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Editor(s)-in-Chief: Albert Lam, Emeritus Editors-in-Chief and Ghazanfar Safdar

EAI Endorsed Transactions on Energy Web is an open access, peer-reviewed scholarly journal focused on cross-section topics related to IT and Energy. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a continuous frequency.

INDEXING: Scopus (CiteScore: 2.2), Compendex, DOAJ, ProQuest, EBSCO, DBLP

  • Editors-in-Chief
  • Albert Lam, University of Hong Kong, HK
  • Area Editors
  • Mikhail Medvedev (Southern Federal University, Russia)
  • Vladimir Dyo (University of Bedfordshire, UK)
  • Andrey Mazalov (South Federal University, Russia)
  • Elena Gebel (Omsk State Technical University, Russia)
  • Irina Semykina (T. F. Gorbachev Kuzbass State Technical University, Russia)
  • Vyacheslav Potekhin (Peter the Great St. Petersburg Polytechnic University, Russia)
  • Sabu Thampi (Indian Institute of Information Technology and Management-Kerala (IIITM-K), India)
  • Manik Sharma (DAV University, India)
  • Amando P. Singun JR. (University of Technology and Applied Sciences, Muscat, Oman)
  • Irina Semykina (Sevastopol State University, Ukraine)
  • Abel C. Chen (Chunghwa Telecom Laboratories, Taiwan)
  • Vitor Monteiro (University of Minho, Portugal)
  • Joao L. Afonso (University of Minho, Portugal)
  • Abdul Shabeer (IBM India Pvt Ltd, Chennai, India)
  • Anandakumar H (Sri Eshwar College of Engineering, India)
  • Marta Chinnici (ENEA, Italy)
  • Howard NJOKU (University of Nigeria, Nigeria)
  • José Gabriel Oliveira Pinto (University of Minho, Portugal)
  • P Krishna (VIT University, India)
  • Jacinth Poornima (Hindusthan College of Engineering and Technology, India)
  • Karthikumar S (Anna University)
  • Duckki Lee (Yonam Institute of Technology, South Korea)
  • A. Hnatov (Kharkiv National Automobile and Highway University, Ukraine)
  • Dr. P. Karthikeyan (Jain University, India)
  • Sagheer Abbas (National College of Business Administration and Economics, Pakistan)
  • Ahmed Elngar (Beni-Suef University, Egypt)
  • Debnath Bhattacharyya (KL University, India)
  • Jatinder Manhas (University of Jammu, India)
  • Michele Mastroianni (University of Salerno, Italy)
  • Hitesh Mohapatra (KIIT University, India)
  • Brojo Kishore Mishra (NIST Berhampur, India)
  • Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
  • Sudhakar Sengan (PSN College of Engineering and Technology, India)
  • Haris M. Khalid (University of Dubai, United Arab Emirates)
  • Emeritus Editors-in-Chief
  • Ghazanfar Safdar, University of Bedfordshire, UK

  • Energy management system design for high energy consuming enterprises integrating the Internet of Things and neural networks

    Appears in:
    ew 25(1):

    Authors:
    Zhiping Zhang, Zhaolin Wang

    Published:
    2nd May 2025

    Abstract:
    INTRODUCTION: High energy consuming enterprises continue to pay increasing attention to energy consumption. Therefore, designing an energy management system is significant. OBJECTIVES: To improve the management level and economic benefits of enterprises, a high energy consuming enterprise energy management system design based on Internet of Things technology and neural network algorithms is proposed. METHODS: Internet of Things devices are used for data collection and transmission. The combination of neural network model prediction and optimization algorithms can achieve real-time monitoring, prediction, and optimization control of energy consumption. RESULTS: The research results indicated that the response time of the high energy consuming enterprise energy management system proposed in the study was 80.2 ms when the number of people was 600. The fluctuation range of CPU usage within 24 hours was 14% to 45%. CONCLUSION: A high energy consuming enterprise energy management system that integrates the Internet of Things and neural networks can manage energy more efficiently and intelligently, thereby improving the production efficiency and economic benefits of the enterprise. This helps companies gain greater advantages in fierce market competition.

