed PSO-STSMC based model is compared with existing models like proportional integral derivative (PID) controller, proportional integral (PI) controller, proportional derivative (PD) controller, and fractional order proportional derivative (FO-PD) controller and the optimized models of the particle swarm optimization based proportional integral (PSO-PI) controller and particle swarm optimization based proportional integral derivative (PSO-PID) controller. Simulations results demonstrate that energy price regulation by PSO-STSMC consistently controls the elastic demand for real time energy balancing.

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

La planification de la charge, le contrôle du stockage de l'énergie de la batterie et l'amélioration du confort de l'utilisateur sont des problèmes critiques d'optimisation de l'énergie dans le réseau intelligent. Cependant, les entrées du système telles que le processus de génération d'énergie renouvelable, le processus de génération de réseau conventionnel, le processus de charge/décharge de la batterie, les signaux de prix dynamiques et le processus d'arrivée de la charge comprennent les performances du contrôleur pour optimiser avec précision la planification du stockage de l'énergie de la batterie en temps réel, la planification de la charge, la génération d'énergie et le confort de l'utilisateur. Ainsi, dans ce travail, la technique d'optimisation Lyapunov basée sur la stabilité de la file d'attente virtuelle (LOT) est adoptée pour étudier l'optimisation de l'énergie en temps réel dans une maison intelligente durable connectée au réseau avec un la charge de chauffage, de ventilation et de climatisation (CVC) compte tenu de la dynamique inconnue des entrées du système. L'objectif principal est de minimiser le coût énergétique moyen global dans le temps et le coût de l'inconfort thermique à long terme pour une maison intelligente durable tenant compte des changements dans l'état d'occupation de la maison, du réglage de la température le plus confortable, de la consommation électrique, de la production d'énergie renouvelable, de la température extérieure et des coûts de l'électricité. L'algorithme employé crée et régule quatre files d'attente pour la température intérieure, la charge du véhicule électrique (VE) et le système de stockage d'énergie (ESS). Des simulations approfondies sont effectuées pour valider l'algorithme employé. La programación de la carga, el control del almacenamiento de energía de la batería y la mejora de la comodidad del usuario son problemas críticos de optimización de la energía en la red inteligente. Sin embargo, las entradas del sistema, como el proceso de generación de energía renovable, el proceso de generación de red convencional, el proceso de carga/descarga de la batería, las señales dinámicas de precios y el proceso de llegada de la carga, comprenden el rendimiento del controlador para optimizar con precisión la programación del almacenamiento de energía de la batería en tiempo real, la programación de la carga, la generación de energía y la comodidad del usuario. Por lo tanto, en este trabajo, se adopta la técnica de optimización de Lyapunov basada en la estabilidad de la cola virtual (LOT) para investigar la optimización de la energía en tiempo real en una casa inteligente sostenible conectada a la red con una red. carga de calefacción, ventilación y aire acondicionado (HVAC) teniendo en cuenta la dinámica de entradas desconocidas del sistema. El objetivo principal es minimizar el coste medio global de energía y el coste de incomodidad térmica en un horizonte a largo plazo para un hogar inteligente sostenible teniendo en cuenta los cambios en el estado de ocupación del hogar, el ajuste de temperatura más cómodo, el consumo eléctrico, la producción de generación renovable, la temperatura exterior y los costes de electricidad. El algoritmo empleado crea y regula cuatro colas para la temperatura interior, la carga de vehículos eléctricos (EV) y el sistema de almacenamiento de energía (ESS). Se realizan simulaciones exhaustivas para validar el algoritmo empleado. Load scheduling, battery energy storage control, and improving user comfort are critical energy optimization problems in smart grid.However, system inputs like renewable energy generation process, conventional grid generation process, battery charging/discharging process, dynamic price signals, and load arrival process comprise controller performance to accurately optimize real-time battery energy storage scheduling, load scheduling, energy generation, and user comfort.Thus, in this work, the virtual queue stability based Lyapunov optimization technique (LOT) is adopted to investigate real-time energy optimization in a grid-connected sustainable smart home with a heating, ventilation, and air conditioning (HVAC) load considering unknown system inputs dynamics.The main goal is to minimize overall time average energy cost and thermal discomfort cost in a long time horizon for sustainable smart home accounting for changes in home occupancy state, the most comfortable temperature setting, electrical consumption, renewable generation output, outdoor temperature, and the electricity costs.The employed algorithm creates and regulates four queues for indoor temperature, electric vehicle (EV) charging, and energy storage system (ESS).Extensive simulations are conducted to validate the employed algorithm. تعد جدولة الحمل، والتحكم في تخزين طاقة البطارية، وتحسين راحة المستخدم من المشاكل الحرجة لتحسين الطاقة في الشبكة الذكية. ومع ذلك، فإن مدخلات النظام مثل عملية توليد الطاقة المتجددة، وعملية توليد الشبكة التقليدية، وعملية شحن/تفريغ البطارية، وإشارات الأسعار الديناميكية، وعملية وصول الحمل تشمل أداء وحدة التحكم لتحسين جدولة تخزين طاقة البطارية في الوقت الفعلي بدقة، وجدولة الحمل، وتوليد الطاقة، وراحة المستخدم. وبالتالي، في هذا العمل، تم اعتماد تقنية تحسين Lyapunov القائمة على استقرار قائمة الانتظار الافتراضية (LOT) للتحقيق في تحسين الطاقة في الوقت الفعلي في منزل ذكي مستدام متصل بالشبكة مع حمل التدفئة والتهوية وتكييف الهواء (HVAC) مع الأخذ في الاعتبار ديناميكيات مدخلات النظام غير المعروفة. الهدف الرئيسي هو تقليل متوسط تكلفة الطاقة الإجمالية وتكلفة عدم الراحة الحرارية في أفق زمني طويل لحساب المنزل الذكي المستدام للتغيرات في حالة إشغال المنزل، وإعداد درجة الحرارة الأكثر راحة، والاستهلاك الكهربائي، وإنتاج التوليد المتجدد، ودرجة الحرارة الخارجية، وتكاليف الكهرباء. تقوم الخوارزمية المستخدمة بإنشاء وتنظيم أربع طوابير لدرجة الحرارة الداخلية، وشحن المركبات الكهربائية (EV)، ونظام تخزين الطاقة (ESS). يتم إجراء محاكاة مكثفة للتحقق من الخوارزمية المستخدمة.

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.