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Analysis and Simulation of DC Microgrid for Sustainable Energy Development

Analysis and Simulation of DC Microgrid for Sustainable Ener...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Kruthi Jayaram Vidya H A Department of Electrical & Electronics Engineering BNM Institute of Technology Bengaluru India Department of Electrical & Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

A Cutting edge modern day technology for the existing conventional power system is the idea of smart grid. To eradicate climate changes, market variations and security of power supply in near future smart grids help in restructuring the power structure. Smart grids are applied to low carbon energy emissions, electric vehicles, microgrid, renewable energy, flexible power system, distributed energy resources (DER’s), advanced metering infrastructure, fuel markets, customer support etc. In this paper, Analysis of DC Microgrid is implemented with distributed generation. On site generations like PV, wind and battery act as input sources and deliver output to the load. The proposed model is simulated in MATLAB Simulink and the results are verified for the operation of DC Microgrid using distributed energy resources. Results highlight upon the flow of power continuously using any one the input resources. The structure is implemented in MATLAB/Simulink and the results are tabulated.

关键词： Wind Software packages Microgrids Voltage Smart grids Batteries distributed power generation Topology Security MATLAB

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AI-Driven Network Effects and Tokenization for Next-Gen Communication Networks in Energy Systems

AI-Driven Network Effects and Tokenization for Next-Gen Comm...

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Mukul Singh Devesh Singh Pal Priyanka Yadav M.A. Ansari Vishwamitra Singh Vikas Beniwal School of Engineering Gautam Buddha University Gautam Budh Nagar India Dept. of Information Technology HIMT Group of Institutions Gautam Budh Nagar India Dept. of Computer Applications HIMT Group of Institutions Gautam Budh Nagar India Indian Institute of Management Kozhikode Kozhikode Kerela India UNSW Engineering University of New South Wales New South Wales Australia

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The decentralization of energy systems, driven by rapid adoption of renewable energy sources, has posed new challenges in communication, data sharing, and energy trading. Traditional grid structures rarely cope with the fast-changing dynamics of distributed generation resources (DERs), thus demanding smarter and scalable communication networks and mechanisms of synchronizing and optimizing operations. The set of technologies on AI-driven and tokenization-like approaches practically allows for the change. AI-driven networks support this by enabling real-time decision-making and predictive analytics through smart energy distribution and demand forecasting and massively automated grid optimization. Tokenization, reinforced by blockchain technologies, allows the decentralization of energy trading through transparent peer-to-peer (P2P) transactions that ease the encumbrances of the traditional utility. The paper focuses on how AI-driven networks and tokenization integrate to form the future communication networks in energy systems. It highlights their contribution to improving grid efficiency, securing grid infrastructure, diminishing operational costs, and democratizing energy markets. Additionally, it discusses technical challenges, regulatory barriers, and long-term implications while looking ahead and thinking about how these innovations can be used to reorganize energy infrastructures all over the globe and rapidly quicken the shift to decentralized sustainable energy ecosystems.

关键词： Technological innovation Renewable energy sources Tokenization Real-time systems Communications technology Peer-to-peer computing distributed power generation Blockchains Communication networks Predictive analytics

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A Bayesian Belief Network Framework for Protecting Generative AI-driven Attack in Smart grid Communication

A Bayesian Belief Network Framework for Protecting Generativ...

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international conference on computing, Networking and Communications (ICNC)

作者： Md Shirajum Munir Sravanthi Proddatoori Manjushree Muralidhara Sachin Shetty School of Computing Analytics and Modeling Univesity of West Georgia Carrollton GA USA Dept. of Computer Science Old Dominion University Norfolk VA USA Dept. of ECE Old Dominion University Norfolk VA USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