  • Power optimization and control strategy for new energy hybrid power generation system based on deep learning

    Appears in:
    ew 25(1):

    Authors:
    Fei Li

    Published:
    29th Apr 2025

    Abstract:
    The continuous shortage of non-renewable energy and the increasingly serious environmental pollution have made clean renewable energy represented by wind and solar energy become the focus of attention. This paper mainly studies the power optimization and control strategy of a new energy hybrid power generation system based on deep learning. This paper introduces the basic principle and structure of a new energy hybrid power generation system and the application of deep learning technology in power optimization and control strategy. In this paper, a power optimization method based on deep learning is proposed, which realizes real-time optimization of power generation system powers by training neural network models. A control strategy based on deep learning is designed to improve the stability and efficiency of the power generation system. The effectiveness of the proposed method in practical application is verified by experiments.

  • Investigation of the anchor chain tension distribution and six-degree-of-freedom motion characteristics of a floating fan platform in a wind-wave basin

    Appears in:
    ew 25(1):

    Authors:
    Jie Dong, Yupeng Wang, Jiankang Wang, Xi Zhang, Guoqiang Chen, Jingyi Wei, Guirong Lu

    Published:
    24th Apr 2025

    Abstract:
    INTRODUCTION: Because of its versatility and adaptability, floating wind turbine platforms have emerged as the go-to foundation type for deep sea wind power as offshore wind power production steadily expands into deeper waters. OBJECTIVES: But the floating platform's six-degree-of-freedom motion and the anchor chain system's mechanical reaction to the combined force of wind, wave, and current are incredibly intricate, and this directly affects the platform's stability and safety. METHODS: Under various wind, wave, flow incidence angle, and chain length conditions, the platform's six-degree-of-freedom motion characteristics and anchor chain tension distribution are carefully studied using potential flow theory, the finite element method, and the fluid-structure coupling model. The numerical simulation combined JONSWAP wave spectrum and NPD wind spectrum to conduct multi-condition analysis. RESULTS: the results show that the incidence Angle and anchor chain configuration have significant effects on the dynamic response of platform pitching and pitching. CONCLUSION: This paper deeply discusses the platform motion response under the broken anchor chain, and puts forward the corresponding optimal design scheme.

  • Intelligent Equipment Scheduling Optimization Model for Transmission Lines Based on Improved BFO Algorithm

    Appears in:
    ew 25(1):

    Authors:
    Zhi Wang, Yangliang Zheng, Ning Wang, Xin Zhang, Fuchun Zhang, Wulue Zheng

    Published:
    22nd Apr 2025

    Abstract:
    INTRODUCTION: In modern power systems, the optimization of intelligent equipment scheduling for transmission lines is a key task. OBJECTIVES: To improve the effectiveness of scheduling optimization, this study introduces an intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm. METHODS: This model achieves global and local search capabilities through an improved Bacterial Foraging Optimization algorithm, maintaining the diversity of equipment states and effectively improving the optimization level of scheduling results. RESULTS: At 3000 iterations, the model was able to reach its optimal state, and its optimization results showed excellent performance in terms of convergence and uniformity, which was very close to the optimal solution. In practical applications, the performance of the intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm is also excellent. The average line usage rate of the scheduling scheme proposed by the model reached 70.69%, while the average line usage rate of the manual scheduling scheme was only 64.63%. In addition, the optimal relative error percentage of this model is less than 2.1%, while the BRE of other algorithms reaches around 10%. CONCLUSION: The intelligent equipment scheduling optimization model for transmission lines on the ground of improved Bacterial Foraging Optimization algorithm has important practical significance for improving the operational efficiency of the power system, reducing operating costs, and making sure the stable and reliable operation of the power system.

  • Modeling and simulation of control strategy for voltage source converter multi-terminal DC system

    Appears in:
    ew 25(1):

    Authors:
    Xingchao Zhang

    Published:
    17th Apr 2025

    Abstract:
    An improved strategy for controlling DC voltage droop is proposed to solve the problems of conventional DC voltage droop control strategy, such as the received power of converter stations is distributed according to fixed proportion, the power variation amplitude is large, the power regulation ability is easy to lose when reaching the upper limit, and there is obvious voltage deviation. A three-terminal VSC-MTDC system model including wind farm was constructed by Matlab/Simulink. According to different conditions of power fluctuation caused by wind power output variation, simulation and analysis of VSC active power and DC voltage variation were carried out. Simulation results showed that the improved strategy for controlling DC voltage droop proposed in this paper made the converter station retain more power regulation margin when the converter station absorbed more active power, and could prevent the converter station from losing its power regulation ability when the power reached the upper limit, and realize reasonable power distribution among converter stations. Meanwhile, the proposed control strategy could maintain DC voltage constant, which verified the feasibility of its application in wind power grid-connected flexible DC transmission system.

