Lolla Phani Raghav

@anits.edu.in

Assistant Professor, Department of Electrical Engineering
Anil Neerukonda Institute of Technology and Sciences

Lolla Phani Raghav


                  Download Compact PDF  
https://researchid.co/phaniraghav
Dr. L. Phani Raghav was graduated in 2012 from JNTU Kakinada and completed his Master's in 2014 from GITAM University with a specialization in Power Systems and Automation. He obtained Ph.D. in 2022 from National Institute of Technology Silchar. His research interests include Energy Management, Demand Response Applications, Metaheuristic Optimization, Operation, Protection, and Control of Smart grids and Microgrids. He has published numerous Q1 SCI-indexed articles in various international journals.

EDUCATION

Ph.D, M.Tech, B.Tech

RESEARCH INTERESTS

Smart Grids, Power System Optimization, Soft Computing techniques in Power Systems Operation and Control, Grid-connected Renewable Energy Sources, Applications of Machine Learning in Power Systems
13

Scopus Publications

1013

Scholar Citations

14

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • Demand response of grid-connected microgrid based on metaheuristic optimization algorithm
    Arvind R. Singh, Lei Ding, D. Koteswara Raju, R. Seshu Kumar, L. Phani Raghav
    Energy Sources Part A Recovery Utilization and Environmental Effects, 2025
    Demand Response Programs (DRPs) have gained the microgrid (MG) operators phenomenal attention for mobilizing the end-users in alleviating the uncertainties associated with renewable energy sources. In this research work, the effective MG energy management system (EMS) in conjunction with price-driven DRPs is proposed to achieve synergistic coordination between the energy providers and consumers and reduce operational costs. The flexible price elasticity model is implemented instead of using the price elasticity models with a preordained constant value like most existing literature. This results in a more realistic characterization of customer responsiveness to energy price changes and promotes the DRPs under a grid-connected MG environment. With this regard, a stochastic day-ahead energy management strategy is proposed to incorporate four distinct DRPs and schedule the MG distributed energy resources. The proposed strategy is verified on a practical 3-feeder MG test system with a majority of 50% industrial load considered. The short-term scheduling period of 15 min ahead solar and wind power forecast is considered to optimize the microgrid dispatch costs accurately. The intermittent nature of renewable energy sources is addressed by employing a stochastic-based scenario generation and reduction approach. A novel and nature-inspired Black Widow Optimization (BWO) is applied to determine the optimal scheduling configuration. The effectiveness of the BWO is validated in terms of rate of convergence, computational time, and solution efficacy. Finally, the best DRP has been chosen based on its technical and economic performance indices by employing a multi-criteria decision-making approach.
  • State-of-the-art review on energy sharing and trading of resilient multi microgrids
    Abhishek Kumar, Arvind R. Singh, L. Phani Raghav, Yan Deng, Xiangning He, R.C. Bansal, Praveen Kumar, R.M. Naidoo
    Iscience, 2024
    Independently run single microgrids (MGs) encounter difficulties with inadequate self-consumption of local renewable energy and frequent power exchange with the grid. Combining numerous MGs to form a multi-microgrid (MMG) is a viable approach to enhance smart distribution networks' operational and financial performance. However, the correlation and coordination of intermittent power generation within each MG network pose many techno-economic challenges for energy sharing and trading. This review offers a comprehensive analysis of these challenges within the framework of MMG operations. It examines state-of-the-art methodologies for optimizing multi-energy dispatch and scrutinizes contemporary strategies within energy markets that contribute to the resilience of power systems. The discourse extends to the burgeoning role of blockchain technology in revolutionizing decentralized market frameworks and the intricacies of MMG coordination for reliable and cost-effective energy distribution. Overall, this study provides ample inspiration for theoretical and practical research to the new entrants and experts alike to develop new concepts for energy markets, scheduling and novel operating models for future resilient multi-energy networked systems/MMGs.
  • Enhancement of loadability and voltage stability in grid-connected microgrid network
    D. Koteswara Raju, R. Seshu Kumar, L. Phani Raghav, Arvind R. Singh
