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Assessment using MCDM approach of renewable hybrid energy system with hydrogen production capability

International Journal of Hydrogen Energy 2025年 137卷 448-460页

作者： Gaurav, Avinash Tyagi, Arjun Jha, S.K. Kumar, Bhavnesh Department of Electrical Engineering Netaji Subhas University of Technology New Delhi110078 India Department of Electrical Engineering Faculty of Technology University of Delhi Delhi110007 India Department of Instrumentation & Control Engineering Netaji Subhas University of Technology New Delhi110078 India

Researchers worldwide are exploring renewable hybrid energy systems that integrate battery energy storage systems and hydrogen production technology to combat climate change. The study presents a novel hydrogen production method that integrates a biomass reformer with an electrolysis system, offering a sustainable, cost-effective solution for clean hydrogen generation, addressing biomass waste management, and contributing to climate change mitigation. This study aims to design an optimal renewable hybrid energy system (RHES) with battery storage and hydrogen production capability for powering rural communities in Bihar, India. Three different battery energy storage systems (Lithium-ion, Lead-acid, Vanadium Flow) are examined using HOMER Pro software. An investigation carried out on techno-environment-economic perspective to assess the designed configuration on excess energy generation, cost of energy, net present cost, cost of hydrogen and CO2 emission. The MCDM strategy reveals that vanadium flow-based RHES is the optimal, sustainable and feasible solution. © 2025

关键词： Battery storage

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Economical Demand-Side Management with Distributed Energy Resources

Economical Demand-Side Management with Distributed Energy Re...

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IEEE Region 10 Symposium

作者： Vishal Sachin Kumar Harsh Kumar Sagar Gaurav Alok Agrawal Department of Electrical Engineering Netaji Subhas University of Technology New Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

A potent technology for demand response is proposed in this paper. The proposed algorithm is believed to bring significant monetary increments and improvements in grid stability, especially within the emerging context of renewables as well as Electric Vehicles (EVs). This research presents an innovative strategy for finding residential participants with load profiles alone for maintaining grid stability, which is useful in data-scarce countries like India. The proposed algorithm aimed at maximizing customer and grid benefits. Additionally, the adoption of Distributed Energy Resources (DERs) will make Demand Side Management (DSM) strategies more effective, thus underlining their significance in molding a sustainable energy environment. More importantly, the study contributes to the knowledge of economic DSM practices outlining DER integration for future energy resilience and sustainability.

关键词： Energy consumption Renewable energy sources Demand side management Shape Power system stability Demand response Distributed power generation Particle swarm optimization Sustainable development Resilience

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Predictive Modeling and Sensitivity Analysis for Averting Nanoparticles Agglomeration in Metal Matrices

SSRN

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SSRN 2024年

作者： Gupta, Sanjay Tevatia, Abhishek Rana, K.P.S. Department of Mechanical Engineering Netaji Subhas University of Technology Delhi110078 India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology Delhi110078 India

Nanoparticles agglomeration has been a crucial factor in restricting their potential improvements in metal matrix nanocomposites (MMNCs), since last decades. Though their extraordinary properties have proven applicability of MMNCs for diversified applications, agglomeration of nanoparticles resulting in poor dispersion and distribution in matrices restricts their competence. Various experimental investigations have proven the opportunities for reducing the nanoparticles agglomeration and proposed theoretical factors behind the agglomeration tendency of dispersed nanoparticles. Present work offers a predictive model for predicting nanoparticles agglomeration. Particularly, the influence of nanoparticle agglomeration on yield strength of MMNCs has been investigated through a yield strength prediction model by considering various strengthening mechanisms and degradation factor, which quantifies the porosity. It is worth mentioning that the nanoparticle agglomeration causes interfacial stress concentration and poor dispersion of load which is mainly responsible for an overall reduction in the yield strength. Two distinct agglomeration factors and the sensitivity analysis provides significant insights into changes in parameters and their influence on the predictive model. The proposed model has been validated with other available models and experimental data. Obtained results also revealed that the yield strength is significantly influenced by the volume fraction and size of the nanoparticles. © 2024, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Improved Grey Wolf Optimization Abetting Fuzzy Logic controllers Used for Load Frequency control in Two-Area Interlinked Reheat Thermal Power Plants applying Governor Dead Band and Generation Rate Constraint Non-Linearity

Improved Grey Wolf Optimization Abetting Fuzzy Logic Control...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shasya Shukla Shyam Kant Jha Instrumentation and Control Engineering Department Netaji Subhas University of Technology New Delhi India

