Bhamidi Lokeshgupta

@mnnit.ac.in

Assistant Professor and Department of Electrical and Electronics Engineering
MNNIT Allahabad

Bhamidi Lokeshgupta


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https://researchid.co/lokesh206

EDUCATION

PhD IIT Patna

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, Energy Engineering and Power Technology, Energy
39

Scopus Publications

928

Scholar Citations

12

Scholar h-index

13

Scholar i10-index

Scopus Publications

  • A comparative study of machine learning and deep learning models for renewable energy and load demand forecasting in smart microgrids
    K. Ravivarma, B. Lokeshgupta, Ramanjaneya Reddy Udumula, Kiran Kumar Nallamekala
    Discover Sustainability, 2026
    This paper presents a hybrid deep learning-based prediction model, i.e., a bidirectional long short-term memory-gated recurrent unit (BiLSTM-GRU) model to forecast the uncertain RER output powers and load demand data in the MG environment. The proposed BiLSTM-GRU model enhances the prediction accuracy by incorporating the strengths of both BiLSTM and GRU models, which capture the data with complex temporal patterns and interdependencies and reduce the complexity of the network structure. The prediction accuracy and performance efficiency of the proposed model have been compared with the different machine and deep learning models such as support vector machine (SVM), random forest regression (RF), gradient boosting machine (GBM), long-short term memory (LSTM), BiLSTM, GRU, and a hybrid LSTM-GRU with the help of various performance metrics. The performance evaluation has been tested with four distinct time horizons: 6 h, 12 h, 18 h, and 24 h ahead. The BiLSTM-GRU model outperformed when compared all other prediction models, achieving improved performance metric values, which are 0.000019 in MSE, 0.0020 in MAE, 0.0044 in RMSE, and 0.0246 in NRMSE for solar output power prediction. In the case of wind output power prediction, 0.00090 in MSE, 0.0215 in MAE, 0.0300 in RMSE, and 0.0763 in NRMSE values are obtained. Similarly, for the load demand data prediction, 0.00022 in MSE, 0.0124 in MAE, 0.0150 in RMSE, and 0.0206 in NRMSE are achieved. Furthermore, to validate the performance of the proposed model, a Diebold-Mariano (DM) test is performed. The DM results confirmed that the proposed model obtains the statistically significant improvements over benchmark models with average p values of 0.0288, 0.0410, and 0.027 ( p < 0.05) for solar output power, wind output power and load demand data, respectively. The computational efficiency of the proposed model has also demonstrated, which requires only 49 s for training process and 0.3 s for testing process, and making it suitable for the real-time energy forecasting applications. The overall results analysis clearly shows the superior performance of the proposed BiLSTM-GRU model for the forecasting of RER output powers and load demand data with the highest accuracy.
  • A blockchain based peer-to-peer energy trading model for multi microgrid distributed energy management
    K. Ravivarma, B. Lokeshgupta, Ramanjaneya Reddy Udumula
    Sustainable Energy Grids and Networks, 2025
  • Adaptive neural network models for state of charge estimation under dynamic battery conditions
    D.S.R.S.L. Avanthika, Bhamidi Lokeshgupta, Ramanjaneya Reddy Udumula
    Journal of Energy Storage, 2025
  • Hybrid Finite Control Set Model Predictive Control and Universal Droop Control for Enhanced Power Sharing in Inverter-Based Microgrids
    Devarapalli Vimala, Naresh Kumar Vemula, Bhamidi Lokeshgupta, Ramesh Devarapalli, Łukasz Knypiński
    Energies, 2025
    This paper proposes a novel hybrid control strategy integrating a Finite Control Set Model Predictive Controller (FCS-MPC) with a universal droop controller (UDC) for effective load power sharing in inverter-fed microgrids. Traditional droop-based methods, though widely adopted for their simplicity and decentralized nature, suffer from limitations such as steady-state inaccuracies and poor transient response, particularly under mismatched impedance conditions. To overcome these drawbacks, the proposed scheme incorporates detailed modeling of inverter and source dynamics within the predictive controller to enhance accuracy, stability, and response speed. The UDC complements the predictive framework by ensuring coordination among inverters with different impedance characteristics. Simulation results under various load disturbances demonstrate that the proposed approach significantly outperforms conventional PI-based droop control in terms of voltage and frequency regulation, transient stability, and balanced power sharing. The performance is further validated through real-time simulations, affirming the scheme’s potential for practical deployment in dynamic microgrid environments.
