Optimization of Vehicle-to-Grid Technology for Enhancing Microgrid Stability with Renewable Energy Integration
Graph Neural Network-Based Prediction of Output Waveform in a Single-Phase Matrix Converter
Optimizing Electric Vehicle Charging Infrastructure: A Location Analysis Approach using Domination Theory
Study of Virtual Quantitative Alarm Parameters of Improved Fault Diagnosis in Power Grids using Fuzzy Logic
Life Cycle Cost Analysis of Building Integrated Photovoltaic Thermal (BIPVT) Systems: A Review
Power Sector Reform in India: Strategies, Private Sector Role, and Technological Transformation
Multi Area Load Frequency Control of a Hybrid Power System with Advanced Machine Learning Controller: Case Study of Andhra Pradesh
A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies
Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions
Design of a Grid Connected PV System and Effect of Various Parameters on Energy Generation
Comparative Analysis of Harmonics by Shunt Active Filter using Resonant Current Control in Distribution System
Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI
Development of Power Flow Controller for Grid Connected Renewable Energy Sources Using Lyapunov function
Detection and Location of Faults in Three Phase 11kv Underground Power Cables By Discrete Wavelet Transform (DWT)
Design of PV-Wind Hybrid Micro-Grid System for Domestic Loading
Applications of Artificial Neural Networks in various areas of Power System; A Review
In this research paper, the integration of Vehicle-to-Grid (V2G) technology within a microgrid framework, emphasizing its role in enhancing grid stability through renewable energy optimization, was explored. The increasing penetration of electric vehicles (EVs) and renewable energy sources, such as photovoltaic (PV) farms and wind farms, presents both opportunities and challenges for modern power grids. To address these, we developed a comprehensive model simulating a 24-hour operation of a microgrid incorporating an 8 MW PV farm, a 4.5 MW wind farm, and a diesel generator. The model also includes residential and industrial loads, along with 100 EVs equipped with 40 kW batteries, capable of contributing 4 MW back to the grid. The simulation, conducted in MATLAB/Simulink, examines the dynamic interactions between these components under various conditions, including partial shading effects on the PV farm and wind farm tripping due to high wind speeds. The results demonstrate the effectiveness of V2G technology in balancing power generation and consumption, particularly during peak load periods. By optimizing the timing of EV charging and discharging, we show a significant reduction in the dependency on nonrenewable energy sources, thereby enhancing the overall stability and efficiency of the microgrid. The findings highlight the potential of V2G systems to serve as a crucial element in future energy systems, facilitating the seamless integration of intermittent renewable energy sources while supporting grid frequency regulation and reducing greenhouse gas emissions. This study provides valuable insights for optimizing microgrid operations and underscores the importance of V2G technology in achieving sustainable energy goals.
This paper presents a Graph Neural Network (GNN) model designed to predict the output voltage waveform of a single- phase matrix converter (SPMC) functioning as a rectifier under the Sinusoidal Pulse Width Modulation (SPWM) technique. By representing the circuit topology as a graph-with electrical components as nodes and their connections as edges-the model captures the underlying physical interactions within the converter. This graph-based framework enables the network to accurately learn and replicate the behavior of the system, delivering precise waveform predictions. The proposed approach demonstrates both high accuracy and computational efficiency, making it well-suited for converter design validation and performance forecasting.
The rapid global shift towards sustainable transportation underscores the urgent need for efficient electric vehicle (EV) infrastructure. A major barrier to widespread EV adoption is the irregular and inadequate distribution of charging stations. This paper introduces a graph-theoretic model, termed auto charge domination, to optimize the placement of EV charging stations across transportation networks. By modeling roads as graphs—with vertices representing cities and intersections, and edges representing road segments—we apply domination theory to determine the minimum number of strategically located charging stations. A new parameter, the auto charge domination number, is introduced to ensure that all locations, including pendant vertices (minor or unsurveyed roads), remain within 200 km round-trip distance of a station. The methodology involves constructing graphs from highway layouts, ensuring all nodes are covered within reach, and analyzing the auto charge domination number for classical graphs such as path, cycle, Petersen, and bipartite graphs. Our results reveal that fewer, strategically placed stations can provide comprehensive coverage, improving infrastructure cost-efficiency without compromising accessibility.
Fault diagnosis in power grids is essential for ensuring uninterrupted and reliable electricity supply. Traditional approaches rely on expert systems, fuzzy logic, and machine learning, but they often underutilise the quantitative aspects of alarm information generated by monitoring systems. This paper proposes a novel fault diagnosis framework that integrates quantitative alarm data with machine learning techniques to enhance the accuracy and efficiency of fault identification. The proposed approach demonstrates improved diagnostic performance, offering greater reliability for modern power systems.
The building-integrated photovoltaic thermal (BIPVT) system can be considered a powerful and versatile technology that is being used to meet the growing demand for energy in the modern world. Nowadays its use is not only limited to urban areas, but due to its affordable and pollution-free nature, it is becoming popular among rural people as well. Since it is much cheaper than the systems associated with energy-producing grids in the traditional way, nowadays its popularity is not limited to industrial applications; nowadays it is also used in residential buildings for solar heaters and other applications in the winter season. This paper reviews the current state-of-the-art technology of BIPV, including BIPV Foil, Tile, Module, and Solar Sale Glazing products. AI techniques such as artificial neural networks (ANN), fuzzy logic, and machine learning are being used to customize energy production, improve thermal management, and predict system behavior. This review paper has analyzed cost analysis for the proposed system based on various factors. Life cycle cost analysis is calculated based on the initial and current costs spent during the use of PV modules. Various PVs are used worldwide. Technology has been analyzed, and conclusions have been made about material and savings based on it. Building Integrated Photovoltaic Thermal (BIPV/T) systems combine solar photovoltaic with thermal energy recovery, allowing building elements (such as roofs or facades) to generate both electricity and useful heat. Life cycle cost analysis (LCCA) evaluates the total cost of these systems over their operational lifespan-including initial investment, operation, maintenance, component replacement, and end-of-life costs-providing insight into long-term economic feasibility.
Electricity is a fundamental necessity for modern life and is increasingly recognized as a basic human right. The Indian power sector is undergoing substantial reforms; however, the demand for electricity continues to exceed its supply. This persistent gap is attributed to challenges in generation, transmission, and distribution, as well as inefficient energy use and inadequate financial investment. High technical and commercial losses, coupled with the absence of a business-oriented approach in utility management, have resulted in unsustainable operations. According to the 2025 Census, approximately 18% of Indian households still lack access to reliable electricity.
