Energy Management System Research Papers (original) (raw)

2025, 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, FISTS 2011

One of the most important environmental problems in large cities is the vehicular emission. Electric Vehicles (EVs) are a growing alternative for internal combustion engine (ICE) vehicles. Since this kind of vehicle has low autonomy yet, it is important to optimize energy consumption, for instance by planning a suitable infrastructure of battery recharge and/or battery-switch stations. This paper presents an architecture for EV simulation, important to analyze traffic flow, its dynamics and the performance when there are obstructions or intense traffic. There are several tools for traffic simulation, SUMO (Simulation of Urban MObility) is one of them. But none of the existing traffic simulators integrates models of EV that allow, for example, perform simulation studies regarding energy consumption. SUMO is a portable open source simulator with multi-modal traffic feature capabilities that permit the simulation of various types of vehicles. This work is an extension of the SUMO, two-dimensional (2D) vehicular simulation package. To allow the simulation of energy consumption of EV, two extensions were incorporated in SUMO: EV models and modeling of altitude, transforming SUMO into a threedimensional (3D) simulator. The energy model effectiveness and correctness with 3D capabilities has been validated using two driving schedules (Urban Dynamometer Driving Schedule and Highway Fuel Economy Driving Schedule). This new tool will also support the study of better routes choice in 3D environment with EV aiming minimum energy consumption.

2025, Journal of Control, Automation and Electrical Systems

Metering systems planning in transmission networks can be modeled as an optimization problem in which the metering system cost is minimized, subjected to redundancy constraints that enable the state estimation function to observe system state and debug bad measurement data. However, the budget for investments in the metering system is commonly insufficient to achieve such a goal for the entire power system. On the other hand, there are areas of the power network considered more important for system operation than others. Consequently, a more qualified supervision of such areas is necessary to ensure adequate decision making regarding network operation and control. This work proposes a methodology flexible and easily adaptable to cope with limited investment budgets. An ant colony optimization algorithm (ACO-PCH) is employed to solve the optimal meter placement problem. A fast and cost-effective algorithm based on a decomposition strategy (ACO-PCH-DS) that takes into account the local nature of the state estimation problem is also proposed in order to improve computational efficiency of planning metering systems for large power networks. Tests with the IEEE bus systems and part of a real Brazilian system are carried out to validate the proposed methodology. If the stop criteria are the time limit (200 s), the ACO-PCH-DS reduces in 15% the cost of the measurement plan in IEEE 118-bus system, when compared to the ACO-PCH. For 300 min as a time limit, the ACO-PCH-DS do not provide a better cost only for reliability in the IEEE-300 bus system.

2025, DOAJ (DOAJ: Directory of Open Access Journals)

The drastic variations in energy demand have a significant impact on the operation of energy networks. The COVID-19 pandemic led to changes in electricity demand profile, directly affecting the efficiency and in some cases the stability of the systems. An overview of these outputs underscores the importance of making effective policy decisions to promote the transition to more sustainable energy systems. The goal is for the systems to be able to withstand the effects of threats like the, still ongoing, pandemic, of extreme weather phenomena which occur more frequently due to climate change and the of potential risk of a looming global energy crisis. The purpose of this article is to present methodologies for creating a safer and more sustainable energy system during extreme situations like a lockdown. The integration of distributed energy sources into the utility network paves the way for resilient urban grids and infrastructure. In this line of approach, a critical analysis of the energy management systems typologies and a SWOT/ PESTLE analysis to reveal the most important factors while managing an energy system, are presented. This analysis aids in the selection of the appropriate energy system by taking into account both internal and external factors, with a special focus on the social aspect in the context of resilience. The results indicate that, when permissible constraints allow, a centralized energy system is chosen to better deal with crisis scenarios, such as pandemic conditions.

2025, Applied Energy

Portal del coneixement obert de la UPC Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Applied Energy.

2025, IET Cyber-Physical Systems: Theory & Applications

This paper proposes and evaluates a predictive control model for management of the power flow in a hybrid microgeneration power plant with additional storage capacity. The plant integrates a photovoltaic array, a wind turbine, a diesel generator, and a lithium ion battery bank. One objective of the proposed predictive control model is to maximize the use of power from renewable resources looking for the weather predictions and thus minimize the use of fossil power from the diesel generator and correspondent CO2 emissions. Another aim is to maximize the duration of lithium ion batteries, since extending their lifetime is crucial for the system's economic viability, and since battery disposal brings environmental concerns as well. A numerical evaluation is performed about the evolution of power dispatch decisions and of the batteries state of charge, depending on the available power storage capacity. Model predictive control proves to be a suitable strategy in this system. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.