  • Fusion-Based Switched Kalman Filter for State-of-Charge Estimation in Electric Vehicles Under Dynamic Conditions: A Real-Time Validation
    D S R S L Avanthika, Ramanjaneya Reddy Udumula, Bhamidi Lokeshgupta
    2025 IEEE 4th International Conference on Smart Technologies for Power Energy and Control Stpec 2025 Conference Report, 2025
    The battery is a critical component in Electric Vehicles (EV's), and accurate State of Charge (SoC) estimation is essential for the efficient functioning of Battery Management Systems (BMS). Traditional methods like Coulomb Counting (CC) are prone to integration drift over time, while Open Circuit Voltage (OCV) methods offer limited accuracy under dynamic load conditions. To address these challenges, this study proposes a Switch Mode Kalman Filter (KF) approach, which intelligently switches between CC during load conditions and OCV during rest periods. To simulate the dynamic behavior of EV operations, a Hybrid Pulse Power Characterization (HPPC) test was conducted on a Lithium-ion (Li-ion) cell. Real-time validation using OPALRT confirms the improved performance of the proposed method. The traditional Kalman Filter achieved an RMSE of 0.1085 and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{2}$</tex> of 0.7077, whereas the proposed SM-KF achieved a lower RMSE of 0.0548 and a higher <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{2}$</tex> of 0.9253, demonstrating superior estimation accuracy. By utilizing OCV data during rest phases, the switch mode KF effectively corrects CC drift, making it highly suitable for real-world EV applications that frequently alternate between driving and idle conditions. The results highlight the robustness and adaptability of the proposed approach for real-time SoC estimation.
  • A Finite Control Set based Model Predictive Controller for Load Power Sharing Applications in Inverter Fed Microgrids
    Devarapalli Vimala, B. Lokeshgupta, V. Naresh Kumar
    2025 4th International Conference on Power Control and Computing Technologies Icpc2t 2025, 2025
    Microgrids have gained more attention in recent days due to the efficient integration of various distributed energy resources. However, the load power sharing between the distribution generators (DGs) in the microgrids is one of the major challenges, especially at the peak load demand condition. This paper proposes a finite control set-based model predictive controller (FCS-MPC) for the DG-fed inverters in microgrid applications. A universal droop controller model is also considered into account to generate the reference values for the proposed FCS-MPC controller for improved power sharing. The main goal of this paper is to efficiently regulate the power flow from/to the parallel DGs in a microgrid environment. The proposed control method is able to share equal load power, though there is a mismatch in line impedances in the AC microgrid network. In this study, the microgrid test system with two parallel DGs is used to evaluate the performance of the proposed model. To show the effectiveness of the proposed control method, the simulation results of the proposed model have also been compared with the conventional droop control technique. The proposed model has superior performance compared to the conventional droop controller in terms of load power sharing and maintaining tolerance limits, as evidenced by the simulation results.
  • Design and Analysis of DC-DC Boost converter using Model Predictive Controller
    Devarapalli Vimala, B. Lokeshgupta, V Naresh Kumar
    2025 4th International Conference on Power Control and Computing Technologies Icpc2t 2025, 2025