  • Cloud model-based unconventional risk assessment method for flexible distribution system

    Appears in:
    ew 25(1):

    Authors:
    Yi Liang, Na Zhang, Peng Ye, Shaotao Guo, Qixiang Wang, Jinhang Shan, Zhentao Han

    Published:
    16th Apr 2025

    Abstract:
    The integration of high-penetration distributed resources has led to increased complexity and uncertainty in the unconventional risks of distribution networks, posing higher demands on the risk assessment of distribution networks. This paper proposes an unconventional risk assessment method for flexible distribution system based on cloud model. Firstly, an unconventional risk assessment system for distribution networks is constructed by considering the probability of unconventional risk occurrence and the severe consequences, and an improved AHP-entropy weight method for index weighting is proposed. Then, the cloud model for risk assessment is used to quantitatively evaluate the risk level of the distribution system. The variable weight cloud model is employed to replace the traditional cloud model to provide risk indicator evaluation information. The inverse cloud generator is used to infer and correct the risk cloud model parameters, and the assessment is completed by comparing with the digital characteristics of the standard cloud model. Finally, the effectiveness of the proposed assessment method is verified through an example analysis of a certain region in China.

  • Research on multi-stage optimization planning of power internet of things based on seagull optimization algorithm

    Appears in:
    ew 25(1):

    Authors:
    Huan Wang, Mingli Zhang, Yi Zhao, Shaotao Guo, Peng Ye, Shuo Yang, Guanxian Liu

    Published:
    15th Apr 2025

    Abstract:
    The Power Internet of Things (PIoT) is a significant technology for realizing the transformation of future energy systems, with the Integrated Energy System (IES) playing a crucial role in realizing the value of PIoT. Traditional IES planning methods typically focus on a single-stage planning approach and involve complex solution models, often resulting in inefficient equipment configurations and resource wastage. This study proposes a multi-stage IES planning method aimed at enhancing both energy efficiency and the economic performance of IES. The method models the IES based on electric, gas, and thermal buses, considering the coupling, storage, and conversion of multiple energy sources. A range of constraints, such as energy coupling, equipment capacity, and energy purchases, are considered. The planning cycle is divided into multiple stages, and an economic model is developed that accounts for both system investment and operating costs. Given the complexity of the multi-stage planning model, the Seagull Optimization Algorithm (SOA) is introduced to solve the problem. The SOA leverages its strong global and local search capabilities to determine the optimal capacity configuration at each stage. The comparison of the single-stage planning method by a calculation example proves the economic advantage of the multi-stage planning scheme and effectiveness of SOA.

  • Research on short-term power load forecasting based on deep reinforcement learning with multiple intelligences

    Appears in:
    ew 25(1):

    Authors:
    Jinglong He, Jinming Liu, Yuan Fu, Dunlin Zhu, Sheng Yang, Tianyun Luo

    Published:
    14th Apr 2025

    Abstract:
    A reliable supply of power systems is critical for industry, commerce, and residential life. Improving the accuracy and reliability of short-term electricity load forecasting plays a crucial role in ensuring the satisfaction of electricity demand and the stable operation of the power system. Therefore, to realize accurate and efficient prediction of short-term power loads, a short-term power load prediction method based on multi-intelligence deep reinforcement learning is proposed to address the complex nonlinear characteristics of load data. In this paper, we analyze the multi-intelligence application architecture in power load forecasting, and analyze the function of each intelligent unit applied to short-term power load forecasting; based on clarifying the interaction relationship of each intelligent unit in short-term power load forecasting, we model short-term power load forecasting as a distributed and partially observable Markov decision-making process, which is suitable for multi-intelligence deep reinforcement learning; based on the MATD3 algorithm, a centralized training-distributed execution framework is used to train multiple intelligences within the model to achieve short-term power load forecasting. The experimental results show that in the August short-term electricity load forecasting using the design method, the obtained MAE value is 35.94 kW, MAPE value is 4.05%, and RMSE value is 32.71 kW. In the short-term power load forecasting evaluation conducted for December, the average absolute error (MAE) value obtained was 36.75 kilowatts, the average absolute percentage error (MAPE) value was 4.51%, and the root mean square error (RMSE) value was 34.82 kilowatts. These evaluation results fully demonstrate that the design method adopted has high prediction accuracy and forecast precision. This method has demonstrated good practical value and broad application prospects in practical applications due to its high-precision prediction performance and strong prediction stability.