2025, SSRG international journal of electrical and electronics engineering

This article proposes the utilization of the Least-Square Support Vector Machine (LS-SVM) approach to ascertain the presence of a fault in power transformers. Power transformers are essential elements of electrical power systems. The failure of a power transformer can cause a disturbance in the functioning of power distribution and transmission systems. This situation will result in an increase in operating expenses due to the need for repairs and maintenance. The reliability of the electrical grid may be compromised. Therefore, it is crucial to identify any flaws in the power transformer at an early stage. In this paper, the LS-SVM utilizes Dissolved Gas Analysis (DGA) data as its input. The DGA methodology is widely accepted as the prevailing method for identifying the early stages of defects that arise in power transformers by analyzing the ratio of essential gases. The simulation data acquired from the industry comprises a standard state and six distinct fault types of transformers, which are utilized as input for the LS-SVM models. The suggested model underwent testing in multiple scenarios, yielding a maximum accuracy of 97.37%.

2025, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

This paper investigates the effectiveness of Proportional-Resonant (PR) controllers in Hybrid Renewable Energy
Systems (HRES), which integrate solar and wind components to optimize energy reliability and efficiency. The study addresses
the research question regarding the extent to which PR controllers enhance performance and efficiency in managing power
quality, system stability, and frequency-specific control in grid-connected applications. Experimental evaluations further validate
that variations in load conditions, such as those from RL loads, have minimal impact on output performance, underscoring the
robustness of the PR approach.

2025, Mathematical Problems in Engineering

e proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. e energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. e EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. e developed system is also accounted for degradation battery model and its associated cost. e incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.

2025

infrastructure. To overcome these problems, smart grid is the new solution which is more reliable, flexible and controllable. Home energy management system (HEMS) in the smart home allows the customer to control, optimize and monitor the energy consumption. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented. Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and reviewed. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. The home server monitors and controls the energy consumption and controls the home energy use to reduce the energy cost. The remote energy management server aggregates the energy information from the home servers, compares them and creates statistical analys...

2025, IEEE Access

An efficient energy management system for a small-scale hybrid wind-solar-battery based microgrid is proposed in this paper. The wind and solar energy conversion systems and battery storage system have been developed along with power electronic converters, control algorithms and controllers to test the operation of hybrid microgrid. The power balance is maintained by an energy management system for the variations of renewable energy power generation and also for the load demand variations. This microgrid operates in standalone mode and provides a testing platform for different control algorithms, energy management systems and test conditions. A real-time control is performed by rapid control prototyping to test and validate the control algorithms of microgrid system experimentally. The proposed small-scale renewable energy based microgrid can be used as a test bench for research and testing of algorithms in smart grid applications.

2025

As the globe moves rapidly towards achieving net-zero emissions goals, the year 2030 signifies a critical juncture in energy engineering. This paper introduces a forward-looking yet practical model of integrated sustainable energy ecosystems, driven by cutting-edge AI, next-generation renewable materials, and closed-loop manufacturing. By amalgamating advancements in ultra-efficient solar fabrics, dynamic wind-harvesting microgrids, and solid-state quantum batteries, we propose a smart, decentralized, and adaptive energy framework that can respond to real-time demands and climate variations. The research also investigates innovative bio-inspired materials such as photosynthetic graphene membranes-and eco-friendly manufacturing processes that reduce lifecycle emissions. By employing a hybrid approach that combines systems engineering models, hands-on prototypes, and predictive AI simulations, this study demonstrates how effective and scalable these innovations are in both urban and rural testbeds across Asia and Europe. The results indicate a remarkable 70% reduction in carbon-heavy waste streams, a 60% boost in storage density, and up to a 40% improvement in conversion efficiency. This research envisions a transformative shift: from passive sustainability to proactive ecological intelligence where energy systems not only fulfill human requirements but also enhance environmental health. The findings imply that by 2030, sustainable energy will no longer be an alternative but the intelligent standard, integrated into every aspect of infrastructure.

2025

The state-of-the-art Internet of Things (IoT) innovation known as the Autonomous Gas Reservoir Management System (AGaRMS) totally changes the most common way of overseeing gas supplies. AGaRMS uses sensors that monitor gas levels and a distributed architecture that forecasts patterns in gas use to ensure that gas refills occur at the right time. Complex AI calculations examine verifiable information, current weather patterns, and usage patterns to estimate when gas will run out. This, then, prompts robotized booking sales to be transported off the supplier. Likewise, AGaRMS appeals to the savvy progression of energy, which empowers strong gas usage. The framework's flexibility and versatility make it reasonable for use in private and business settings, further developing production network productivity, reducing working expenses, and improving the client experience. The AGaRMS system offers a smart method for managing gas leaders that work with gathering effective power energy practices and streamlining gas provisioning processes. AGaRMS optimizes the gas supply chain and improves the user experience by catering to a wide range of residential and industrial applications with its flexible and scalable design.