    A boost converter is a type of device used to convert direct current (DC) from one voltage level to another. It operates by increasing the input voltage to a higher output voltage level.DC-DC converters are utilised in a wide range of applications. The primary application of boost converters is to establish an interface with renewable energy sources.This paper presents a comparative analysis of two controllers, namely the Proportional Integral controller and Model Predictive Control (MPC), for practical applications of the Boost converter. The boost converter is designed and simulated in the MATLAB/SIMULINK environment for this study.The performance of the MPC controller is found to be superior when compared to the PI controller.The effectiveness of the proposed control scheme is validated through the utilisation of OPAL-RT.
  • BiLSTM-Based Load Forecasting for Individual Appliances in Smart Homes
    Ravivarma Kamireddy, Lokeshgupta Bhamidi, U Ramanjaneya Reddy
    2025 IEEE 4th International Conference on Smart Technologies for Power Energy and Control Stpec 2025 Conference Report, 2025
    This study develops an intelligent energy predictor using a Bidirectional long short-term memory (BiLSTM) neural network model for predicting energy demand response within a smart home. To implement this proposed model, the real-time data from five different appliances such as air conditioner, TV, computer, fan, and lamp were monitored via smart meters within a Home Energy Management System (HEMS). In addition to the smart meters, an extra meter has been installed to monitor overall energy consumption data. This real-time collected data setup enables the collection of precise and comprehensive energy consumption data, which in turn supports data analysis and confirms the system's effectiveness before its large-scale deployment. The data normalization and feature selection plays a crucial role in the effectiveness of the proposed model. Various performance metrics are used to validate the proposed model and compare with LSTM model. The proposed Bi-LSTM method achieved superior performance over the LSTM model by reducing errors interms of performance metrics such as MAE, RMSE, NRMSE, MSE, and improving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> of individual appliances in a smart home. The results highlight the model's potential in smart-home energy management and suggest future work in integrating contextual data and enhancing real-world applications.
  • Random Forest based Machine Learning Algorithm for Estimating State of Charge in Lithium-ion Batteries
    D S R S L Avanthika, Bhamidi Lokeshgupta, Ramanjaneya Reddy Udumula
    2025 4th International Conference on Power Control and Computing Technologies Icpc2t 2025, 2025
    This paper proposes a Random Forest (RF) machine learning algorithm-based prediction model for the state of charge (SoC) level of lithium-ion batteries for electric vehicles. To show the effectiveness of the proposed prediction model performance, the RF model has been compared with the other machine learning algorithms such as Support Vector Machines (SVM) and Gradient Boosting (GB) approaches. The dataset includes cell temperature, state of charge (SoC), voltage, and current readings at three different external temperatures—15, 25, and 30 degrees Celsius are considered in this paper to test the performances of the proposed model. After preprocessing of the dataset, 20% of the data was used for testing and the remaining 80% for training purposes. The various metrics such as mean squared error (MSE), mean absolute error (MAE), coefficient of determination $\left(R^{2}\right)$, root mean squared error (RMSE), normalized root mean squared error (NRMSE), residual standard error (RSE), and relative absolute error (RAE) are usually preferred to evaluate the performance of the prediction models. The simulation results of the proposed model clearly show the effectiveness of SoClevel estimation for real-time battery management systems (BMS) when compared to other machine learning algorithms. The efficiency of the proposed model is $\mathbf{9 9 \%}$ and execution time is less than 5 seconds. The accurate estimation of the SOC of lithium-ion batteries is crucial for optimizing battery performance, ensuring safety, and extending battery life in electric vehicles.
  • Electricity Theft Detection Model Using CNN-BiLSTM with Attention Mechanism in Smart Grid Environment
    Ravivarma Kamireddy, Lokeshgupta Bhamidi, Ramanjaneya Reddy U
    2025 IEEE 4th International Conference on Smart Technologies for Power Energy and Control Stpec 2025 Conference Report, 2025