  • Distributed energy storage hierarchical partition dispatch control of virtual power plant based on SaDE-BBO algorithm

    Appears in:
    ew 25(1):

    Authors:
    Tianyi Yu, Ning Sun, Shijia Wei, Zhipeng Zhang, Tao Lu

    Published:
    14th Apr 2025

    Abstract:
    To improve the response ability of the virtual power plant during operation and the adjustment ability when the load fluctuates, and ensure its stable operation, a virtual power plant distributed energy storage hierarchical partition dispatch control method based on the SaDE-BBO algorithm is proposed. This method is based on the operation structure of the virtual power plant, analyzes the operating characteristics of the distributed energy storage system and the output of uncertainty factors, considers the grid load, renewable energy and distributed energy storage on the time scale, and constructs hierarchical partitions of the virtual power plant. The dispatch model determines the day-ahead and day-in-day hierarchical partition dispatch control objective functions, and sets corresponding constraints; the dispatch control model based on the solution of the SaDE-BBO algorithm outputs the virtual power plant distributed energy storage hierarchical partition dispatch control optimization plan. The test results show that the maximum load peak value after dispatch control through this method is 40.9 MW; the active power loss results are all below 10 MW, real-time response to control instructions ensures the safety and stability of the voltage of the virtual power plant under the access of renewable energy, and the nodal voltage fluctuated within the permissible range of 0.95 to 1.05 p.u.

  • Power grid inspection based on multimodal foundation models

    Appears in:
    ew 25(1):

    Authors:
    Jingbo Hao, Yang Tao

    Published:
    14th Apr 2025

    Abstract:
    INTRODUCTION: With the development of large foundation models, power grid inspection is transmitting from traditional deep learning to multimodal foundation models. OBJECTIVES: This paper aims to boost the application of multimodal foundation models for power grid inspection. METHODS: Current research on foundation models and multimodal large language models (LLMs) is introduced respectively. Three application forms of multimodal foundation models in power grid inspection are explored. The reliability of these models is discussed as well. RESULTS: These techniques can significantly reduce the time and cost of inspection by automating the analysis of large amounts of sensor data. They can also improve the accuracy and reliability of inspection by leveraging the understanding and reasoning abilities of LLMs. CONCLUSION: These advanced techniques have shown great application potential in power grid inspection. But it is important to note that they should not entirely replace human inspectors who can validate automatic findings and address possible issues not captured by these models alone.

  • The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

    Appears in:
    ew 23(1):

    Authors:
    Srishti Dikshit, Vinay Dwivedi, Aarushi Thusu, Mohammad Shahid, Areeba Atiq

    Downloads:
    1314

    Abstract:
    The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

  • A Short Literature on Linear Programming Problem

    Appears in:
    ew 23(1):

    Authors:
    R. Kumar, Shubham Kumar Tripathi

    Downloads:
    1126

    Abstract:
    Researchers and scientists have developed various approaches and methodologies over time to model and analyze different types of linear programming problems, such as assignment problems and parametric programming problems. This paper provides a critical review and classification of existing modelling approaches and solution methods related to linear programming problems. Moreover, the simplex method is discussed in detail through a comprehensive literature review. The paper concludes by presenting an integrated research framework that is directly applicable to the present context, along with suggestions for future research directions.

  • Green Economic Policies, Strategies & Initiatives of India

    Appears in:
    ew 23(1):

    Authors:
    Ajit Singh, Monica

    Downloads:
    893

    Abstract:
    Sustainable economic development is crucial to secure human welfare and eliminate social inequality. Global leaders have recently made progress towards reaching an agreement on how to convert the current unsustainable economic trends into sustainable green economic growth. The viability of the green economy depends on several variables, including governmental policy, the business climate, and environmental concerns. The presented paper examines the implementation of green economic policies and analyses government strategies and initiatives taken by the government at the national level. The study uses a descriptive-analytical approach. The study finds India is putting many regulations and initiatives into place to encourage the effective use of energy in a variety of economic areas, including green building, equipment, farming, mobility, and fuels. These green growth initiatives lower the economy's carbon intensity and provide a sizable number of green jobs.