2025, TIDAC Research Symposium

Enhancing the resilience of microgrids involves implementing strategies and technologies that improve their robustness and reliability. Islanded microgrids, operating independently from the main utility grid, face significant challenges in maintaining a stable power supply, especially with the integration of intermittent renewable energy sources. This research proposes the implementation of a Fuzzy Logic Controller driven Battery Energy Storage System to enhance the resilience of such microgrids. By employing an advanced FLC, the study aims to dynamically adjust the operational modes of the microgrid based on real-time assessments of energy availability and demand. The methodology incorporates a Mixed Integer Linear Programming model to optimize the sizing and management of the BESS, facilitating improved energy distribution and reduced reliance on diesel generators. This approach is expected to enable more sustainable management of energy resources, significantly lower operational costs, and improve the environmental footprint of islanded microgrid systems. The integration of FLC with BESS represents a forward-thinking solution to the operational challenges faced by less infrastructure or independent energy systems, aiming to establish a more reliable, efficient, and adaptable energy management framework.

2025, IETE Technical Review

Improving energy efficiency in order to reduce CO 2 emissions is a permanent challenge in the European space. Smart metering could help for improving energy efficiency by offering information about the way in which the energy is used. Smart metering will be based on large volumes of sensor data, since energy monitoring will bring together sensor data from various critical areas. The main purpose of this paper was to present the selection mechanism for a scalable storage solution, based on the requirements of the DEHEMS (Digital Environment Home Energy Management System) project. With regular sensor readings coming at every 6 seconds, there is an impressive amount of data collected even for the minimal target of about 250 households, 10 sensors per user. With these huge data streams that are non-stationary time-series data, collected at discrete intervals, the DEHEMS project has to offer a solution for storing and retrieving sensor data in a responsive way. We have tested both collection speed and aggregation speed for reasonable data streams of sensor data. The tests were performed on various database models, with their associated representations, including relational databases, key-value stores, column stores, self-tuning databases, as well as time-series enabled database systems. These experiments confirmed that column stores and keyvalue stores perform better than relational databases, while time-series databases outperform all the others.

2025, International Journal of Electrical and Computer Engineering (IJECE)

The global imperative to transition towards sustainable energy sources has sparked innovative solutions for energy generation and environmental conservation challenges. As fossil fuel usage for power generation continues to raise environmental concerns, converting kinetic energy from vehicular motion via speed breakers presents a unique avenue for renewable power production. This study explores the concept of utilizing speed breakers as a means of electricity generation to power little power-consuming but critical load, with Covenant University serving as a pertinent case study. This research investigates the technical, economic, and environmental implications of implementing speed breaker-based electricity generation within Covenant University. Analyzing the university's energy consumption patterns showed that some loads do not require much power but are critical. Street lighting is one of such loads. This study discerns the potential contribution of speed breaker-generated electricity to address energy demands by simulation and constructing a prototype. Advanced engineering tools, such as simulation software Fusion 360 and Proteus 8.0, were employed to model and integrate the roller speed breaker mechanism with the electrical infrastructure. The findings offer valuable insights into the viability of speed breaker-generated electricity as an alternative energy source, paving the way for sustainable energy practices in educational institutions and beyond.

2025, TechConnect Briefs

Demand response (DR) has proven to show capabilities of providing ancillary services (AS) to grid operators. Advances in cloud computing and the availability of widespread and efficient network infrastructure bring tools that were otherwise inaccessible-financially or technologically-within reach. Building upon these advances, economically sound solutions to the established need for simulation testbeds for integration of distributed energy resources (DERs) systems into the power grid become available. This work presents methods of advanced power system modeling, integrated hardware design, and software development tools to develop a DR simulation testbed for grid stabilization in a power grid with a high presence of intermittent renewable generation. The result is a comprehensive package for internet of things hardwarein-the-loop simulation (iHILS) that was tested using DR aggregate control to provide stability to a grid with high integration of DERs.

2025, Scientific Programming

In this paper, a novel approach is presented for authorship identification in English and Urdu text using the LDA model with n-grams texts of authors and cosine similarity. The proposed approach uses similarity metrics to identify various learned representations of stylometric features and uses them to identify the writing style of a particular author. The proposed LDA-based approach emphasizes instance-based and profile-based classifications of an author’s text. Here, LDA suitably handles high-dimensional and sparse data by allowing more expressive representation of text. The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing, and the inherent ambiguity of the Urdu language. A large corpus has been used for performance testing of the presented approach. The results of experiments show superiority of the proposed approach over the state-of-the-art representations and other algorithms used for authors...