    Electricity theft is a severe non-technical losses for electricity provider, as it reduces supply quality, forces legitimate consumers to pay higher bills, and negatively impacts on the economy. The development of electricity theft detection models play a major role in identification of electricity theft consumers. The main issue with current anomaly detection models are the misclassification of imbalanced energy consumption data, high false positive rates and overfitting issues. An Intensive research is essential for the precise identification of electricity theft and for recovering significant economy losses faced by utility companies. To this end, a hybrid convolutional neural network- bidirectional long short-term memory (CNN-BiLSTM) with attention mechanism based deep learning model for anomaly detection is proposed in this work. The proposed model addresses the data imbalance problem by employing two data balancing approaches: K-means Synthetic Minority Over-sampling Technique (K-SMOTE) and K-means Adaptive Synthetic Sampling (K-ADASYN). The proposed model utilizes real time data I.e, State Grid Corporation of China (SGCC) to analyze and address electricity theft, and also it compares with different classification and deep learning based detection models such as Support vector machine (SVM), Logistic regression (LR), Long shortterm memory (LSTM), Bidirectional long short-term memory (BiLSTM), Convolutional neural network (CNN), and hybrid models such as CNN-LSTM, CNN- BiLSTM, CNN-LSTMAttention mechanism. To verify the proposed model different performance metrics are considered such as precision, recall, F1 score, accuracy, Matthews Correlation Coefficient (MCC), and Area Under Curve (AUC). From the results, it is clearly evident that the proposed CNN-BiLSTM-Attention mechanism with Kmeans ADASYN has better accuracy of 95%, F1 score of 95%, Recall of 95%, Precision of 95%, MCC of 90% and AUC of 95% When compared with other balancing approach and different algorithms.
  • Energy Resource Planning and Management for Economical and Reliable Microgrid
    Ram Prakash, B. Lokeshgupta, Bhavesh R. Bhalja, S. Sivasubramani
    Conference Record IAS Annual Meeting IEEE Industry Applications Society, 2025
  • Dual Estimation of State of Charge and State of Health of a Battery: Leveraging Machine Learning and Deep Neural Networks
    D S R S L Avanthika, Ramanjaneya Reddy Udumula, Bhamidi Lokeshgupta, Mahesh Kumar Morampudi
    2025 4th International Conference on Power Control and Computing Technologies Icpc2t 2025, 2025
  • Data-Model Fusion Approach for Combined State of Charge and State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Using Extended Kalman Filter
    Kolli Sai Kiran, D S R S L Avanthika, Ramanjaneya Reddy Udumula, Bhamidi Lokeshgupta, Naresh Kumar Vemula
    2025 IEEE 4th International Conference on Smart Technologies for Power Energy and Control Stpec 2025 Conference Report, 2025
  • Bi-LSTM based electrical load prediction model for a microgrid community area of Panama city
    Ravivarma Kamireddy, Lokeshgupta Bhamidi, Veerakotlu Lella, Yasmeena, D S R S L Avanthika
    2025 4th International Conference on Power Control and Computing Technologies Icpc2t 2025, 2025
  • A peer-to-peer energy trading model for community microgrids with energy management
    K. Ravivarma, B. Lokeshgupta
    Peer to Peer Networking and Applications, 2024
  • Smart Home Energy Management with a Coordinated Neighborhood Energy Sharing
    K. Ravivarma, B. Lokeshgupta, Udumala Ramanjaneya Reddy
    Lecture Notes in Electrical Engineering, 2024
  • Dynamic Operation of Islanded DC Microgrid with Fuel Cell Using Hybrid Energy Storage Systems
    Maya Vijayan, Ramanjaneya Reddy Udumula, Tarkeshwar Mahto, Lokeshgupta Bhamidi
    Lecture Notes in Electrical Engineering, 2024
  • Optimal DG Planning Incorporating Energy Management for an Economical and Resilient Smart Distribution System
    Ram Prakash, B. Lokeshgupta, S. Sivasubramani, Tarakanath Kobaku, Vivek Agarwal
    IEEE Transactions on Industry Applications, 2024
  • A Multi-objective Optimization Model for Economic-Environmental Operation of Microgrid
    Mohan Kumar Gowrisetty, Manjunath Yalagala, Vineeth Reddy LakkiReddy, Ravivarma Kamireddy, Lokeshgupta Bhamidi
    Smart Innovation Systems and Technologies, 2024