  • Platformization and the Metaverse: Opportunities and Challenges for Urban Sustainability and Economic Development

    Appears in:
    ew 23(1):

    Authors:
    Aram Mohammed-Amin Qadir, Ava Omar Fatah

    Downloads:
    817

    Abstract:
    In simpler terms, our day-to-day life, from various urban sectors to all deep corners of city life, is becoming hugely influenced by digital platforms' data systems, economic tactics, and ways of management. This is a trend that we call "platformization." It's taken us to a point where we now live in what's often described as a "platform society" because these platforms now largely control urban civilizations. What's fascinating is that this platformization trend has created something pretty striking: the Metaverse. The Metaverse is an impressive global platform project launched by Meta, the company we used to know as Facebook. This project brings to life a potential "virtual world" that mirrors our reality. The idea is that the Metaverse can serve as a virtual version of the future cities – not too different from what we think of as smart cities. Thanks to cutting-edge technologies like Artificial Intelligence, Big Data, Internet of Things (IoT), and Digital Twins, we now have enough resources and understanding of human behavior to make a project like the Metaverse possible. The promise is that the Metaverse can revolutionize how we design cities and deliver public services, making cities more efficient, accountable, and with a higher quality performance. But of course, the arrival of the Metaverse isn't without its worries. There are many questions over the ethical, human, social, and cultural implications the Metaverse may have. Particularly, there are concerns about the kind of impact it may have on the quality of human social relationships and how it may reshape urban life. To unpack all of these, this research work aims to thoroughly examine available literature on this topic. The paper further looks into the new products and services coming into being because of the Metaverse, examining how they might help smart cities, especially those aiming for better environment, economy, and social sustainability. The insights gathered here could help city leaders understand the Metaverse's potential for technology-driven urban practices and future city plans. It also takes a critical stance, challenging whether the Metaverse might significantly change how reality is constructed in our increasingly platform-driven urban world. This discussion, hopefully, can fuel future research and critical conversation on this hot topic.

  • The Advancement of Electronic Gadgets in 21st Century

    Appears in:
    ew 23(1):

    Authors:
    Divjot Singh, Radha R, Arjun Vishanth, Nirav Bhatt

    Downloads:
    695

    Abstract:
    The 21st Century was full of innovation when it came to the Tech Sector alone, be it the introduction of the Iphone, or the launch of the big conglomerates such as Facebook and Twitter. The 21st Century gave rise to the concepts of the Curved Displays on phones and the Foldable phones. There were numerous problems which were associated with the flat screen displays, and the shortcomings have been overcome by the Curved Displays and the Foldable phones as discussed in the paper. They have revolutionized the Technological market since their launch. Many Conglomerates such as Samsung, Honor, Xiaomi have invested millions of dollars in availing this new technology. However, currently Samsung has the lead over all its Chinese competitors, and with different companies investing in the new technology, the competition is predicted to become more intense. This will surely give customers a huge variety of phones to choose from. However, this technology did not come without its drawbacks. There were multiple issues associated with the Foldable Phones such as the weight associated with the phone when folded, the tensile strength of the Foldable phones is very low, and the User Interface is not something one could get excited about. Similar issues existed with Curved Displays as well, including costly repair costs, and it is harder to get a grip on compared to the Foldable phones due to the thinning of the edges. The paper discusses in depth the shortcomings of all the types of Phones, as well as which would be the best fit for the user, along with the current trend of the users, and what the Future holds for this segment of Technology.

  • Analysis of Artificial Intelligence Design Incorporated into Design of Street Space in Small Cities

    Appears in:
    ew 24(1):