2025, International Journal of Power Electronics and Drive System (IJPEDS)

Solar PV's explosive expansion is changing distribution networks and posing new problems, such as bidirectional power flow, unstable voltage, and power quality problems, particularly in networks with low X/R ratios. Abrupt changes in voltage are difficult for conventional voltage control techniques like shunt capacitors and on-load tap changers (OLTCs) to handle. IEEE Standard 1,547 has little efficacy in such networks, despite the fact that PV inverters may provide reactive power. This paper suggests a real-time coordinated control approach to improve voltage regulation by combining PV inverters, OLTC, and battery energy storage systems (BESS). Reactive power from PV inverters is prioritized to lower operational expenses and reliance on BESS. Better voltage stability, a decrease in BESS energy processing from 9400.3 kWh to 1701.87 kWh, and a reduction in OLTC activities are the outcomes. Rural networks gain from the strategy's ability to support smaller, more affordable BESS units' voltage sensitivity analysis, and ideal BESS sizing may be investigated in future studies.

2025, Energetica

La gestión de la energía es una estrategia de control aplicada en las organizaciones que permite obtener una mayor eficiencia en el uso de energéticos. La NTC ISO 50001:2011 facilita a las organizaciones la implementación

2025, Elektronika ir Elektrotechnika

While home energy prices keep rising, homeowners nowadays are searching for the right options to reduce their electricity bills. Besides, the increase in power consumption can contribute to environmental pollution. Therefore, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, the most domestic household appliances consumption of energy are modelled and analysed using the fuzzy logic controller (FLC) in order to permit the home energy management system (HEMS) to perform energy utilization estimation and cost analysis. These appliances are the heating ventilation and air conditioning (HVAC), electric water heater (EWH), and lighting, respectively. The developed system can help to analyse the appliances’ energy consumption and cost sceneries during peak and off-peak hours. The modelling of a fuzzy-based domestic appliances controller for HEMS takes the peak and non-peak tariff of Malaysian ...

2025, International Journal of Engineering & Technology

Smart load management system with an advanced metering infrastructure operates to monitor the electricity consumption by the load and transferring data to the utility grid. It has direct benefit to the end-users by managing the load. This system has incorporated with home appliance for achieving the goal of home energy management system (HEMS) such as efficient energy utilization of house by avoiding the wastage. Efficient loading system can strengthen the efficient power utilization and thus can save the economy greatly. Air conditioner (AC), thermostat associated with a room were selected for this purpose as they have the high demand of electricity consumption. This study mainly focuses on developing the mathematical model and simulate it for the considered home appliances to assess the trend of electricity consumption. Research proved that, considering the ambient temperature developed model can provide the specific instructions for automatic controlling of the appliances which w...

2025, Power Systems Conference and Exposition, PSCE, IEEE

This paper is the second paper devoted to the contributions of Glenn W. Stagg to the advancement of the state- of-the-art in power system analysis, planning and operations. It provides a critical review and assessment of his work in the fields of: computer method development; faults and short circuit analysis; load-flow and stability computation techniques; and energy management systems. Also

2025, 2006 IEEE PES Power Systems Conference and Exposition

This paper describes the approach taken by Transelectrica S.A. to real-time stability assessment. Seamlessly integrated with the Real-Time Network Analysis sequence is a fast voltage and steady-state stability application that runs automatically after each successful state estimate. The calculations are performed both for the entire interconnected system of Romania and for the areas connected via "stability constrained links". The "stability constrained links" are transmission paths where the stability limits are more restrictive than the thermal limits. They are identified via off-line studies and are dynamically reconfigurable. The key results are posted on intuitive diagrams, including a real-time stability trending chart, which allow the operator to continuously monitor the distance to instability both on a system level and across critical area interfaces. This is a first, and very efficient, line of defense against blackouts. The next step consists of periodically evaluating what-if scenarios with comprehensive off-line stability tools.