  • Power Factor Correction Buck-Boost Converter for On-Board EV Charging Application
    Ramanjaneya Reddy Udumula, Surjeet Patnaik, Sharan Kumar Nandigama, Uday Sankar Dega, Bhamidi Lokeshgupta, Naresh Kumar Vemula, N. Kirankumar
    Lecture Notes in Electrical Engineering, 2024
  • Coordinated smart home energy sharing with a centralized neighbourhood energy management
    B. Lokeshgupta, K. Ravivarma
    Sustainable Cities and Society, 2023
  • A Multi-Objective Optimization Model for Microgrid Optimal Operation with Cooperative Game Theory Approach
    Munnangi Sudhakar, R. S. Srinivas, Ravivarma Kamireddy, Bhamidi Lokeshgupta
    2023 IEEE 20th India Council International Conference Indicon 2023, 2023
  • A Multi Microgrid Energy Management with Peer-to-Peer Energy Trading Model
    K. Ravivarma, J. Keerthi Tanvita, N. Harshitha, B. Lokeshgupta
    2023 IEEE IAS Global Conference on Emerging Technologies Globconet 2023, 2023
  • Optimal PI-Controller-Based Hybrid Energy Storage System in DC Microgrid
    Maya Vijayan, Ramanjaneya Reddy Udumula, Tarkeshwar Mahto, Bhamidi Lokeshgupta, B Srikanth Goud, Ch Naga Sai Kalyan, Praveen Kumar Balachandran, Dhanamjayulu C, Sanjeevikumar Padmanaban, Bhekisipho Twala
    Sustainability Switzerland, 2022
  • Dynamic Economic and Emission Dispatch with Renewable Energy Integration Under Uncertainties and Demand-Side Management
    B. Lokeshgupta, S. Sivasubramani
    Electrical Engineering, 2022
  • Reliability Improvement of a Radial Distribution System Considering Load Modeling and Energy Management
    Ram Prakash, B. Lokeshgupta, S. Sivasubramani
    Icpc2t 2022 2nd International Conference on Power Control and Computing Technologies Proceedings, 2022
  • Optimal Sizing of Smart Home Renewable Energy Resources and Battery under Prosumer-Based Energy Management
    Lokeshgupta Bhamidi, S. Sivasubramani
    IEEE Systems Journal, 2021
  • Optimal Planning and Operational Strategy of a Residential Microgrid with Demand Side Management
    Lokeshgupta Bhamidi, S. Sivasubramani
    IEEE Systems Journal, 2020
  • Multi-objective residential demand side management with solar and wind energy integration
    B. Lokeshgupta, S. Sivasubramani
    2019 8th International Conference on Power Systems Transition Towards Sustainable Smart and Flexible Grids Icps 2019, 2019
  • Optimal operation of a residential microgrid with demand side management
    B. Lokeshgupta, S. Sivasubramani
    Proceedings of 2019 IEEE Pes Innovative Smart Grid Technologies Europe Isgt Europe 2019, 2019
  • Multi-objective Harmony Search Algorithm for Dynamic Optimal Power Flow with Demand Side Management
    Lokeshgupta Bhamidi, Sivasubramani Shanmugavelu
    Electric Power Components and Systems, 2019
  • Multi-objective home energy management with battery energy storage systems
    B. Lokeshgupta, S. Sivasubramani
    Sustainable Cities and Society, 2019
  • Cooperative game theory approach for multiobjective home energy management with renewable energy integration
    Bhamidi Lokeshgupta, Shanmugavelu Sivasubramani
    Iet Smart Grid, 2019
  • Multi-Objective Residential Demand Side Management with Game Theory Approach
    B. Lokeshgupta, S. Sivasubramani
    2018 20th National Power Systems Conference Npsc 2018, 2018
  • Multi-Objective Bat Algorithm for Optimal Placement and Sizing of DG
    Ram Prakash, B. Lokeshgupta, S. Sivasubramani
    2018 20th National Power Systems Conference Npsc 2018, 2018
  • Prosumer based energy management with optimal sized renewable energy integration
    B. Lokeshgupta, S. Sivasubramani
    Proceedings of 2018 IEEE International Conference on Power Electronics Drives and Energy Systems Pedes 2018, 2018
  • Optimal Site and Size of DG with different Load Models using Cuckoo Search Algorithm
    Ram Prakash, B. Lokeshgupta, S. Sivasubramani
    Proceedings of 2018 IEEE International Conference on Power Electronics Drives and Energy Systems Pedes 2018, 2018
  • Multi-objective optimization for demand side management in a smart grid environment
    B. Lokeshgupta, A. Sadhukhan, S. Sivasubramani
    2017 7th International Conference on Power Systems Icps 2017, 2018
  • Multi-objective dynamic economic and emission dispatch with demand side management
    B. Lokeshgupta, S. Sivasubramani
    International Journal of Electrical Power and Energy Systems, 2018

