Dr. Nisha Dagade | Renewable Energy | Best Researcher Award

Dr. Nisha Dagade | Sinhgad Institutes | India

Dr. Nisha R. Dagade is an accomplished Assistant Professor at Sinhgad Institutes, Pune, India, specializing in electrical power systems with a particular focus on Distributed Generation and Reliability Analysis. Her research emphasizes the optimal integration of renewable DG sources into modern distribution networks, addressing both technical and economic challenges through heuristic and metaheuristic optimization approaches such as Ant Colony Optimization (ACO). Dr. Dagade’s scholarly contributions explore multi-objective frameworks that aim to reduce power losses, improve voltage profiles, and enhance the overall reliability and cost-effectiveness of distribution systems. Her notable work, “Ant colony optimization technique for integrating renewable DG in distribution system with techno-economic objectives,” published in Evolving Systems (2022), has gained significant academic recognition. With a strong research portfolio comprising 10 completed and ongoing projects, 7 Scopus-indexed journal publications, and one published book, and maintains an active research profile with 61 citations, an h-index of 5, and an i10-index of 2 , she continues to advance innovation in the domain of sustainable power systems. She has also collaborated with IIT Bombay on research initiatives that bridge academic insights with real-world applications. Her professional memberships in IAENG and I2OR reflect her active engagement in the global engineering research community. Dr. Dagade’s work embodies the integration of renewable energy technologies for efficient, reliable, and environmentally responsible power system development.

Profile: Google Scholar

Featured Publications

Godha, N. R., Bapat, V. N., & Korachagaon, I. (2022). Ant colony optimization technique for integrating renewable DG in distribution system with techno-economic objectives. Evolving Systems, 13(3), 485–498.
Godha, N. R., Deshmukh, S. R., & Dagade, R. V. (2011). Application of Monte Carlo simulation for reliability cost/worth analysis of distribution system. In 2011 International Conference on Power and Energy Systems (pp. 1–6).
Godha, N. R., Deshmukh, S. R., & Dagade, R. V. (2012). Time sequential Monte Carlo simulation for evaluation of reliability indices of power distribution system. In Proceedings of the 2012 IEEE Symposium on Computers and Informatics (ISCI 2012).
Godha, N. R., Bapat, V. N., & Korachagaon, I. (2019). Placement of distributed generation in distribution networks: A survey on different heuristic methods. In Techno-Societal 2018: Proceedings of the 2nd International Conference on Techno-Societal.
Dagade, N. R. G., Bapat, V. N., & Korachagaon, I. (2020). Improved ACO for planning and performance analysis of multiple distributed generations in distribution system for various load models. In 2020 Second International Sustainability and Resilience Conference.

Dr. Guangxing Guo | Renewable Energy | Best Academic Researcher Award

Dr. Guangxing Guo | Yangzhou University | China

Dr. Guangxing Guo is a dedicated researcher in the field of wind energy with strong academic training and impactful contributions. He is pursuing a Doctor of Engineering degree at Yangzhou University, focusing on the application of artificial intelligence in wind energy, and also gained international academic exposure as a guest student at Aalborg University, Denmark. He earned a Master of Engineering degree from the Institute of Engineering Thermophysics, Chinese Academy of Sciences, where his research centered on the structural performance of wind turbine blades, and a Bachelor of Engineering in New Energy Science and Engineering from Lanzhou University of Technology. His expertise covers aerodynamics, aeroelastic dynamics, structural dynamics, and computational fluid dynamics. To date, he has published 8 documents, including SCI-indexed works in Composite Structures, Sustainable Energy Technologies and Assessments, and Energies. His research has received 90 citations by 75 documents, with an h-index of 5, demonstrating growing academic recognition. He has also presented his work at international conferences, highlighting contributions to wind turbine blade design, wind farm noise reduction, and machine learning-based performance evaluation.

Profile: Scopus | Orcid

Featured Publications

Guo, G., Zhu, W., Sun, Z., Fu, S., Shen, W., & Hua, Y. (2025). Large wind turbine blade design with mould sharing concept based on deep neural networks. Sustainable Energy Technologies and Assessments, 73, 104131.

Guo, G., Zhu, W., Zhang, Z., Shen, W., & Chen, Z. (2025). Achieving power-noise balance in wind farms by fine-tuning the layout with reinforcement learning. Energies, 18(18), 5019.

Guo, G., Zhu, W., Sun, Z., Fu, S., Shen, W., & Cao, J. (2024). An aero-structure-acoustics evaluation framework of wind turbine blade cross-section based on gradient boosting regression tree. Composite Structures, 337, 118055.

Guo, G., Zhu, W., Sun, Z., Shen, W., Cao, J., & Fu, S. (2023). Drag reducer design of wind turbine blade under flap-wise fatigue testing. Composite Structures, 318, 117094.

Zhu, W., Liu, J., Sun, Z., Cao, J., Guo, G., & Shen, W. (2022). Numerical study on flow and noise characteristics of an NACA0018 airfoil with a porous trailing edge. Sustainability, 15(1), 275.

Li, S., Zhang, L., Xu, J., Yang, K., Song, J., & Guo, G. (2020). Experimental investigation of a pitch-oscillating wind turbine airfoil with vortex generators. Journal of Renewable and Sustainable Energy, 12(6), 063304.