2025, ISO Systems أنظمة الجودة

2025, International Journal of Applied Power Engineering (IJAPE)

The electric vehicle (EV) is increasingly emerging as an attractive solution to reduce reliance on fossil fuels in India. In commercial EVs, solar photovoltaic (PV) technology is employed both to charge the battery and power the vehicle. However, the conventional bidirectional DC-DC converter layout results in underutilization of solar PV power when the battery's state of charge (SOC) reaches maximum capacity. This work offers a unique dual input super boost (DISB) DC-DC converter designed specifically for solar-powered electric vehicles (EVs) to address the aforementioned challenge. The recently suggested converter operates in six different modes to effectively capture solar photovoltaic (PV) power. Notable benefits of this design include a wide range of speed control and fewer conduction devices in each mode, which eventually result in increased overall efficiency. An extensive analysis of the suggested DISB DC-DC converter is carried out by the study, encompassing detailed examination of operating waveforms and dynamic evaluations. Furthermore, the converter's performance and operation under the six different modes are verified through simulation.

2025, International Journal of Applied Power Engineering (IJAPE)

Nowadays renewable energy generation techniques and their application for home energy management are becoming very common topics of discussion all across the globe. The increased user comfort, bill reduction, and government subsidy schemes make more consumers interested in installing these sustainable sources in their homes. Also, the utility company will be able to level its peak load and reduce its carbon footprint. Does installing renewable energy sources in homes with a conventional billing scheme help in reducing the carbon footprint of the utility company? Also, are there chances for an increased trend of electric load addition in homes installed with renewable energy plants having net metering schemes to lead to peak load management burden? This paper is an attempt to underline the benefits of using renewable energy sources at home but at the same time what are the precautions to be taken while using the same in the state of Kerala, India. The paper also proposes an economical portable solar-powered light tower that helps in leveling peak loads in homes with on-grid power plants which are billed under a conventional block rate pricing scheme through net metering.

2025, Energy

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

2025, Energies

The current microgrid (MG) needs alternatives to raise the management level and avoid waste. This approach is important for developing the modern electrical system, as it allows for better integration of distributed generation (DG) and battery energy storage systems (BESSs). Using algorithms based on artificial intelligence (AI) for the energy management system (EMS) can help improve the MG operation to achieve the lowest possible cost in buying and selling electricity and, consequently, increase energy conservation levels. With this, the research proposes two strategies for managing energy in the MG to determine the instants of charge and discharge of the BESS. A heuristic method is employed as a reference point for comparison purposes with the fuzzy logic (FL) operation developed. Furthermore, other algorithms based on artificial neural networks (ANNs) are proposed using the non-linear autoregressive technique to predict the MG variables. During the research, the developed algorit...

2025, Journal of Applied Science and Technology Trends

The distributed energy system (DES) architecture is subject to confusion about renewable energy limits, primary energy supply and energy carriers' costs. For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential. The participation of end-users could influence the management of the system and the volatility of energy prices. By delivering auxiliary services using demand side-resource to increase system reliability, robust planning, constraint control and scheduling, consumers may support grid operators. The optimized approach to managing energy resources enhances demand response to renewable energy sources integrally, controls the demand curve with load versatility as the system requires it. The opportunity to adjust/regulate the charging profile by choosing a particular device. This article discusses a literature and policy analysis that looks at the role of energy management system aggrega...

2025

The hybrid fuel cell electric vehicle powered by household power during peak use is another opportunity to reduce emissions and save money. For this reason, Vehicle-toHome (V2H) and Home-to-Vehicle (H2V) systems were proposed as a new method of exchanging smart energy and a new method of exchanging smart energy. The main goal of this paper is to develop a smart home energy management based on IoT, generate more energy efficiency and share production between home and vehicle. In fact, the Hybrid Fuel cell electric vehicle will be used simultaneously to power household appliances during peak demand for electricity to solve energy consumption. The household's energy is derived from an accurate Autonomous hybrid power system. Several technologies such as Proton Exchange Membrane Fuel Cell, solar panel, Supercapacitor (SC) device and water electrolyzer are incorporated into the proposed system. Two-way electrical energy from the PEMFC-Hybrid Electric Vehicle and household power will ...

2025

Fast and accurate measurement of the instantaneous average active power (IAAP) is useful for building Energy Management System (EMS) in order to assure quality of service such as continuity, optim ize energy consumption and reduce carbon dioxide emission. In this paper the problems connected to the measurement of the instantaneous average active power for energy usage improving are discussed, also as the operational processes which deal to solve such prob lems.

2025

The rapid increase in electricity demand has led to innovative peak demand control processes, notably Vehicle-to-Grid (V2G) technology. V2G enables electric vehicles (EVs) to both draw power from and release stored energy back to the grid, making EVs mobile energy storage units. Traditional peak load management strategies are insufficient for modern energy demands, requiring comprehensive methodologies for effective dynamic peak load management using EVs in island mode microgrids. Integrating renewable energy sources enhances grid stability and sustainability. V2G technology's bidirectional power exchange allows EVs to balance demand and supply while supporting grid stability. This research focuses on maximizing efficiency, managing peak load demand, and improving grid stability with V2G technology, transforming EVs into energy management assets. By using EV batteries to meet peak demand, reduce fossil fuel-based power plant expenses, and lower generation costs, the study aims to optimize energy utilization and island-mode microgrid stability in a simulated city under various conditions.

