Mehdi Shafiee | Power System | Editorial Board Member

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Dr. Mehdi Shafiee | Power System | Editorial Board Member

Faculty Member at Technical and Vocational University | Iran

Dr. Mehdi Shafiee is a committed researcher in advanced power and energy systems, with notable contributions to power system flexibility, resiliency, smart grids, renewable energy integration, and intelligent energy management. His work focuses on probabilistic modeling, robust and multi-objective optimization, and advanced control strategies for modern power networks involving electric vehicles, distributed generation, and hybrid renewable systems. He has contributed extensively to areas such as transactive energy scheduling, load frequency control using fractional-order fuzzy and adaptive PID controllers, microgrid energy management via multi-agent systems, voltage stability enhancement, and flexibility-based approaches to unit commitment under uncertainty. His research portfolio includes impactful publications in international journals and conferences, covering themes like ancillary service market participation by electric vehicles, dynamic assessment of hybrid wind–PV–battery systems, optimal resource scheduling, and innovative methods for distributed generation placement. He has also co-authored books offering foundational and practical knowledge on electric circuits and power distribution system design. With 89 citations, 18 publications, an h-index of 5, and recognition through 84 citing documents, Dr. Shafiee has established a growing research footprint. His academic and scholarly contributions reflect a strong commitment to advancing sustainable, resilient, and smart energy systems aligned with the evolving demands of modern power grids.

Profiles: Scopus | Google Scholar

Featured Publications

Esmaeili, S., & Shafiee, M. (2012). Simulation of dynamic response of small wind-photovoltaic-fuel cell hybrid energy system. Smart Grid and Renewable Energy, 3(3), 194.

Shafiee, M., Ghazi, R., & Moeini-Aghtaie, M. (2019). Day-ahead resource scheduling in distribution networks with presence of electric vehicles and distributed generation units. Electric Power Components and Systems, 47(16–17), 1450–1463.

Estabragh, M. R., Mohammadian, M., & Shafiee, M. (2012). A novel approach for optimal allocation of distributed generations based on static voltage stability margin. Turkish Journal of Electrical Engineering and Computer Sciences, 20(7), 1044–1058.

Shafiee, S. S., & Shafiee, M. (2009). Dynamic performance of Wind/PV/Battery/Fuel-cell hybrid energy system. Journal of International Review on Modelling and Simulation, 2.

Shafiee, M., Vahidi, B., Hosseinian, S. H., & Jazebi, S. (2008). Using artificial neural network to estimate maximum overvoltage on cables with considering forward and backward waves. In Proceedings of the 43rd International Universities Power Engineering Conference (pp. 1–8). IEEE.

 

 

Krishna Pavan Inala | Smart Grids | Best Researcher Award

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Assist. Prof. Dr. Krishna Pavan Inala | Smart Grids | Best Researcher Award

Assistant Professor at BV Raju Institute of Technology | India

Dr. Krishna Pavan Inala is an accomplished researcher specializing in communication networks for smart grids, electric vehicle integration, and intelligent energy management systems. His research advances focus on Vehicle-to-Grid (V2G) communication, distributed grid voltage stability, communication-system impacts on power networks, and machine-learning-based load forecasting. He has published impactful journal articles in leading venues such as IEEE Systems Journal, IEEE Transactions on Industrial Informatics, IET Power Electronics, and the International Journal of Ambient Energy. His research output is well-recognized, with 103 citations, an h-index of 5, and an i10-index of 2 (with 100 citations, h-index 5, and i10-index 2 since 2020). His conference and book-chapter contributions, presented in prominent IEEE conferences and Springer proceedings, span optimal load prediction using machine learning, AI and IoT applications in smart-grid security, deep learning for household demand forecasting, and the role of communication networks in V2G environments. His expertise includes MATLAB, Python, communication-network simulators, and advanced data-analytics tools, enabling him to develop and evaluate innovative solutions for smart-grid communication reliability, predictive modeling, and energy optimization. In teaching and mentoring roles, he integrates concepts from communication engineering, artificial intelligence, and power systems to guide student research and project development. Dr. Inala’s research vision emphasizes building intelligent, secure, and resilient smart-grid infrastructures through robust communication frameworks, AI-driven forecasting, and sustainable energy technologies, contributing significantly to the evolution of next-generation smart electric systems.

Profile: Google Scholar

Featured Publications

1. Sah, B., Kumar, P., Rayudu, R., Bose, S. K., & Inala, K. P. (2018). Impact of sampling in the operation of vehicle to grid and its mitigation. IEEE Transactions on Industrial Informatics, 15(7), 3923–3933.

2. Inala, K. P., Sah, B., Kumar, P., & Bose, S. K. (2020). Impact of V2G communication on grid node voltage at charging station in a smart grid scenario. IEEE Systems Journal, 15(3), 3749–3758.

3. Inala, K. P., Kumar, P., & Bose, S. K. (2019). Impact of communication systems on grid node voltage and operation of a vehicle-to-grid controller in a smart-grid scenario. IET Power Electronics, 12(13), 3499–3509.

4. Inala, K. P., & Thirugnanam, K. (2022). Role of communication networks on vehicle-to-grid (V2G) system in a smart grid environment. In Proceedings of the 2022 4th International Conference on Energy, Power and Environment (ICEPE) (pp. 1–5). IEEE.

5. Manojkumar, R., & Inala, K. P. (2024). Optimised rule-based peak shaving and its impact on annual energy cost reduction. International Journal of Ambient Energy, 45(1), 2397672.