In the era of artificial intelligence (AI), native generative AI (GenAI) has become prominent in generating intelligent cyber-attacks in a smart grid environment by imitating operational parameters and metrics. As a result, it is imperative to understand the attack generation capabilities of native GenAI models, such as Generative Adversarial Networks (GANs) and Autoencoders in a smart grid environment. This research aims to explore the potential of GenAI models to accurately imitate and analyze the behavior of cyberattacks within a smart grid environment. The goal is to understand the risks posed by these intelligent attacks while developing strategies to protect against disruptive distributed Energy Resources (DER) events, such as incorrect load shifting, imbalanced demand supply, and unstable price forecasting. First, this work leverages two customized native GenAI models, namely GAN and Autoencoder, to synthesize DERs control message parameters such as nominal power consumption, price elasticity coefficients, and communication data packets, among others, to introduce highly unpredictable new intelligent attack vectors. Second, this research proposes and develops a new Bayesian Belief Networks (BBNs) framework by creating correlation dependency nodes among DER control message parameters to understand the risks of intelligent attack vectors better while monitoring and mitigating their impact on smart grid operations. In particular, by leveraging the customized BBN, the system can observe and analyze the uncertain behaviors in DER operations that GenAI introduces, and the proposed framework can help mitigate grid vulnerabilities by understanding the poisonous parameters through mutual information. Finally, the experiment results show that the Autoencoder outperformed GAN in reproducing intelligent attacks with an MSE about 98.984% lower than that of GANs. Additionally, BBN can explain prominent parameters of the intelligent attacks’ by quantifying dependencies thro

关键词： Generative AI Autoencoders Generative adversarial networks Vectors Data models Smart grids distributed power generation Bayes methods Mutual information Load modeling

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Hierarchical reinforcement learning for enhancing vehicle-to-grid operations in solar power systems

Hierarchical reinforcement learning for enhancing vehicle-to...

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international conference on Energy, Power, Environment, Control and computing (ICEPECC 2025)

作者： Muhammad Usman Sadiq Lucheng Hong Vikram Kumar Muhammad Ahsan Niazi Mian Rizwan Shafiq Ahmad School of Electrical Engineering Southeast University Nanjing People's Republic of China Industrial Engineering Department College of Engineering King Saud University Riyadh 11421 Saudi Arabia

ISBN: (数字)9781837243150

This study proposes a Hierarchical Reinforcement Learning (HRL)-based framework for optimizing Vehicle-to-grid (V2G) operations, with a focus on addressing solar power generation (SPG) forecast errors and facilitating efficient energy exchange between electric vehicles (EVs) and the grid. To tackle the inherent variability of solar energy and dynamic operational scenarios, the proposed approach formulates V2G operations as a multi-level decision-making process. High-level policies are designed to handle global objectives such as grid stability, while low-level policies focus on optimizing battery-level operations. Leveraging simulated SPG data and synthetic forecasts, the HRL framework provides a robust environment for training and evaluation, enabling a detailed exploration of critical parameters like blocking probability thresholds and discount factors. Results show significant reductions in Mean Squared Error (MSE) values and improved SPG forecast accuracy, highlighting the model’s adaptability to dynamic conditions. Training loss trends confirm convergence, and sensitivity analyses underscore the framework’s flexibility under diverse operational scenarios. The findings demonstrate the potential of HRL-based strategies to enhance smart grid efficiency by seamlessly integrating distributed energy resources (DERs), offering a scalable and sustainable solution for modern energy management systems.

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A Comprehensive Study for Application of Blockchain Technologies for the Decentralized grid Utilization Possibilities

A Comprehensive Study for Application of Blockchain Technolo...

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Intelligent Control, computing and Communications (IC3), international conference on

作者： Kamal Upreti Prakash Divakaran Vandana Mishra Chaturvedi Shitiz Upreti Prashant Vats Kamal Goyal Dept. of Computer Science CHRIST (Deemed to be University) Delhi NCR India Himalayan University Itanagar Arunachal Pradesh India D Y Patil Deemed to be University Navi Mumbai India Department of Management (MMIM) Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala Haryana India Dept. of CSE Manipal University Jaipur Jaipur Rajasthan India Marketing Pimpri Chinchwad University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This thorough research explores the world of blockchain technology and its significant effects on the use of decentralized grids. Decentralized networks have shown promise in addressing the rising need for renewable energy and effective resource management. Blockchain, a technology based on distributed ledgers, presents creative approaches to improve grid oversight, enable through peer-to- energy trade, and guarantee openness and protection in the management of the grid. This paper investigates the advantages and disadvantages of using blockchain in decentralized grids. From trading in renewable energy to grid optimization and demand response, we examine a variety of use cases and applications. We offer insight into the practical viability and scalability of blockchain-based solutions through a thorough analysis of real-world deployments and case reports. We also address the legislative and technological challenges to be solved before blockchain technology can realize its full potential in decentralized grid setups. Our research strongly emphasizes that regulatory architectures, seamless integration, and standardization all contribute to supporting the harmonious adaptation of blockchain technology worldwide. This study provides lawmakers, industry players, and investigators in the efforts to build a sustainable and effective energy future with an informative tool: it will shed light on the potential and constraints presented by blockchain technology in a decentralized environment of grid usage.

