Article
Engineering, Electrical & Electronic
Hamidreza Momen, Ahad Abessi, Shahram Jadid, Miadreza Shafie-khah, Joao P. S. Catalao
Summary: Recent natural disasters have emphasized the importance of enhancing power systems resilience through dynamic microgrid formation using distributed energy resources. Electric vehicles with V2G and G2V capabilities, as well as plug-in hybrid electric vehicles with high-powered engine-generators, offer new possibilities for restoring critical loads during outages. The paper introduces a new method for load restoration in mesh networks using a mixed-integer linear programming approach, demonstrated through simulations on test systems and real distribution networks.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Thermodynamics
Morteza Vahid-Ghavidel, Miadreza Shafie-khah, Mohammad S. Javadi, Sergio F. Santos, Matthew Gough, Darwin A. Quijano, Joao P. S. Catalao
Summary: This paper proposes a method for self-scheduling a distributed energy resource aggregator in a multi-energy system, which manages uncertainty via a combination of Info-gap Decision Theory and stochastic programming, considering factors such as renewable energy, DERs, and EV parking lots.
Review
Green & Sustainable Science & Technology
Annu Dagar, Pankaj Gupta, Vandana Niranjan
Summary: The amalgamation of distributed energy resources with power system faces various challenges, such as technical issues in microgrid implementation and designing a protection scheme that is reliable, selective, fast, and sensitive in both working modes.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Electrical & Electronic
Juan Diego Rios Penaloza, James Amankwah Adu, Alberto Borghetti, Fabio Napolitano, Fabio Tossani, Carlo Alberto Nucci
Summary: The paper investigates the impact of load modeling and load composition on the transient stability assessment of a medium voltage microgrid during islanding transition. It highlights the importance of appropriate load modeling for an accurate analysis of the transient response of islanded microgrids.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Energy & Fuels
Amir Habibzadeh, Mahmoud Samiei Moghaddam, Seyyed Javad Mohammadi Baygi, Soheil Ranjbar
Summary: This paper presents an online index for adaptive protection of microgrids concerning the low inertia concept, utilizing a three-step approach to consider offline and online environments, and validating the effectiveness of the proposed protection scheme.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Computer Science, Artificial Intelligence
C. D. Patel, T. K. Tailor
Summary: A modified version of the sine-cosine algorithm (SCA), called multi-agent sine-cosine algorithm (MA-SCA), has been developed for solving complex optimization problems. The proposed modifications in the self-learning operator and inversion operator have been validated through experiments on standard functions. The MA-SCA algorithm has also been applied to optimize the deployment of energy resources and capacitors in a distribution network, and the results show improvement compared to other optimization methods.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Janaina B. Almada, Ruth P. S. Leao, Rosana G. Almeida, Raimundo F. Sampaio
Summary: This paper focuses on the two-stage hierarchical control of microgrids, using decentralized droop control at the primary level and distributed multi-agent coordination at the secondary level, implemented on the PADE environment. The key contributions include control strategies for reactive power output, dynamic sharing of energy storage systems, and load management based on multi-agent systems. Co-simulations using PSCAD(TM) and PADE validate the accurate performance of the proposed control strategies under different load conditions.
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
M. Mollayousefi Zadeh, P. MohammadAli Rezayi, S. Ghafouri, M. H. Alizadeh, G. B. Gharehpetian
Summary: This paper proposes an optimization model based on multi-agent systems for coordinating the power flow and achieving optimum energy management in interconnected microgrids. The model utilizes the decentralized nature and multifactorial approach of multi-agent systems, and can be divided into multiple levels to reduce communication burden. Moreover, it takes into account the uncertainty of renewables and demand, and incorporates demand response programs for more realistic modeling and improved efficacy.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Energy & Fuels
Md Tanjimuddin, Petri Kannisto, Peyman Jafary, Mikael Filppula, Sami Repo, David Hastbacka
Summary: The current advancements in energy, information, communication, and automation technologies have facilitated the transformation of the energy industry towards cleaner energy systems. The concept of energy internet has emerged as a result of this transition, leveraging the capabilities of recent energy technologies for clean energy generation, storage, and demand response. Software frameworks and platforms are being developed to automate the operation and control of energy resources, with most of them following the design principles of either multi-agent systems (MAS) or service-oriented architecture (SOA). However, there is a lack of clear criteria to select the appropriate framework for implementing energy system automation applications aligned with the vision of energy internet. This study investigates MAS- and SOA-based software solutions through a use case design for microgrid application automation, proposing a combined approach that combines the strengths of MAS and SOA.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Green & Sustainable Science & Technology
Meryem Hamidi, Abdelhadi Raihani, Omar Bouattane
Summary: This paper examines a sustainable, intelligent energy management system for a microgrid based on a multi-agent system (MAS). The system addresses challenges posed by intermittent renewable energy sources, optimizes the use of available AC-DC renewable energy sources, and achieves energy savings of over 82.34%. This innovative solution has the potential to reduce the need for energy storage, improve energy efficiency, and reduce CO2 emissions, offering a promising sustainable development solution for managing and controlling microgrids.