    Authors:
    Lei Song

    Downloads:
    654

    Abstract:
    INTRODUCTION: At present, China's urbanization continues to develop, but the level of urban planning and design has not kept pace with the progress of development, especially the creation of street space in small cities, which is still not able to adapt to the current needs of people's cultural life. With the development of the economy and science and technology, artificial intelligence and other information technologies are constantly integrated into daily life. Therefore, utilizing artificial intelligence for street space design research is significant. OBJECTIVES: To improve the development level of artificial intelligence, information technology, and the application level of artificial intelligence in China; to solve the problem of the relatively low level of street space design in the current city, especially in small towns, to meet the needs of people's cultural life; to promote the improvement of the level of artificial intelligence in China for the convenience of civil life, and to further improve the level of street space design in China. METHODS: Firstly discusses the theories, such as the principles of street space design in small cities, to illustrate the necessity of integrating AI into street space design in small towns; secondly, the model established by AI is used to optimize the street space in small cities; finally, the feasibility of integrating AI design into street space design in small towns is illustrated through cases and examples. RESULTS: Street space design in small cities in China needs more design aesthetics, and artificial intelligence needs to be integrated into street space design. Artificial intelligence has a noticeable optimization effect on street space design, and using artificial intelligence can improve the design level of limited space. Also illustrates that artificial intelligence can be better integrated into the street space design of small cities through cases. CONCLUSION: In the context of the digital era, the street space design of small cities needs to apply artificial intelligence. In modernization-oriented urban construction, the street space design level of small towns should be further strengthened to improve the modernization level of cities.

  • Thermodynamic Simulation of Producer Gas Combustion from Biomass Gasification

    Appears in:
    ew 22(5): e1

    Authors:
    Fajri Vidian, Arif Rahman Hakim

    Downloads:
    592

    Abstract:
    The use of new and renewable energy for the application on gas burners is beneficial for the environment. This study aims to determine the effect of excess air during the combustion of producer gas from biomass gasification to release heat used as a fuel gas burner. Simulations are carried out by applying mass and energy balance, showing that an increase in excess air will decrease the non-adiabatic and adiabatic flame temperature. The result showed that an increase in excess of air reduces the amount of heat released to the environment for the same flame temperature. The maximum adiabatic flame temperature is at 1725.430C, while the non-adiabatic ranges from 600 to 8000C. Furthermore, heat is released in the range of 20.1 kW to 28.8 kW, and excess air from 0 to 40%. 

  • Advancing Climate Modeling through High-Performance Computing: Towards More Accurate and Efficient Simulations

    Appears in:
    ew 24(1):

    Authors:
    Alok Gaddi, Sendhilkumar Manoharan, Prasad Kulkarni

    Downloads:
    567

    Abstract:
    A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.

  • State-of-the-art review on energy management systems, challenges and top trends of renewable energy based microgrids

    Appears in:
    ew 24(1):

    Authors:
    JinZhu Cui, Yefan Wu, Caiyan Liu

    Downloads:
    546

    Abstract:
    Due to the increasing demand for electrical energy worldwide and environmental concerns, modern power systems are looking for a fundamental change. These changes include reducing dependence on the primary electricity grid and using renewable energy sources on a large scale. The emergence of microgrids in electrical energy systems will improve the level of these systems due to technical, economic, and environmental benefits. In this research work, the authors have conducted extensive studies on control methods, types of power sources, and the size of microgrids and analyzed them in tabular form. In addition, the review of communication technologies and standards in microgrids, as well as the review of microgrid energy management systems to optimize the efficiency of microgrids, is one of the main goals of the authors in this article. Also, in this article, the top 10 trends of microgrids in 2023 have been examined to increase the flexibility of network infrastructure, which helps readers to improve their strategic decisions by providing an overview of emerging technologies in the energy industry.

  • Adaptive FPA Algorithm based OPF with Unified Power Flow Controller

    Appears in:
    ew 22(40): e4

    Authors:
    Nageswara Rao Atyam, Challa Babu, Immanuel A., Pavan Kumar Naidu R., Sudheer P.

    Downloads:
    523

    Abstract:
    In this work a novel modified flower pollination algorithm has been developed to solve the problem of single and multi-objective Optimal Power Flow operations for Unified power Flow Controller in Flexible Alternating Current Transmission Systems. In the proposed Adaptive Flower Pollination Algorithm the best initial solution can be chosen from the fittest and also the weights are adaptively adjusted to get better convergence characteristics. The nature of the objective functions is non-linear and difficult to get best possible solutions within the boundary conditions of total power demand. The weak nodes are determined in the system to locate the UPFC with Fuzzy approach considering input parameters as L-Index and voltage magnitudes. The projected method is validated using IEEE-30 and IEEE-57 bus systems for three objective functions, namely, system real power loss minimization, fuel cost minimization and the combination of total generating cost and system real power loss. Results of Fuzzy- Adaptive Flower Pollination Algorithm based OPF optimization for UPFC produced optimum results for the considered objectives of total fuel cost, real power loss and for the multiobjective.
Publisher
EAI
ISSN
2032-944X
Number of Volumes
12
Last Published
2024-11-04
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