关键词： Ethics Renewable energy sources Technological innovation Supply chain management Law distributed ledger Smart contracts Medical services Blockchains Standards

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SQP based Predictive Controller for grid Parameters Optimization in distributed Generation System

SQP based Predictive Controller for Grid Parameters Optimiza...

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international conference on ent computing and Control Systems (ICICCS)

作者： Janagiraman. A Nivedha. G Gunapriya. S Vinisha Laxmi. G Kalaiyarassi. M Department of Electrical and Electronics Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The electrical grid is a complex system designed to balance electricity supply and demand, ensuring consistent power delivery to consumers. With the rising demand for energy and widespread adoption of renewable energy systems (RES), solar energy has emerged as a pivotal contributor to sustainable power generation. Integrating solar panels into the grid offers significant environmental benefits but also poses challenges due to the intermittent nature of solar irradiance. These fluctuations can cause voltage and current inconsistencies, potentially impacting grid stability and power quality. Furthermore, the presence of nonlinear loads intensifies the issue by introducing frequency variations, making effective grid management a pressing *** address these challenges, the present work aims to enhance grid performance through a Predictive Controller integrated with Sequential Quadratic Programming (SQP) optimization. This innovative approach dynamically adjusts system parameters in real time, compensating for fluctuations caused by varying solar irradiance and load conditions. By forecasting grid behaviour and optimizing control actions, the method ensures consistent and reliable power delivery. The project focuses on minimizing Total Harmonic Distortion (THD), current and voltage inconsistencies, enabling seamless solar integration into the grid while maintaining stability and power quality. Thus, the approach ultimately strengthens the grid's sustainability and resilience, supporting a clean energy future without sacrificing reliability or power quality for consumers.

关键词： Solar irradiance Total harmonic distortion Fluctuations Power quality Stability criteria Power system stability Power system reliability Quadratic programming Voltage control Optimization

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Unified Kill Chain Model for Executing MITM Attacks in Microgrids with DERs

Unified Kill Chain Model for Executing MITM Attacks in Micro...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Suresh Mogilicharla Tony Kondety Manoj Tripathy Mital Kanabar Electrical Engineering Department Indian Institute of Technology Roorkee India GE Renewable Energy GE Grid Solutions Markham Ontario Canada

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The increasing integration of networked distributed Energy Resources (DERs) into microgrids is expanding the attack surface of modern power systems, particularly in decentralized environments. This paper explores the use of the Unified Kill Chain (UKC) framework to model and execute a Man-in-theMiddle (MITM) attack in a microgrid, focusing on the various stages of the attack, from reconnaissance to the final impact on the Microgrid Energy Management System (MEMS). By leveraging the UKC, the study demonstrates how sophisticated attackers can exploit vulnerabilities in communication protocols like Modbus to manipulate critical parameters, such as the battery state of charge (SoC), leading to erroneous decision-making and undermining the operational reliability of the microgrid. The paper underscores the need for targeted defensive actions at each stage of the attack chain to detect, mitigate, and neutralize the intent of advanced persistent threats (APTs) against DER systems.

关键词： Micromechanical devices Protocols Power control Focusing Microgrids Reconnaissance distributed power generation Power system reliability State of charge Reliability

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Digital Twins for Predictive Maintenance in Renewable Energy grids with Tokenized Transactions: A Review

Digital Twins for Predictive Maintenance in Renewable Energy...