    Journal of Cleaner Production, 2022
  • A swarm intelligence approach for energy management of grid-connected microgrids with flexible load demand response
    Arvind R. Singh, Lei Ding, Dhenuvakonda Koteswara Raju, Lolla Phani Raghav, Rangu Seshu Kumar
    International Journal of Energy Research, 2022
    Ever since its inception, the concept and application of demand‐side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load‐responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price‐based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price‐based demand response program. Furthermore, the stochastic‐based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence‐based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno‐economic performance indices were evaluated for all case studies, and a priority‐wise ranking is assigned based on the multi‐criteria assessment technique.
  • Optimal day ahead energy consumption management in grid-connected microgrids
    Lolla Phani Raghav, Rangu Seshu Kumar, Dhenuvakonda Koteswara Raju, Arvind R. Singh
    International Journal of Energy Research, 2022
    The day‐ahead scheduling of microgrids in the presence of nondispatchable distributed generators (DGs) is a challenging task for microgrid operators. The valve point loading problem of distributed generators and its effect on input‐output characteristics is not extensively covered in the published scientific literature on microgrids. In this research work, the day‐ahead scheduling problem of microgrids is formulated in the presence of DGs with a nonconvex cost function. First, the flexible load‐shaping based demand‐side management strategy is adopted to reduce the peak loads and enhance the DG's unit operational costs. The impact of demand‐side management and price‐driven demand response programs on convex and nonconvex energy management system (EMS) problems is investigated. Furthermore, the short‐term scheduling horizon of 15‐minutes resolution time is considered for both solar and wind power to maintain forecast accuracy. The state‐of‐art optimization algorithm of quantum particle swarm optimization is devised to solve the proposed problem in the presence of the nonconvex cost function of DGs. The technical performance indices for each demand response program is evaluated, and the best alternative demand response program is chosen by implementing analytical hierarchy process. The proposed algorithm efficiently solves the nonconvex EMS problem, and the simulation results yield a 12.11% reduction in operating cost without compromising customer satisfaction. Computational time, convergence characteristics, and solution effectiveness in contrast to recently reported metaheuristic algorithms are examined for the effectiveness of the suggested algorithm.
  • Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid
    L. Phani Raghav, R. Seshu Kumar, D. Koteswara Raju, Arvind R. Singh
    Applied Energy, 2022
  • Optimal Energy Management of Microgrids Using Quantum Teaching Learning Based Algorithm
    L. Phani Raghav, R. Seshu Kumar, D. Koteswara Raju, Arvind R. Singh
    IEEE Transactions on Smart Grid, 2021
    Quantum inspired computational intelligence is gaining momentum in the interest of enhancing the performance of existing metaheuristic optimization while solving multi-dimensional nonlinear problems. The microgrid optimal energy scheduling is one such problem that involves multiple distributed energy resources (DER) with volatile characteristics and proficient energy management is essential for their coordination and reducing global carbon emissions. Relatively very few works in the existing literature have attempted to solve this problem using quantum-based algorithms. In this article, a stochastic framework associated with the Quantum Teaching Learning-based optimization (QTLBO) algorithm is devised for the first time to optimize energy flow in the microgrids. Four scenarios concerning seasonal variations are chosen to address the uncertainties related to generated power from DERs with better accuracy. The day-ahead optimum power scheduling configuration of DERs is evaluated for each scenario. The performance of QTLBO is assessed on a grid-connected microgrid network and compared with existing metaheuristic algorithms such as the Real-coded Genetic Algorithm, Differential Evolution, and TLBO. The obtained simulation results prove the superiority of QTLBO in terms of convergence and achieving a global optimum solution by overcoming premature convergence. Further, the proposed stochastic framework is helpful to attain techno-economic benefits to both customers and market operators.
  • Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids
    R. Seshu Kumar, L. Phani Raghav, D. Koteswara Raju, Arvind R. Singh
    Applied Energy, 2021
  • Optimal energy management of microgrids-integrated nonconvex distributed generating units with load dynamics
    Lolla Phani Raghav, Seshu Kumar Rangu, Koteswara Raju Dhenuvakonda, Arvind R. Singh
    International Journal of Energy Research, 2021
    Microgrid (MG) energy management is a complex task for MG operators to integrate and utilize consumer‐based power sources. The MG energy management systems’ problem will become tedious by considering distributed generation (DG) units' nonconvex characteristics. Therefore, a novel attempt is made to solve the day‐ahead dispatch problem of grid‐connected MG with the nonconvex cost function of DG units, including weekend and weekday load dynamics. At first, the utility‐induced flexible load shaping strategy is implemented to enhance the DG units' operation cost and reduce the peak loads. Then, demand‐side management (DSM) programs are plausibly the essential form of energy management to regulate the consumers' energy usage without violating grid price policies. Next, the DSM program is implemented to study the impact of DSM participation levels with convex and nonconvex cost functions. Further, the day‐ahead scheduling time duration with a resolution of 15 minutes is considered to examine the impact of a typical weekend and weekday load dynamics on DG units' nonconvex cost function. Finally, the Quantum Teaching‐Learning‐Based Optimization algorithm (QTLA) is devised to handle the nonconvex cost function of DG units and optimize MG's total operating costs for the first time. The proposed QTLA algorithm is compared with other metaheuristic optimization techniques such as differential evolution (DE), real‐coded genetic algorithm (RCGA), and Teaching‐Learning‐based Optimization (TLBO). The results show that the proposed strategy reduces the MG operating cost by 3.14% compared to the case study, where no DSM participation is considered. Finally, the QTLA algorithm outperforms in terms of efficacy, convergence characteristics, and computational time.
  • Customer-oriented energy demand management of grid connected microgrids
    Rangu Seshu Kumar, Lolla Phani Raghav, Dhenuvakonda Koteswara Raju, Arvind R. Singh
    International Journal of Energy Research, 2021
    The consumers' active participation in production and energy consumption in response to market changes is an integral characteristic of the smart grid paradigm. In other words, their role in energy trading, either at the individual capacity or as aggregators, will shape the future of fulfilling energy needs. Despite the foreseen technological advancements, the design and implementation of demand response programs (DRPs) with accurate customer responsiveness have remained essential for network operators. Moreover, with non‐dispatchable energy sources' involvement, DRPs play a vital role in shaping the load profile and minimizing the operating costs. It calls for a need to develop a suitable energy management system (EMS) to promote consumers' active participation. With this sense, the incentive‐based DRPs are incorporated into the microgrid EMS to investigate their impact on day‐ahead scheduling costs and managing consumers load profile. A stochastic‐based EMS framework is proposed to this end, and five distinct types of DRPs are implemented on a practical microgrid network, including commercial, industrial, and residential loads. The flexible price elasticity concept is adopted to model the realistic behavior of price responsive loads under different DRPs. Finally, the recently reported novel optimizer is employed to solve the proposed EMS problem and investigate the flexible price model's impact on optimizing the grid‐connected microgrid's daily operating costs. The obtained simulation results are compared with the existing state‐of‐the‐art metaheuristic optimizers to test the attributes related to solution efficiency and convergence rate.
  • Intelligent demand side management for optimal energy scheduling of grid connected microgrids
    R. Seshu Kumar, L. Phani Raghav, D. Koteswara Raju, Arvind R. Singh
    Applied Energy, 2021
  • A comprehensive review of soft computing algorithms for optimal generation scheduling
    Phani Raghav Lolla, Seshu Kumar Rangu, Koteswara Raju Dhenuvakonda, Arvind R. Singh
    International Journal of Energy Research, 2021
  • Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review
    Seshu Kumar Rangu, Phani Raghav Lolla, Koteswara Raju Dhenuvakonda, Arvind R. Singh
    International Journal of Energy Research, 2020