2025

2025, International Journal of Power Electronics and Drive Systems (IJPEDS)

Planning and management of distribution networks has become a very difficult task, especially with the strong expansion of renewable energy sources (RES) which are intermittent in nature. Maintaining fluidity and reliability of real-time decisions while taking into consideration uncertainties related to production and increasing the profit of distribution network operators is the objective of the system proposed in this work. It is an intelligent energy management system dedicated to the management of grid-integrated RES and battery energy storage systems (BESS), composed of: i) a real-time control and data acquisition model, ii) a model for forecasting the intermittent parameters of RES based on neural networks, iii) a long-term planning model based on the optimal placement and size of RES and BESS, and iv) an hourly planning model for scheduling the energy distribution between energy sources. The non-dominated sorting genetic algorithm and the entropy-TOPSIS method (technique for ...

2025, IJNRES

The reliability and safety of power distribution systems heavily depend on the continuous and effective operation of power transformers. Conventional maintenance methods often fail to predict failures in real-time. This paper presents an intelligent Transformer Health Monitoring System (THMS) that integrates TensorFlow-based deep learning models with fuzzy logic to analyze and predict the condition of transformers. Real-time data acquisition is performed using sensor networks that monitor key parameters such as oil temperature, winding temperature, load current, and dissolved gas concentrations. TensorFlow is employed to train a deep learning model for anomaly detection, while a fuzzy logic controller interprets the criticality of abnormalities based on expert rules. The hybrid approach enhances accuracy, interpretability, and timely response, providing an efficient diagnostic system for proactive transformer maintenance.

2025, IJNRES

The increasing global demand for sustainable and efficient energy systems has intensified the
development of advanced computational techniques for optimizing power systems, electric vehicle (EV)
design, and solar photovoltaic (PV) power extraction. This paper presents a comprehensive review and
application of fuzzy logic and hybrid metaheuristic algorithms for addressing critical challenges in
distributed generation (DG) allocation in restructured power systems, structural design optimization for
electric vehicles, and maximum power point tracking (MPPT) in solar PV systems. The integration of fuzzy
logic with metaheuristic algorithms such as bat–grasshopper optimization and bat–modified multiverse
optimization enhances the adaptability and accuracy of conventional methods. Simulation results and
comparative analyses demonstrate the efficacy of the proposed approaches in improving system efficiency,
stability, and energy harvesting capabilities

2025, IJNRES

The rapid evolution of smart grid technologies necessitates innovative approaches for managing energy resources, especially in the face of growing electric vehicle (EV) adoption and the integration of distributed generation (DG). Fuzzy logic and edge computing have emerged as powerful tools for addressing these challenges by enabling intelligent decision-making and real-time system optimization. This paper proposes a synergistic framework that leverages fuzzy logic and edge computing to optimize DG allocation and enhance EV longevity in smart grids. A comprehensive discussion is presented on system design, implementation methodology, simulation results, and practical applications. The outcomes demonstrate significant improvements in energy efficiency, system reliability, and EV battery health.

2025, IJNRES

As electric vehicle (EV) adoption surges, ensuring efficient, accessible, and reliable charging infrastructure becomes essential. Fast Charging Stations (FCSs) play a pivotal role in alleviating range anxiety and supporting the growth of EVs. This study tackles the complex issue of determining optimal FCS locations and sizes, focusing on minimizing total costs-investment, operation, maintenance, power loss, and reliability costs. Using Particle Swarm Optimization (PSO), a powerful optimization algorithm, this research offers a framework to effectively evaluate and optimize FCS placement and sizing, accounting for variables like EV demand, grid constraints, and station capacity. A comprehensive simulation was conducted on India's northeastern 203-bus test system using Power Factory DIgSILENT software, validating the method's effectiveness. Results show the proposed PSO-based model not only reduces overall costs but enhances grid reliability by incorporating the Energy Not Supplied (ENS) index to measure reliability costs. This approach underscores a viable pathway for strategically placing FCSs to achieve sustainable, cost-effective EV charging infrastructure.