RECENT SCHOLAR PUBLICATIONS

  • A comparative study of machine learning and deep learning models for renewable energy and load demand forecasting in smart microgrids
    K Ravivarma, B Lokeshgupta, RR Udumula, KK Nallamekala
    Discover Sustainability 7 (1), 518 , 2026
    2026
  • BiLSTM-Based Load Forecasting for Individual Appliances in Smart Homes
    R Kamireddy, L Bhamidi, UR Reddy
    2025 IEEE 4th International Conference on Smart Technologies for Power … , 2025
    2025
  • Electricity Theft Detection Model Using CNN-BiLSTM with Attention Mechanism in Smart Grid Environment
    R Kamireddy, L Bhamidi
    2025 IEEE 4th International Conference on Smart Technologies for Power … , 2025
    2025
  • Data-Model Fusion Approach for Combined State of Charge and State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Using Extended Kalman Filter
    KS Kiran, D Avanthika, RR Udumula, B Lokeshgupta, NK Vemula
    2025 IEEE 4th International Conference on Smart Technologies for Power … , 2025
    2025
  • Fusion-Based Switched Kalman Filter for State-of-Charge Estimation in Electric Vehicles Under Dynamic Conditions: A Real-Time Validation
    D Avanthika, RR Udumula, B Lokeshgupta
    2025 IEEE 4th International Conference on Smart Technologies for Power … , 2025
    2025
  • A blockchain based peer-to-peer energy trading model for multi microgrid distributed energy management
    K Ravivarma, B Lokeshgupta, RR Udumula
    Sustainable Energy, Grids and Networks, 102045 , 2025
    2025
    Citations: 3
  • Adaptive neural network models for state of charge estimation under dynamic battery conditions
    D Avanthika, B Lokeshgupta, RR Udumula
    Journal of Energy Storage 133, 117951 , 2025
    2025
    Citations: 5
  • Hybrid Finite Control Set Model Predictive Control and Universal Droop Control for Enhanced Power Sharing in Inverter-Based Microgrids
    D Vimala, NK Vemula, B Lokeshgupta, R Devarapalli, Ł Knypiński
    Energies 18 (19), 5200 , 2025
    2025
    Citations: 2
  • Energy Resource Planning and Management for Economical and Reliable Microgrid
    R Prakash, B Lokeshgupta, BR Bhalja, S Sivasubramani
    2025 IEEE Industry Applications Society Annual Meeting (IAS), 1-8 , 2025
    2025
  • Dual Estimation of State of Charge and State of Health of a Battery: Leveraging Machine Learning and Deep Neural Networks
    D Avanthika, RR Udumula, B Lokeshgupta, MK Morampudi
    2025 Fourth International Conference on Power, Control and Computing … , 2025
    2025
    Citations: 1
  • Random Forest based Machine Learning Algorithm for Estimating State of Charge in Lithium-ion Batteries
    D Avanthika, B Lokeshgupta, RR Udumula
    2025 Fourth International Conference on Power, Control and Computing … , 2025
    2025
    Citations: 1
  • Design and Analysis of DC-DC Boost converter using Model Predictive Controller
    D Vimala, B Lokeshgupta, VN Kumar
    2025 Fourth International Conference on Power, Control and Computing … , 2025
    2025
  • Bi-LSTM based electrical load prediction model for a microgrid community area of Panama city
    R Kamireddy, L Bhamidi, V Lella, D Avanthika
    2025 Fourth International Conference on Power, Control and Computing … , 2025
    2025
    Citations: 2
  • A Finite Control Set based Model Predictive Controller for Load Power Sharing Applications in Inverter Fed Microgrids
    D Vimala, B Lokeshgupta, VN Kumar
    2025 Fourth International Conference on Power, Control and Computing … , 2025
    2025
    Citations: 2
  • Converter for On-Board EV Charging Application
    RR Udumula, S Patnaik, SK Nandigama, US Dega, B Lokeshgupta
    Recent Advances in Power Electronics and Drives: Select Proceedings of EPREC … , 2025
    2025
  • A peer-to-peer energy trading model for community microgrids with energy management
    K Ravivarma, B Lokeshgupta
    Peer-to-Peer Networking and Applications 17 (4), 2538-2554 , 2024
    2024
    Citations: 14
  • Power Factor Correction Buck-Boost Converter for On-Board EV Charging Application
    RR Udumula, S Patnaik, SK Nandigama, US Dega, B Lokeshgupta, ...
    International Conference on Electric Power and Renewable Energy, 207-231 , 2024
    2024
  • A Multi-Objective Optimization Model for Microgrid Optimal Operation with Cooperative Game Theory Approach
    M Sudhakar, RS Srinivas, R Kamireddy, B Lokeshgupta
    2023 IEEE 20th India Council International Conference (INDICON), 299-304 , 2023
    2023
    Citations: 4
  • A Multi-objective Optimization Model for Economic-Environmental Operation
    MK Gowrisetty, M Yalagala, VR LakkiReddy, R Kamireddy, L Bhamidi
    Intelligent Manufacturing and Energy Sustainability: Proceedings of ICIMES … , 2023
    2023
  • Optimal dg planning incorporating energy management for an economical and resilient smart distribution system
    R Prakash, B Lokeshgupta, S Sivasubramani, T Kobaku, V Agarwal
    IEEE Transactions on Industry Applications 60 (1), 1890-1901 , 2023
    2023
    Citations: 11