The need of a fast, effective and economical controller for load frequency control has been established in this paper. The primary aim is to conduct an investigation into the effectiveness of P.I.D. and FUZZY LOGIC controller applied to load frequency control (LFC) of interlinked power system with non-linearity by applying optimization. As it is already known when certain input variables and the related output variables do not have a linear or proportional connection, this is referred to as non-linearity in power systems. In other words, changes in input parameters do not always correspond to changes in output parameters in a straight proportion. Instead, the system’s behaviour displays nonlinear traits, which can make analysis and control more difficult and complicated. In order to preserve system stability, efficiency, and the capacity to maintain a constant frequency and voltage, dealing with non-linearity in power systems is crucial. Fuzzy logic, optimization, and adaptive control are examples of sophisticated modelling and control approaches that engineers and researchers frequently use to better understand and handle non-linear phenomena and guarantee the stable functioning of power systems. Considering the domain of global optimization and engineering design challenges, the Improved Grey Wolf Optimization (I-GWO) offers a highly effectual and sturdy approach. This improvement seeks to solve some of the drawbacks of the conventional Grey Wolf Optimizer (GWO) method while applying it to a system having GRC and GDB non-linearity.

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Frequency Regulation of Multi-Source Power System Through Aggregated Thermostatically controlled Loads

Frequency Regulation of Multi-Source Power System Through Ag...

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Silchar Subsection Conference (SILCON), IEEE

作者： Sweta Gupta V S K V Harish Rajneesh Sharma Department of Electrical Engineering Netaji Subhas University of Technology New Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

The use of thermostatically controlled loads like inverter air conditioners (IACs) is widespread and can be leveraged for frequency regulation services. This paper focuses on load frequency control (LFC) for single area multi-source system comprising thermal, hydro and gas sources. The proportional integral (PI) and fractional order PI (FOPI) controllers are proposed using the particle swarm optimization (PSO) algorithm. The PSO based PI/FOPI strategy determines controller parameters by optimizing ITAE performance index. MATLAB/SIMULINK results demonstrate the significant improvements in overall system dynamics. The robustness of the proposed controllers is assessed under non-linear conditions, including generation rate constraints (GRC), governor dead band (GDB) and time delays. The PSO-FOPI controller exhibits superior robustness and dynamic response among the considered controllers.

关键词： PI control Power system dynamics Power system stability Robustness Inverters Nonlinear dynamical systems Thermal loading Particle swarm optimization Frequency control Thermal stability

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control of PV Based Water Pumping System Optimized Using Neural Network

Control of PV Based Water Pumping System Optimized Using Neu...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Saritraj Shubham Kumar Vikhyat Dhingra Aman Dahiya Anuradha Tomar Electrical Engineering Netaji Subhas University of Technology Instrumention and Control Engineering Netaji Subhas University of Technology

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The objective of this study is to enhance the efficiency of a solar-powered water pumping system through the integration of advanced control methodologies. The study focuses on harnessing the potential of photovoltaic (PV) panels by employing Maximum Power Point Tracking (MPPT) with incremental conductance method to maximize power extraction under varying irradiance conditions. The irradiance values obtained from the solar panels are utilized as inputs to a neural network (NN) predictive model, which accurately forecasts power output. The predicted power values are then transformed into reference speeds and employed within a field-oriented control (FOC) framework to regulate the operation of the induction motor (IM). This integrated approach aims to enhance the efficiency and reliability of the system by dynamically adjusting motor speed based on real-time irradiance fluctuations and mitigating the impact of changing environmental conditions. The findings contribute to the advancement of control strategies for renewable energy systems, particularly in remote agricultural applications where reliable water supply is crucial for sustainable development.

关键词： Maximum power point trackers Induction motors Fluctuations Heuristic algorithms Artificial neural networks Predictive models Real-time systems Steady-state Solar panels Sustainable development

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4th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2022

作者： Mukhesh, R.S.V. Soni, Aashnna Amrita Vishwa Vidyapeetham Electronics and Communication Engineering Amritapuri India Netaji Subhas University of Technology Instrumentation and Control Engineering India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Machine learning, simply put, is the practice of using computer programs to learn from data without hard-coded instructions. One use for this type of AI computation is 'similarity matching' - identifying how two texts might be similar. Advanced natural language processing technologies can now be used on a large scale to identify similar sentences when processed by Apache Spark and other open-source frameworks on top of distributed file systems such as HDFS, Hadoop Distributed File System (HDFS). Basic sentence similarity matching techniques are more appropriate for large scale tasks where limited resources are expected but overtime they would provide more accuracy. This paper will be focused on how to setup the training of specific question pairs on Apache Spark, which can allow algorithms to learn patterns in close matrices. The authors apply clustering algorithms, Apache Spark, and machine learning algorithms in this research to develop a model that evaluates whether two questions are the same in a dataset. This method yielded a 70% accuracy using relatively simple methods. © 2022 IEEE.