Article
Construction & Building Technology
Mahdi Azimian, Vahid Amir, Saeid Javadi, Soheil Mohseni, Alan C. Brent
Summary: This paper introduces a cyber-attack-resilient design of a multi-carrier microgrid, which aims to minimize the total planning cost and ensure the reliable supply of critical loads. Numerical simulations demonstrate the effectiveness and economic viability of the proposed model.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Energy & Fuels
Hamid Karimi, Shahram Jadid
Summary: This paper proposes a stochastic framework for the operation scheduling of integrated renewable-based energy microgrid systems. The proposed model presents comprehensive scheduling that simultaneously considers total generation costs, generation flexibility, and demand-side flexibility. The framework consists of three layers, with each layer targeting specific aspects of the operation management. The application of the proposed framework to a general energy system structure shows significant improvements in electrical and thermal generating flexibility.
Article
Energy & Fuels
Necmi Altin, Suleyman Emre Eyimaya, Adel Nasiri
Summary: A microgrid is a grid of interconnected distributed energy resources, loads and energy storage systems. The coordinated operation of distributed generation units is crucial in microgrid systems with renewable energy resources to maintain stability. The use of appropriate hardware, such as energy storage systems, and control strategies significantly reduces undesirable situations like distorted voltage profiles and frequency fluctuations. Multi-agent systems offer inherent benefits such as autonomy, responsiveness, and social ability, making them popular for microgrid control. This study provides an overview of the agent concept, multi-agent systems, and recent research on their application in microgrid control. It also proposes a multi-agent-based controller and energy management system design for a DC microgrid, which is tested in different scenarios and validated through simulations.
Review
Computer Science, Information Systems
Kumail Twaisan, Necaattin Barisci
Summary: In the near future, integrating distributed energy resources (DERs) to build a microgrid will be crucial. This paper reviews the studies on microgrid technologies, including the modeling and optimization methodologies of DERs, and system control approaches for DERs and microgrids. The findings suggest that considering multimodal indicators can enhance the reaction capability of communities and stakeholders. The cost of operating a microgrid in isolated mode is significantly higher compared to grid-connected mode.
Article
Energy & Fuels
Kingsley Nweye, Siva Sankaranarayanan, Zoltan Nagy
Summary: Building and power generation decarbonization pose challenges to electric grid reliability. Grid-interactive efficient buildings can provide grid flexibility services through demand response. Reinforcement learning is suitable for energy management in such buildings, but its adoption is hindered by sample inefficiency, control security, and generalizability. In this study, we propose the MERLIN framework to address these challenges and demonstrate its effectiveness using real-world data.
Article
Engineering, Electrical & Electronic
Ankit Vijayvargiya, Bharat Singh, Nidhi Kumari, Rajesh Kumar
Summary: This paper proposes an automated system for diagnosing knee abnormality using sEMG signals. The method includes wavelet denoising, overlapping windowing, and a hybrid Conv-LSTM model for screening abnormal subjects. The results show that this approach is the most precise and convenient model for detecting knee abnormality using sEMG signals.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Forestry
Arun Kumar Thakur, Rajesh Kumar, Raj Kumar Verma, Pankaj Kumar
Summary: This study explores the relationships between tree species distribution and environmental parameters in Western Himalaya, finding that altitude and temperature are the major determining factors. It also shows an upward shift in the regeneration pattern of these tree species. These findings are important for conservation planning and monitoring tree range dynamics under climate change.
JOURNAL OF SUSTAINABLE FORESTRY
(2023)
Article
Engineering, Multidisciplinary
Ashish Kumar Srivastava, Vimal Kumar Pathak, Rakesh Kumar, Rajesh Kumar
Summary: This research article estimates the impact of graphene sheet reinforcement on the elastic modulus of carbon fiber composites. The study models graphene sheet-embedded aluminum nanocomposites at the nanoscale using molecular dynamics, and then uses the resulting elastic modulus to estimate the elastic modulus at micro and macro scales. The results show that the elastic modulus of carbon fiber-embedded graphene sheet-aluminum nanocomposites is increased compared to the original carbon fiber-reinforced composites, regardless of whether the graphene sheets are stacked or not.
INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION
(2023)
Article
Energy & Fuels
Vikash Kumar Saini, Chandra Shekhar Purohit, Rajesh Kumar, Ameena S. Al-Sumaiti
Summary: Rooftop solar power generation is becoming more popular in residential microgrids. New concepts of electricity markets, such as peer-to-peer (P2P) markets, allow direct exchange of locally generated energy between consumers and prosumers. Blockchain technology is being used to enhance data security in energy trading, simplifying P2P energy transactions.
Article
Computer Science, Artificial Intelligence
Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar
Summary: To tackle the complexity of trajectory generation for biped robots on uneven terrain, a data-driven Gait model is proposed in this paper. Deep learning methods are employed to develop seven different data-driven models, with LSTM+GRU-based model showing the best performance. Experimental results demonstrate the superiority of the proposed Gait models over traditional finite state machine and Basis models in terms of error summary statistics.