RECENT SCHOLAR PUBLICATIONS

  • Demand response of grid-connected microgrid based on metaheuristic optimization algorithm
    AR Singh, L Ding, DK Raju, RS Kumar, LP Raghav
    Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 47 … , 2025
    2025.0
    Citations: 31
  • State-of-the-art review on energy sharing and trading of resilient multi microgrids
    A Kumar, AR Singh, LP Raghav, Y Deng, X He, RC Bansal, P Kumar, ...
    Iscience 27 (4) , 2024
    2024.0
    Citations: 39
  • State-of-the-art review on energy management and control of networked microgrids
    AR Singh, DK Raju, LP Raghav, RS Kumar
    Sustainable Energy Technologies and Assessments 57, 103248 , 2023
    2023.0
    Citations: 83
  • Enhancement of loadability and voltage stability in grid-connected microgrid network
    DK Raju, RS Kumar, LP Raghav, AR Singh
    Journal of Cleaner Production 374, 133881 , 2022
    2022.0
    Citations: 22
  • Optimal day ahead energy consumption management in grid‐connected microgrids
    L Phani Raghav, R Seshu Kumar, D Koteswara Raju, AR Singh
    International Journal of Energy Research 46 (2), 1864-1881 , 2022
    2022.0
    Citations: 30
  • Analytic hierarchy process (AHP)–swarm intelligence based flexible demand response management of grid-connected microgrid
    LP Raghav, RS Kumar, DK Raju, AR Singh
    Applied Energy 306, 118058 , 2022
    2022.0
    Citations: 105
  • Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids
    RS Kumar, LP Raghav, DK Raju, AR Singh
    Applied energy 301, 117466 , 2021
    2021.0
    Citations: 100
  • Optimal energy management of microgrids using quantum teaching learning based algorithm
    LP Raghav, RS Kumar, DK Raju, AR Singh
    IEEE Transactions on Smart Grid 12 (6), 4834-4842 , 2021
    2021.0
    Citations: 133
  • Intelligent demand side management for optimal energy scheduling of grid connected microgrids
    RS Kumar, LP Raghav, DK Raju, AR Singh
    Applied Energy 285, 116435 , 2021
    2021.0
    Citations: 149
  • A comprehensive review of soft computing algorithms for optimal generation scheduling
    PR Lolla, SK Rangu, KR Dhenuvakonda, AR Singh
    International Journal of Energy Research 45 (2), 1170-1189 , 2021
    2021.0
    Citations: 45
  • Optimal energy management of microgrids‐integrated nonconvex distributed generating units with load dynamics
    LP Raghav, SK Rangu, KR Dhenuvakonda, AR Singh
    International Journal of Energy Research , 2021
    2021.0
    Citations: 42
  • Customer‐oriented energy demand management of grid connected microgrids
    RS Kumar, LP Raghav, DK Raju, AR Singh
    International Journal of Energy Research , 2021
    2021.0
    Citations: 30
  • Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review
    SK Rangu, PR Lolla, KR Dhenuvakonda, AR Singh
    International Journal of Energy Research 44 (13), 9889-9911 , 2020
    2020.0
    Citations: 155
  • Feasibility Analysis of Stand Alone Renewable Energy System in Remote Islands using HOMER
    L. Phani Raghav, L. Venu Madhav
    International Journal of Creative Research Thoughts (IJCRT) 6 (2) , 2018
    2018.0
  • An active frequency drift method for an islanding detection of grid connected micro turbine generation system
    LP Raghav, T Sandhya
    Int. J. Innovat. Res. Sci. Eng. Technol.–(ICETS’14) 3 (1) , 2014
    2014.0
    Citations: 13
  • A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response
    AR Singh, L Ding, DK Raju, LP Raghav, RS Kumar
    International Journal of Energy Research , 0
    Citations: 36