2025, IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society

In the field of microgrids with a significant integration of Renewable Energy Sources, the efficient and practical power storage systems requirement is causing DC microgrids to gain increasing attention. However, uncertainties in power generation and load consumption along with the fluctuations of electricity prices require the design of a reliable control architecture and a robust energy management system for enhancing the power quality and its sustainability, while minimizing the associated costs. This paper presents a mixed approach illustrating both simulation and experimental results of a grid-connected DC microgrid which includes a photovoltaic power source and a battery storage system. Special emphasis is placed on the minimization of the total operating cost of the microgrid while considering the battery degradation cost and the electricity tariff. Thereby, an optimal energy management system is proposed for Energy Storage Systems scheduling and enabling the minimization of the electricity bill based on simple models. Simultaneously, the differences between simulation and laboratory performances are highlighted.

2025, Algerian journal of signals and systems

This paper focuses on discussing an energy management system (EMS) for a smart microgrid integrating multiple renewable sources. The task of the EMS is to efficiently balance power generation and consumption by controlling various energy sources, including photovoltaic systems, energy storage units, engine generator set and the utility grid. An EMS optimizes power flow between the microgrid components and keeps the micro-grid stable, by using different control strategies. In this microgrid, the PV system serves as the primary energy source, while the other sources of electrical energy act as backups. The energy management system prioritizes supplying load demand from either the PV system or the other sources, with load shedding during peak hours if the supplied energy is insufficient. The approaches based on StateMachine and StateFlow are discussed in this paper for enhancing the energy management system's performance.

2025, 11th International Automotive Technologies Congress OTEKON 2024

The growing need for sustainable transportation solutions has led to significant advances in fuel cell hybrid vehicles (FCHVs). A critical aspect of FCHV performance is the energy management strategy (EMS) used to optimize the power distribution between the fuel cell, battery and electric motor. This paper presents a novel rule-based energy management strategy designed to improve the efficiency and longevity of fuel cell hybrid vehicles. The proposed strategy leverages a set of predefined rules to manage the energy flow, prioritizing the operation of the fuel cell within the optimal efficiency range, while effectively utilizing the battery to meet transient power demands and regenerative braking. Simulation results show that the rulebased EMS significantly improves fuel economy and reduces hydrogen consumption. This research contributes to the wider adoption of FCHVs in the transportation sector by providing a robust framework for developing practical EMS solutions.

2025

Based on proven and robust technology (Power Line Communication system), we have developed and tested in a real demonstrator a home automation control solution applied to smart lighting and appliances remote control. This technological solution is robust, reliable, economical and perfectly suited to developing countries. The design of power Line carrier communication system based on FSK-KQ330 module has two main advantages: a simple hardware circuit, which makes the module easy to design, manufacture, and maintain. In addition, a low cost, which makes the module affordable for a wide range of applications. The results obtained in real conditions made it possible to prove the feasibility and effectiveness of this remote control device for lighting and household appliances.

2025, American Journal of Electrical Power and Energy Systems

Renewable energy resources are being used to overcome energy shortage. From the point of view of energy management the interconnectivity of electric utility with renewable energy resources is difficult. Renewable energy resources need to be managed with electric utility. A Smart Energy Management System (SEMS) is designed and developed for monitoring an efficient load management of electric utility and photovoltaic power system is presented in this research. The design consists of an Energy Management Center (EMC) and Field Programmable Gate Array (FPGA). Energy Management Center shows the runtime data and also keeps the data log and offers control of the load shifting between utility source and photovoltaic power system. Analog to digital converter is used to interface the current and voltage sensors with FPGA. ZigBee is connected for wireless radio data transmission between the FPGA and energy management & monitoring center. The SEMS increases the efficiency of energy up to 10.5 percent in comparison with normal systems.

2025

Photovoltaic systems convert solar irradiance into electricity. Due to some factors, the amount of solar irradiance arriving at the solar photovoltaic collector at a specific location varies. The goal of this study was to develop a mathematical model for predicting the performance of a photovoltaic system, which depends on the amount of solar irradiance. A novel model for solar irradiance in the form of a delay differential equation is introduced by including the factor of delayed solar irradiance, hour angle and the sun's motion. The simulation study is carried out for the three scenarios of weather conditions: a clear day, a slightly cloudy day, and a heavily overcast day. The numerical solution is obtained by adopting the 4 th-order Runge Kutta method coupled with a parameter fitting technique, the Nelder Mead algorithm, which is implemented by using MATLAB software. The data from a solar plant in Pahang, Malaysia, was used for model validation and it is found that the prediction profile for solar irradiance aligns well with the intermediate and decay phases, but deviates slightly during the growth phase. The output current and power for the solar photovoltaic panel were treated as time-dependent functions. As the solar irradiance increases, the output current and power of the solar panel will increase. The result showed that the maximum output current and output power of STP250S-20/Wd crystalline solar module decreased by 42% and 76%, respectively, during slightly cloudy and heavily overcast conditions when compared to clear days. In other words, the performance of a photovoltaic module is better on clear days compared to cloudy days and heavily overcast. These findings highlight the relationship between delayed solar irradiance and the performance of the solar photovoltaic system.