MOST CITED SCHOLAR PUBLICATIONS

  • Multi-objective home energy management with battery energy storage systems
    B Lokeshgupta, S Sivasubramani
    Sustainable Cities and Society 47, 101458 , 2019
    2019
    Citations: 211
  • Multi-objective dynamic economic and emission dispatch with demand side management
    B Lokeshgupta, S Sivasubramani
    International Journal of Electrical Power & Energy Systems 97, 334-343 , 2018
    2018
    Citations: 202
  • Optimal planning and operational strategy of a residential microgrid with demand side management
    L Bhamidi, S Sivasubramani
    IEEE Systems Journal 14 (2), 2624-2632 , 2019
    2019
    Citations: 100
  • Optimal sizing of smart home renewable energy resources and battery under prosumer-based energy management
    L Bhamidi, S Sivasubramani
    IEEE Systems Journal 15 (1), 105-113 , 2020
    2020
    Citations: 70
  • Cooperative game theory approach for multi‐objective home energy management with renewable energy integration
    B Lokeshgupta, S Sivasubramani
    IET Smart Grid 2 (1), 34-41 , 2019
    2019
    Citations: 64
  • Optimal pi-controller-based hybrid energy storage system in dc microgrid
    M Vijayan, RR Udumula, T Mahto, B Lokeshgupta, BS Goud, CNS Kalyan, ...
    Sustainability 14 (22), 14666 , 2022
    2022
    Citations: 37
  • Multi-objective harmony search algorithm for dynamic optimal power flow with demand side management
    L Bhamidi, S Shanmugavelu
    Electric Power Components and Systems 47 (8), 692-702 , 2019
    2019
    Citations: 36
  • Coordinated smart home energy sharing with a centralized neighbourhood energy management
    B Lokeshgupta, K Ravivarma
    Sustainable Cities and Society 96, 104642 , 2023
    2023
    Citations: 30
  • Multi-objective bat algorithm for optimal placement and sizing of DG
    R Prakash, B Lokeshgupta, S Sivasubramani
    2018 20th National Power Systems Conference (NPSC), 1-6 , 2018
    2018
    Citations: 30
  • Dynamic economic and emission dispatch with renewable energy integration under uncertainties and demand-side management
    B Lokeshgupta, S Sivasubramani
    Electrical Engineering 104 (4), 2237-2248 , 2022
    2022
    Citations: 25
  • Multi-objective optimization for demand side management in a smart grid environment
    B Lokeshgupta, A Sadhukhan, S Sivasubramani
    2017 7th International Conference on Power Systems (ICPS), 200-205 , 2017
    2017
    Citations: 19
  • A peer-to-peer energy trading model for community microgrids with energy management
    K Ravivarma, B Lokeshgupta
    Peer-to-Peer Networking and Applications 17 (4), 2538-2554 , 2024
    2024
    Citations: 14
  • Optimal dg planning incorporating energy management for an economical and resilient smart distribution system
    R Prakash, B Lokeshgupta, S Sivasubramani, T Kobaku, V Agarwal
    IEEE Transactions on Industry Applications 60 (1), 1890-1901 , 2023
    2023
    Citations: 11
  • Multi-objective home energy management with battery energy storage systems. Sustain Cities Soc 2019; 47: 101458
    B Lokeshgupta, S Sivasubramani
    2019
    Citations: 8
  • Reliability improvement of a radial distribution system considering load modeling and energy management
    R Prakash, B Lokeshgupta, S Sivasubramani
    2022 Second International Conference on Power, Control and Computing … , 2022
    2022
    Citations: 7
  • Multi-objective residential demand side management with solar and wind energy integration
    B Lokeshgupta, S Sivasubramani
    2019 8th International Conference on Power Systems (ICPS), 1-6 , 2019
    2019
    Citations: 7
  • Optimal operation of a residential microgrid with demand side management
    B Lokeshgupta, S Sivasubramani
    2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 1-5 , 2019
    2019
    Citations: 7
  • Optimal site and size of DG with different load models using cuckoo search algorithm
    R Prakash, B Lokeshgupta, S Sivasubramani
    2018 IEEE international conference on power electronics, drives and energy … , 2018
    2018
    Citations: 6
  • Cooperative game theory approach for multi-objective home energy management with renewable energy integration, IET Smart Grid 2 (1), 34–41
    B Lokeshgupta, S Sivasubramani
    2018
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