Mehdi Shafiee | Power System | Editorial Board Member

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.

 

 

Williams Ossai | Renewable Energy | Best Researcher Award

Mr. Williams Ossai | Renewable Energy | Best Researcher Award

Data and Analytics Specialist at Summit Media, Hull, United Kingdom

Williams Ossai is a UK-based Data and Analytics Specialist with a solid background in Artificial Intelligence, Data Science, and Physics/Electronics. He is known for integrating machine learning solutions into sectors such as energy, media, and healthcare. His work effectively bridges the gap between industry and applied research, particularly focusing on renewable energy adoption and health data science for social impact. He also engages in consultancy, offering data-driven strategies to top-tier organizations in emerging markets.

Publication Profile 

Google Scholar

Educational Background 🎓

  • Master’s Degree in Artificial Intelligence and Data Science,
    University of Hull, United Kingdom

  • Undergraduate Background in Physics/Electronics
    (Institution not specified in the form)

Professional Experience 💼

  • Current Role: Data and Analytics Specialist, Summit Media, Hull, United Kingdom

  • Sector Experience:

    • Energy: Market analytics, renewable energy modelling

    • Media: Data insights, analytics strategy

    • Health: Epidemiological data modelling with UK Biobank

  • Consultancy Roles:

    • Alpha One Solutions Limited: Analytics and strategy for renewable energy in emerging markets

    • Jinko Solar Co., Ltd: Strategy modelling across 11 West African countries

Research Interests 🔬

  • Applied Machine Learning

  • Renewable Energy Analytics

  • Health Data Science

  • Predictive Modelling for Social Impact

Research Projects

  • Completed:

    • Machine learning-based predictive modelling of renewable energy adoption

    • Improved ML approach for predicting solar energy adoption

  • Ongoing:

    • Association between physical activity and dementia in cancer populations (UK Biobank)

Awards and Honors🏆✨

  • Nominated for: Best Researcher Award under the Global Innovation Technologist Awards

  • Editorial Appointments: None currently

  • Professional Memberships:

    • Faith Driven Entrepreneurs

Collaborations

  • Co-author: Dr. Temitayo Fagbola

    • Affiliation: Centre of Excellence for Data Science, AI & Modelling, University of Hull

Conclusion🌟

Mr. Williams Ossai exhibits strong interdisciplinary expertise, integrating advanced analytics with domain knowledge to address global challenges. Although he has no formal academic publications or citations yet, his practical research applications, industry collaborations, and published books illustrate a trajectory focused on innovation and real-world impact. His work contributes to sustainable development and healthcare improvements in both developed and developing nations. His profile presents a strong case for recognition under the Best Researcher Award, with room to grow in academic dissemination and scientific publishing.

Publications 📚

📝 Ossai, Williams, and T. M. Fagbola. “Machine learning-based predictive modelling of renewable energy adoption in developing countries.” 🌱 Energy Reports, vol. 14, 2025, pp. 66–84.