2025, Applied Sciences

This work focused on prescribing, designing, implementing, and evaluating a pilot project conducted in the Greek power system that addressed balancing and congestion management issues that system operators (SOs) face within the clean energy era. The considered pilot project fully focused on the development of the F-channel platform, including the idea behind this application, the steps that were taken in the process, and the outcomes of the performed activities fitting into the overall picture of the OneNet project. The specified F-channel platform is a web-based, client-server application that uses artificial intelligence (AI) techniques and cloud computation engines to improve the management of the active power for the TSO-DSO coordination. The flexibility of the grid’s resources was identified, and an integrated monitoring system based on the precise forecasting of variable generation and demand was implemented. The focus areas were congestion management, frequency control, and v...

2025, Journal of Renewable and Sustainable Energy

This paper proposes a fuzzy expert system for demand-side management, management of renewable energy sources, and electrical energy storage for smart households and microgrids. The proposed fuzzy expert system is used for automatic decision making regarding energy management in smart microgrids containing renewable sources, storage systems, and controllable loads. The fuzzy expert system optimizes energy consumption and storage in order to utilize renewable energy and maximize the financial gain of a microgrid. In order to enable energy management, the fuzzy expert system uses insolation, price of electrical energy, temperature, wind speed, and power of the controllable and uncontrollable loads as input variables. These input data can be directly measured, imported from grid measurements, or predicted using any data prediction method. This paper presents fuzzification of input variables, defines a set of rules of the expert system, and presents defuzzification of outputs. The output...

2025

Energy storage systems (ESSs) can help to reduce the intermittency and uncertainty of renewable energy supplies in power systems. ESSs are critical components of renewable-rich standalone microgrids (SMGs) to balance power generation and load demand, which is referred to as reliability. To achieve the same level of reliability as conventional power systems for renewable-based SMGs, significant investment in ESSs is required. However, due to the high investment costs of ESSs, the installation of large ESSs will not result in an affordable solution for achieving renewable SMG at the required reliability. As a result, this paper proposes a new sharing concept for ESS, namely energy storage as a service (ESaaS), to be implemented across microgrids as a low-cost alternative for improving reliability. In the proposed ESaaS concept, microgrids can use ESS from an ESS provider as required for different timeframes such as monthly, weekly, or daily, depending on the renewable resources and load profile characteristics. In this paper, the use of ESaaS is investigated over a range of timeframes for a 100 % renewable-based SMG with photovoltaic (PV), wind turbine (WT), and ESS. The SMG reliability is evaluated using Monte Carlo simulation both before and after the ESaaS strategy has been implemented. To evaluate the ESaaS affordability in improving the reliability of an SMG, this paper proposes the criteria of marginal cost of reliability, which indicates the rate of additional investment amount per percentage of reliability improvement. The marginal cost of reliability combines the economic and technical aspects of ESaaS in one simple criterion for effective decision-making among investment strategies such as different timeframes of ESaaS or permanent ESS. The simulation results show the ESaaS based on daily contract results in a lower marginal cost of reliability for the case study. To validate the effectiveness of the proposed ESaaS approach using marginal cost of reliability, the levelized cost of electricity (LCOE) is also calculated for different strategies of reliability improvement. The results confirm that the lowest LCOE is obtained using the strategy that provides the lowest marginal cost of reliability for the case study. In addition, a sensitivity analysis is performed to assess the difference in marginal cost of reliability under various uncertainties associated with the installed capacity of PV and WT, and the cost of utilising the ESaaS.

2025, 2012 IEEE International Symposium on Sustainable Systems and Technology (ISSST)

2025, International Journal of Electrical Power and Energy Systems (IJEPES)

Renewable energy-based microgrids are emerging as a sustainable solution to meet the growing energy demands while reducing dependence on fossil fuels. This paper presents an advanced energy management system (EMS) for microgrids integrating renewable energy sources such as solar and wind. The proposed EMS optimizes power generation, storage, and consumption using intelligent algorithms to ensure reliability, cost-effectiveness, and grid stability. A hybrid approach combining demand-side management, battery energy storage, and real-time load forecasting is implemented to enhance efficiency. Simulation results demonstrate the effectiveness of the system in minimizing energy wastage, reducing carbon footprint, and ensuring uninterrupted power supply. The study highlights the potential of renewable energy-based microgrids in achieving a sustainable and resilient energy future.