Professor, Shahid Bahonar University of Kerman, Iran
Professor Amir Abdollahi, born on September 3, 1985, is a distinguished researcher and educator in power systems engineering. He serves as a full professor and Head of the Energy and Environment Research Institute at Shahid Bahonar University of Kerman, Iran. Prof. Abdollahi earned his Ph.D. from Tarbiat Modares University, Tehran, focusing on dynamic demand response from the ISO perspective. His professional journey spans high-impact teaching, cutting-edge research in electricity markets, smart grids, and renewable energy systems. Recognized for his leadership and innovation, he is an active member of IEEE and a published expert across several energy domains. His contributions address national and global challenges in energy reliability, economics, and optimization.
Profiles
🎓 Education
Professor Abdollahi’s academic journey reflects excellence across Iran’s premier institutions. He completed his Ph.D. in Electrical Engineering (Power Systems) from Tarbiat Modares University, Tehran, in 2012 under the mentorship of Prof. Mohsen Parsa Moghaddam. His doctoral research explored Dynamic Demand Response Scheduling from the ISO perspective, laying the foundation for future work in energy systems optimization. He holds a Master’s degree (M.Sc., 2009) from Sharif University of Technology, where he worked with Prof. Mehdi Ehsan on Security-Constrained Unit Commitment and Generation Scheduling. He began his academic pursuit with a B.Sc. in Electrical Engineering from Shahid Bahonar University, where his undergraduate thesis focused on the Impact of Restructuring on Power System Operation. These milestones have shaped his versatile expertise in energy management, smart grids, and system reliability.
👨🏫 Experience
Prof. Abdollahi brings over a decade of academic and research experience. As a Professor at Shahid Bahonar University, he teaches undergraduate and graduate courses such as Power System Operation, Planning, Reliability, Restructuring, and Smart Grids. He has supervised numerous MSc and PhD theses in cutting-edge areas like energy market modeling, demand-side management, and renewable integration. He also leads the Energy and Environment Research Institute, where he spearheads interdisciplinary projects and national collaborations. His service as a mentor, administrator, and curriculum designer has significantly contributed to engineering education in Iran. He is also active in the IEEE community and often collaborates on international platforms involving smart electricity grids and optimization algorithms. His dynamic presence bridges research, teaching, and innovation.
🔬 Research Focus
Prof. Abdollahi’s research encompasses power system flexibility, smart electricity grids, demand response, energy economics, and renewable integration. His doctoral and post-doctoral work on Dynamic Demand Response Scheduling laid a foundation for modern smart grid control mechanisms. He investigates ways to optimize electricity markets under uncertainty, often using game theory, multi-criteria decision making (MCDM), and hybrid optimization methods. His ongoing projects explore the interaction of distributed energy resources with power system operation, market simulation, and energy resilience strategies. He combines theoretical modeling with real-world scenarios, contributing solutions for grid reliability, peak load management, and market regulation in developing and developed contexts. With energy systems undergoing rapid digital transformation, his work stands at the intersection of engineering, economics, and sustainability.
📄 Publication Top Notes
1. Flexible demand response programs modeling in competitive electricity markets
Authors: M.P. Moghaddam, A. Abdollahi, M. Rashidinejad
Journal: Applied Energy, Volume 88, Issue 9, 2011, Pages 3257–3269
Cited by: 391
Summary:
This paper develops a detailed framework for modeling various flexible demand response (DR) programs in competitive electricity markets. It distinguishes between incentive-based and price-based mechanisms, incorporating customer behavior in response to market signals. By applying optimization techniques, the authors evaluate the impact of DR on market performance, load profiles, and system reliability. The study concludes that DR can significantly enhance both economic efficiency and grid stability.
2. Investigation of economic and environmental-driven demand response measures incorporating UC
Authors: A. Abdollahi, M.P. Moghaddam, M. Rashidinejad, M.K. Sheikh-El-Eslami
Journal: IEEE Transactions on Smart Grid, Volume 3, Issue 1, 2011, Pages 12–25
Cited by: 211
Summary:
This work integrates economic and environmental considerations into a unit commitment (UC) model enhanced with demand response. It proposes a flexible UC framework that incorporates DR as a scheduling tool for power system operators. Using scenario-based simulations, the authors demonstrate that DR reduces both operational costs and CO₂ emissions. The paper emphasizes the strategic role of DR in achieving sustainability goals in smart grid operations.
3. Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model
Authors: H. Khaloie, A. Abdollahi, M. Shafie-Khah, A. Anvari-Moghaddam, S. Nojavan, et al.
Journal: Applied Energy, Volume 259, 2020, Article 114168
Cited by: 159
Summary:
The study proposes a multi-stage stochastic model for coordinated operation of wind, thermal, and energy storage systems in energy and spinning reserve markets. The model effectively handles uncertainties in wind power and market prices, offering optimal bidding strategies to maximize profit while ensuring system reliability. This paper highlights how energy storage enhances the dispatchability of renewable energy and supports reserve provision in volatile market conditions.