Article
Computer Science, Information Systems
Varaha Satya Bharath Kurukuru, Ahteshamul Haque, Mohammed Ali Khan, Rajesh Kumar
Summary: This article proposes a failure mode effect classification (FMEC) approach for localizing faults in power electronic converters. The approach uses model-driven fault detection and data-driven fault identification to determine the fault effect on inputs, components, and sensors without compromising the power stage of the converter. Numerical simulations and experimental analysis validate the effectiveness of the proposed approach.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar
Summary: Long-term prediction of joint kinematics is crucial for advanced technology development in prosthetic leg, biped robot, automotive, and human-robot collaboration fields. However, finding the suitable activation function is challenging. This research studies the suitability of activation functions for a data-driven gait model on uneven surfaces, and the Sigmoid-weighted Linear Units (SiLU) function-based gait model outperforms others in terms of maximum error statistic.
RESULTS IN ENGINEERING
(2023)
Review
Engineering, Electrical & Electronic
Vikash Kumar Saini, Rajesh Kumar, Ameena S. Al-Sumaiti, A. Sujil, Ehsan Heydarian-Forushani
Summary: This paper provides a comprehensive review of learning-based short-term forecasting models for smart grid applications. It explores various types of forecasting models, including physical, statistical, hybrid, and uncertainty analysis models, specifically for wind speed forecasting. The study employs 41 different models and evaluates their performance based on regression coefficients and error indices. The findings suggest that the models' performance varies with seasonal variability. The paper also presents recommendations for energy storage planning, energy market and policymakers, and reliability and reserve sizing.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
V. Kumar Saini, Rajesh Kumar, Ameena Saad Al-Sumaiti, B. K. Panigrahi
Summary: Cloud energy storage systems (CES) are a new paradigm for residential community microgrids, allowing consumers to become self-sustaining and interact with utilities and other consumers. This paper proposes the use of CES for residential prosumers, utilizing machine learning-based uncertainty quantization and an artificial ecosystem optimization (AEO) method to determine optimal battery capacity considering uncertainty in PV, load, and price. The feasibility and profitability of deploying CES with residential PV are assessed, and simulation results show that the suggested framework integrated with a distributed PV system is more economical.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Bhawna Mewara, Gunjan Sahni, Soniya Lalwani, Rajesh Kumar
Summary: Protein-protein interactions (PPIs) play a crucial role in various biological processes and have become a key focus in system biology. They are essential for predicting protein function and drug ability. This article introduces a new feature representation method called CAA-PPI for extracting features from protein sequences and achieving high prediction accuracy in PPI analysis.
Proceedings Paper
Green & Sustainable Science & Technology
Pankaj Yadav, Vikas, Vikash Kumar Saini, Ameena S. Al-Sumaiti, Rajesh Kumar
Summary: Shortage of fossil fuels, high oil prices, and environmental concerns pose significant challenges to the transport sector. Hybrid electric vehicles (HEVs) are a viable solution, but require an efficient energy management strategy (EMS). This study utilizes a model-free reinforcement control mechanism with deep Q learning and deep deterministic policy gradient (DDPG) to enhance the learning process and reliability of the EMS framework. The simulation results demonstrate that deep DDPG outperforms deep Q learning in terms of reliability and speed of convergence.
2023 IEEE IAS GLOBAL CONFERENCE ON RENEWABLE ENERGY AND HYDROGEN TECHNOLOGIES, GLOBCONHT
(2023)
Article
Energy & Fuels
Alya Alhendi, Ameena Saad Al-Sumaiti, Mousa Marzband, Rajesh Kumar, Ahmed A. Zaki Diab
Summary: In this paper, an improved Markov Chain Artificial Neural network (ANN-MC) was used for load forecasting, considering various statistical factors. The validation of the proposed model was confirmed through comparing the results with other methods. Additionally, two risk indices were proposed to evaluate model performance.
Article
Engineering, Electrical & Electronic
Vikash Kumar Saini, Ameena S. Al-Sumaiti, Rajesh Kumar
Summary: This paper proposes a data-driven approach to managing uncertainties in cloud-based energy storage systems integrated with renewable energy. SVR, LSTM, and CNN-GRU algorithms are used to estimate the forecast errors of load and PV power, and two mechanisms are proposed to determine the net load error. The net error is analyzed statistically to form different uncertainty-bound confidence intervals, and the operation cost of the cloud energy storage system is calculated.
ELECTRIC POWER SYSTEMS RESEARCH
(2024)
Article
Computer Science, Artificial Intelligence
Ankit Vijayvargiya, Rajesh Kumar, Parul Sharma
Summary: Artificial intelligence has various applications in biomedical sciences, and this research focuses on using surface electromyography (sEMG) signals to aid lower limb activity recognition. The proposed approach includes a multistage classification strategy to overcome the challenges associated with sEMG signals and achieved high accuracy in recognizing lower limb activities.
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2023)
Review
Multidisciplinary Sciences
Md Irfan Ahmed, Ramesh Kumar
Summary: Combined heat and power (CHP) systems are increasingly popular due to their ability to improve economics and sustainability. This paper provides a comprehensive assessment and analysis of the optimal sizing and placement of CHP, discussing its technical characteristics, economic benefits, and optimization algorithms.