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Mukul Singh Vishvajeet Yadav Devesh Singh Pal M.A. Ansari Omveer Singh Vikash Patel School of Engineering Gautam Buddha University Gautam Budh Nagar India SOICT Gautam Buddha University Gautam Budh Nagar India Dept. of Information Technology HIMT Group of Institutions Gautam Budh Nagar India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

With the remarkable growth of the renewable energy sector, the management of distributed energy resources (DERs) like solar panels, wind turbines, and battery storage systems has become highly complex and increasingly significant. Conventional grid management methods are insufficient to handle the variability and unpredictability of renewable energy sources (RES). This research paper explores the integration of digital twins and tokenization as an innovative and creative solution to efficiently perform the operation, maintenance, and trading within renewable energy systems. Digital Twins are virtual models of physical assets, that enable real-time monitoring, predictive maintenance, and dynamic optimization of energy production. Real-time data from sensors is can be collected, analysed, and embedded in RES so that Digital Twins provides grid operators with important information about the performance and potential failures, which allows for efficient data-driven decision-making to enhance the energy output and grid stability. Tokenization, powered by blockchain technology, provides a decentralized and secure path for energy trading. Peer-to-peer (P2P) markets allow small-scale producers and consumers to trade energy directly using tokenized energy credits without relying on traditional utility companies. The combination of Digital Twins and tokenization enables a transparent, efficient, and resilient system where the extra generated energy is effectively traded, and assets are maintained at optimized performance levels. The research paper discusses the outcomes of this integration, particularly its efficiency to decrease its operational costs, improve energy efficiency, and contribute to global sustainability goals by encouraging the adoption of renewable energy sources. Through this technological merger, renewable energy integrated grids can become more adaptive, scalable, and sustainable, which will help to meet the increasing demand for clean energy.

关键词： Renewable energy sources Companies Tokenization Real-time systems Digital twins Peer-to-peer computing distributed power generation Sustainable development Optimization Predictive maintenance

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Precise Fault Distance Prediction in distributed Generators-Integrated Transmission Lines Using CNN-LSTM Ensemble Networks 4

Precise Fault Distance Prediction in Distributed Generators-...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Bais, Pavan Kumar Londhe, Narendra D. Electrical Engineering Department National Institute of Technology Raipur Raipur India

ISBN: (纸本)9798331530402

Precise fault distance estimation is critical for effective transmission system protection, especially in modern power systems with integrated distributed generators (DGs). As the global demand for energy continues to rise, the adoption of renewable energy sources and their integration into the grid has significantly increased. However, the presence of various DGs complicates fault distance estimation due to the diverse power flows and interactions between DGs and existing grid infrastructure. The integration of large-scale DGs introduces additional challenges in fault detection and location, as the dynamic behavior of these generators affects the fault characteristics. This paper focuses on precise fault distance estimation within the IEEE-9 bus system, which includes two distinct DGs. The proposed solution leverages an ensemble deep learning approach, combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This hybrid CNN-LSTM model takes advantage of CNN's ability to extract spatial features and LSTM's strength in capturing temporal dependencies, making it well-suited for fault distance prediction. The model's performance is evaluated using mean absolute error (MAE), giving 0.6387 average MAE for the studied system. Comparative analysis with existing deep learning models and validated through OPAL-RT 4510 real-time simulator shows that the proposed model delivers superior performance, demonstrating its effectiveness in handling the complexities introduced by DG integration. © 2025 IEEE.

关键词： Electric power system protection

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Novel Enhanced Astacus optimization algorithm for real power loss diminution in grid-connected renewable energy system 4

Novel Enhanced Astacus optimization algorithm for real power...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Kanagasabai, Lenin Dept. of EEE Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India

ISBN: (纸本)9798331530402

Enhanced Astacus optimization (EAOA) algorithm is applied for solving the real power loss diminution problem in grid-connected renewable energy system. Astacus optimization algorithm is scientifically defined through the behaviour of Astacus. Astacus optimization algorithm initializes the population inside the search region limits by means of homogeneously distributed arbitrary quantities. Mutation factor and crossover rate are rationalized when the iteration count are reached predefined level. During this period temperature value and decreasing parameter are modernized. The value of the temperature will define the algorithm to perform the contention and searching periods or to execute the mutation factor and crossover rate. Procedure appraises fresh locations and appraises the entities when enhancements are found. Global best location and fitness values are modernized in all iterations and process end when best location identified. Horse racing betting strategy algorithm is based on the betting stratagem in horse racing. In the direct betting, gambler will bet on the particular horse and if the horse wins the race then the gambler gets complete reward. Astacus optimization algorithm is hybridized with Horse racing betting strategy algorithm to augment the exploitation ability. Enhanced Astacus optimization (EAOA) algorithm is validated in7 Benchmark functions, IEEE 30, 57, 118 bus systems and grid 220 Kv system. © 2025 IEEE.

关键词： Mathematical programming

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