关键词： Machine learning

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Cost Optimization of EV Charging Station Integrated with Solar PV

Cost Optimization of EV Charging Station Integrated with Sol...

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Prabhat Srivastava Alok Agrawal Prerna Gaur Department of Electrical Engineering Netaji Subhas University of Technology New Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Mobile charging stations (MCS) consist of vehicle equipped with battery banks and rapid chargers. The idea of using static charging station is to refuel electric cars. These charging stations are scheduled and routed to meet the needs of electric vehicle users who are heterogeneous in terms of geography and time. In order to meet the requirements of mobile charging stations, charging requests deadlines, cost estimation, acceptance of both the user etc. conditions should be required. Also, to optimize the problem to turn down the cost of charging, it should require to get the solar enabled charging station that provide surplus of energy at the peak hours and increase the reliability of system. Numerical findings demonstrate that the approach can significantly lower the cost of charging at peak hours and off peak too and also improving customer satisfaction by improvising the charging strategy. Solar based MCS have the potential to significantly contribute to the acceleration of EV adoption by offering charging services based on the location and time-agnostic.

关键词： Costs Estimation Charging stations Real-time systems Batteries Solar system Smart grids

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Analysis of Bifacial Photovoltaic Panel Under Different Reflective Surfaces

Analysis of Bifacial Photovoltaic Panel Under Different Refl...

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IEEE International Conference on Power Electronics, Intelligent control and Energy Systems (ICPEICES)

作者： Harit Kathuria Ikjot Singh Aditya Gupta Gaurav Puniya Bhavnesh Kumar Instrumentation and Control Engineering Department Netaji Subhas University of Technology Dwarka New Delhi India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387209

With developments in bifacial solar panels, the solar photovoltaics interest has started shifting from monofacial designs to bifacial solar panels. This paper analyses and compares the performance between a bifacial and a monofacial solar panel system. The bifacial design offers different performance characteristics based on the reflective properties of the base material. This paper reviews and compares the system's performance on various reflective surfaces. This study employs a comparative outlook on performance measures such as current, voltage, power and thermal outputs. Taking the global energy crisis into consideration, solar panels prove to be an important replacement for the conventional ways of electricity generation and bifacial solar panels improve the efficiency of a conventional solar panel. Therefore, this study is anticipated to serve as a guide for integrating bifacial photovoltaic systems in general use, increase its efficiency and also promote research in this domain.

关键词： Temperature dependence Voltage measurement Power measurement Reviews System performance Electricity Power electronics Solar panels Sun Intelligent control

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A review of PV power forecasting using machine learning techniques

Progress in Engineering Science

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Progress in engineering Science 2025年第1期2卷

作者： Gupta, Manvi Arya, Archie Varshney, Uday Mittal, Jishnu Tomar, Anuradha Electrical Engineering Netaji Subhas University of Technology New Delhi India Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

This paper reviews the application of Machine Learning (ML) techniques in Photovoltaic (PV) power forecasting. As solar energy becomes a prominent renewable energy source, accurate prediction of PV power generation is crucial for efficient grid integration. The paper begins by highlighting the challenges of integrating solar power into the grid due to the dynamic nature of solar energy production. ML techniques offer a promising solution for overcoming these challenges. The methodology section details the systematic literature review process employed in the paper. This includes establishing research questions, formulating search strings, applying inclusion and exclusion criteria, and data analysis steps. The core of the paper dives into a review of existing literature on ML techniques for PV forecasting. The review explores various categories of ML algorithms used, including deep learning models, hybrid approaches, and mathematical models. Further, it examines the growth of PV forecasting research over time, the classification of literature based on deployed methods, and the different forecast horizons considered. The paper also explores the diverse input parameters utilized for accurate PV power forecasting. Upon the systematic review, the potential future research directions have also been explored like forecasting with real-time data and integration with grid management systems. © 2025 Elsevier B.V.

关键词： Machine learning Photovoltaic Power forecasting Renewable energy Solar power grid integration

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