4. A comprehensive sequential review study through the generation expansion planning
Authors: H. Sadeghi, M. Rashidinejad, A. Abdollahi
Journal: Renewable and Sustainable Energy Reviews, Volume 67, 2017, Pages 1369–1394
Cited by: 152
Summary:
This review comprehensively analyzes generation expansion planning (GEP) techniques, classifying them by modeling approaches, uncertainty treatment, and objective criteria (economic, environmental, technical). It covers classical methods, stochastic programming, robust optimization, and scenario analysis, providing a step-by-step understanding of GEP frameworks. The study also explores integration of renewable energy, environmental regulations, and modern computational tools, making it a valuable reference for researchers and planners.
5. Co-optimized bidding strategy of an integrated wind-thermal-photovoltaic system in deregulated electricity market under uncertainties
Authors: H. Khaloie, A. Abdollahi, M. Shafie-Khah, P. Siano, S. Nojavan, et al.
Journal: Journal of Cleaner Production, Volume 242, 2020, Article 118434
Cited by: 130
Summary:
This paper introduces a co-optimization strategy for hybrid renewable-conventional power systems (wind, thermal, and solar) in deregulated electricity markets. A stochastic programming approach accounts for uncertainties in generation, demand, and market prices. The findings show improved profitability and resilience of integrated energy systems. It also emphasizes the advantages of diversification and coordination among different energy sources under competitive market conditions.
6. The energy hub: An extensive survey on the state-of-the-art
Authors: H. Sadeghi, M. Rashidinejad, M. Moeini-Aghtaie, A. Abdollahi
Journal: Applied Thermal Engineering, Volume 161, 2019, Article 114071
Cited by: 104
Summary:
This extensive review presents the concept of the “energy hub” as a pivotal solution for managing multiple energy carriers (electricity, gas, heat, etc.) in a smart and integrated manner. It classifies energy hub models based on their mathematical formulation, control strategies, and optimization approaches. The review also discusses the role of energy hubs in smart cities and highlights future challenges in terms of uncertainty modeling, renewable integration, and cyber-physical system design.
7. Evaluation of plug-in electric vehicles impact on cost-based unit commitment
Authors: E. Talebizadeh, M. Rashidinejad, A. Abdollahi
Journal: Journal of Power Sources, Volume 248, 2014, Pages 545–552
Cited by: 101
Summary:
The paper investigates the influence of plug-in electric vehicles (PEVs) on traditional unit commitment strategies. A cost-based unit commitment model is enhanced by incorporating vehicle-to-grid (V2G) capabilities. The analysis reveals that coordinated charging and discharging of PEVs can flatten load profiles, improve generation scheduling, and reduce overall operational costs. This study showcases the benefits of integrating transportation electrification with power system operation.
8. Probabilistic multiobjective transmission expansion planning incorporating demand response resources and large-scale distant wind farms
Authors: A. Hajebrahimi, A. Abdollahi, M. Rashidinejad
Journal: IEEE Systems Journal, Volume 11, Issue 2, 2017, Pages 1170–1181
Cited by: 95
Summary:
This work introduces a probabilistic multiobjective framework for transmission expansion planning (TEP), considering both demand response and large-scale remote wind integration. Using a scenario-based optimization model, it evaluates trade-offs among cost, reliability, and environmental factors. The study emphasizes the significant impact of demand-side resources and renewables on reducing transmission investments and increasing system flexibility.
9. The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost
Authors: S. Dorahaki, A. Abdollahi, M. Rashidinejad, M. Moghbeli
Journal: Journal of Energy Storage, Volume 33, 2021, Article 102022
Cited by: 63
Summary:
The authors explore how energy storage and demand response can support energy democracy and enhance energy productivity in hybrid hub systems. A multi-objective optimization model is proposed, which includes social inconvenience costs—representing the discomfort experienced by users due to participation in DR programs. The findings advocate for people-centered energy policies that balance technical efficiency with consumer welfare.
10. Risk-based probabilistic-possibilistic self-scheduling considering high-impact low-probability events uncertainty
Authors: H. Khaloie, A. Abdollahi, M. Rashidinejad, P. Siano
Journal: International Journal of Electrical Power & Energy Systems, Volume 110, 2019, Pages 598–612
Cited by: 61
Summary:
This paper proposes a hybrid probabilistic-possibilistic model for the self-scheduling of power producers under uncertainty. It particularly addresses high-impact low-probability (HILP) events, such as extreme weather or cyberattacks. The model integrates risk-averse strategies with operational decision-making to maintain reliability and cost-effectiveness. The approach is validated using case studies that show how HILP scenarios influence bidding and reserve commitments in electricity markets.
Conclusion
Professor Amir Abdollahi is a highly qualified and influential academic in the field of Power Systems Engineering. His academic leadership, diverse teaching, and research focus on modern challenges in energy systems make him a strong candidate for the Best Researcher Award, particularly at the national or institutional level.