MOST CITED SCHOLAR PUBLICATIONS

  • Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review
    SK Rangu, PR Lolla, KR Dhenuvakonda, AR Singh
    International Journal of Energy Research 44 (13), 9889-9911 , 2020
    2020.0
    Citations: 155
  • Intelligent demand side management for optimal energy scheduling of grid connected microgrids
    RS Kumar, LP Raghav, DK Raju, AR Singh
    Applied Energy 285, 116435 , 2021
    2021.0
    Citations: 149
  • Optimal energy management of microgrids using quantum teaching learning based algorithm
    LP Raghav, RS Kumar, DK Raju, AR Singh
    IEEE Transactions on Smart Grid 12 (6), 4834-4842 , 2021
    2021.0
    Citations: 133
  • Analytic hierarchy process (AHP)–swarm intelligence based flexible demand response management of grid-connected microgrid
    LP Raghav, RS Kumar, DK Raju, AR Singh
    Applied Energy 306, 118058 , 2022
    2022.0
    Citations: 105
  • Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids
    RS Kumar, LP Raghav, DK Raju, AR Singh
    Applied energy 301, 117466 , 2021
    2021.0
    Citations: 100
  • State-of-the-art review on energy management and control of networked microgrids
    AR Singh, DK Raju, LP Raghav, RS Kumar
    Sustainable Energy Technologies and Assessments 57, 103248 , 2023
    2023.0
    Citations: 83
  • A comprehensive review of soft computing algorithms for optimal generation scheduling
    PR Lolla, SK Rangu, KR Dhenuvakonda, AR Singh
    International Journal of Energy Research 45 (2), 1170-1189 , 2021
    2021.0
    Citations: 45
  • Optimal energy management of microgrids‐integrated nonconvex distributed generating units with load dynamics
    LP Raghav, SK Rangu, KR Dhenuvakonda, AR Singh
    International Journal of Energy Research , 2021
    2021.0
    Citations: 42
  • State-of-the-art review on energy sharing and trading of resilient multi microgrids
    A Kumar, AR Singh, LP Raghav, Y Deng, X He, RC Bansal, P Kumar, ...
    Iscience 27 (4) , 2024
    2024.0
    Citations: 39
  • A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response
    AR Singh, L Ding, DK Raju, LP Raghav, RS Kumar
    International Journal of Energy Research , 0
    Citations: 36
  • Demand response of grid-connected microgrid based on metaheuristic optimization algorithm
    AR Singh, L Ding, DK Raju, RS Kumar, LP Raghav
    Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 47 … , 2025
    2025.0
    Citations: 31
  • Optimal day ahead energy consumption management in grid‐connected microgrids
    L Phani Raghav, R Seshu Kumar, D Koteswara Raju, AR Singh
    International Journal of Energy Research 46 (2), 1864-1881 , 2022
    2022.0
    Citations: 30
  • Customer‐oriented energy demand management of grid connected microgrids
    RS Kumar, LP Raghav, DK Raju, AR Singh
    International Journal of Energy Research , 2021
    2021.0
    Citations: 30
  • Enhancement of loadability and voltage stability in grid-connected microgrid network
    DK Raju, RS Kumar, LP Raghav, AR Singh
    Journal of Cleaner Production 374, 133881 , 2022
    2022.0
    Citations: 22
  • An active frequency drift method for an islanding detection of grid connected micro turbine generation system
    LP Raghav, T Sandhya
    Int. J. Innovat. Res. Sci. Eng. Technol.–(ICETS’14) 3 (1) , 2014
    2014.0
    Citations: 13
  • Feasibility Analysis of Stand Alone Renewable Energy System in Remote Islands using HOMER
    L. Phani Raghav, L. Venu Madhav
    International Journal of Creative Research Thoughts (IJCRT) 6 (2) , 2018
    2018.0