Rahmat-Allah Hooshmand (M’10) received the B.Sc. degree from University of Mashhad in 1989, the M.Sc. degree from University of Tehran, Iran and PhD degree from Tarbiat Modarres University, Iran in 1990 and 1995, respectively all in Electrical Engineering. He is currently Professor in Electrical Engineering Department, University of Isfahan, Isfahan, Iran. His main area of research interests are renewable energy systems, energy management of smart grids and power market.
His main area of research interests are renewable energy systems, energy management of smart grids and power market.
- Power System Operation, Control
- Power System Restructuring and Deregulation
- Power System Reliability
- Intelligent system in Power systems
- High-Voltage Engineering
- Bachelor, Power Engineering, Mashhad University [Mashhad - IRAN]
- Master's degree, Power Engineering, Tehran [Tehran - IRAN]
- Ph.D., Electrical Engineering, Tarbiat Modarres [Tehran - IRAN]
Undergraduate:
- High Voltage Engineering,
- Power Generation in Power Plants
Graduate:
- Power System Operation & Control, - Reactive Power Control
- Power System Restructuring and Deregulation,
- Power System Reliability,
Articles
Publication Date: 2026
Electric Power Systems Research (03787796)255
False Data Injection (FDI) attacks pose significant threats to the stability and security of modern power systems by covertly altering measurement data. This study focuses on stealthy FDI attacks, where an attacker subtly manipulates measurements within a localized region, leveraging limited network information to overload specific transmission lines. To address this challenge, a deep learning framework based on Multi-Task Transformer with Adaptive Attention and Uncertainty-Aware Recovery (MTT-AUR) is proposed. MTT-AUR performs simultaneous attack detection, region and bus-level localization, and measurement recovery solely based on snapshot measurements. MTT-AUR integrates adaptive multi-scale attention and an uncertainty-aware recovery module with a gating mechanism, which enables precise reconstruction of compromised measurements. A multi-task loss function based on uncertainty dynamically balances the four tasks, optimized through variable coefficients determined by each task's uncertainty. Simulations on the IEEE 39-bus system were conducted to evaluate and compare the performance of MTT-AUR against existing deep learning and machine learning methods. Additional experiments on the IEEE 118-bus system demonstrate the scalability and robustness of the model across all four tasks. © 2026 Elsevier B.V.
Publication Date: 2025
Sustainable and Resilient Infrastructure (23789689)10(1)pp. 40-62
Extreme dust storms (EDSs) are high-impact low-probability natural disasters, and their occurrence in humid climates can damage the power distribution systems (PDSs) as a critical infrastructure. In this paper, proposed a bi-level stochastic framework for simultaneously hardening substations and distribution lines. In the first level, total capital cost is addressed for PDS hardening under the financial constraints, while in the second level, the expected operating costs are minimized in the case of an EDS under the operating constraints. In the proposed model, the location of remote-controled switches (RCSs) is determined based on the PDS hardening planning results, and the decisions at each level depend on the planning results of the other level. The simulation results at different budget levels show that simultaneous hardening planning of distribution lines and substations considering network reconfiguration can not only reduce expected operating costs, but also can reducing total capital cost to PDS resilience enhancement. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2025
IET Generation, Transmission and Distribution (17518687)19(1)
Peak load management is a pivotal aspect of power generation and distribution, representing one of the primary challenges for power companies. A key feature of smart grids is their capability to manage available resources effectively to mitigate peak load while accounting for the inherent uncertainties in load demand and the generation of all renewable energy sources. Thereby, this paper proposes a two-stage coordination approach that integrates price-based demand response (PBDR) and energy storage systems, encompassing Battery Energy Storage Systems (BESS) and Compressed Air Energy Storage (CAES). This approach integrates CAES with BESSs to optimise the charging and discharging processes while minimising degradation costs. Specifically, it aims to address the substantial degradation expenses of BESSs by strategically utilising CAES as a complementary storage solution. The objective is to minimise operational costs while controlling peak demand load in smart microgrids. Moreover, to simultaneously address the inherent uncertainties associated with the demanded load and the generating power of renewable energy sources, a method incorporating scenario generation and reduction is introduced to improve scheduling accuracy and enhance the reliability of energy management. To tackle this multifaceted challenge, a novel scenario-based Developed Two-Stage Interval Optimisation (DTSIO) model has been proposed to effectively address uncertainty. By employing the scenario generation method in conjunction with the k-means technique to reduce scenarios with low probabilities of occurrence, the analysis process is optimised for better problem-solving efficiency. The proposed model's efficacy is validated through its implementation on a 33 and 69 bus microgrid, showcasing its ability to enhance profitability, manage peak load, reduce reliance on the upstream grid, and lower carbon dioxide emissions. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Publication Date: 2025
Reliability Engineering and System Safety (0951-8320)254
With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model. © 2024 Elsevier Ltd
Publication Date: 2025
IEEE Access (21693536)13pp. 67301-67322
Energy systems and their related technologies are susceptible to natural extreme events, categorized as high-impact low-probability (HILP) events, posing a significant threat to their reliable functioning. Gas-to-power (G2P) and power-to-gas (P2G) technologies establish a bidirectional interface between these energy systems, leading to the creation of integrated power and natural gas systems (IPGS) as energy systems. Due to their extensive geographical coverage, energy systems are particularly vulnerable to severe damage from natural calamities. Given the growing interest and research focus on energy systems resilience, this comprehensive review meticulously navigates the intricate terrain of resilience differentiation, offering a detailed roadmap fortified by insights from significant instances of grid failures and weather-driven contingencies. The review examines preemptive cyber security fortifications and strategic planning imperatives, scrutinizing each aspect with conviction and clarity. Temporally stratified into long-term and short-term horizons, it not only delineates prevailing approaches and methodologies but also identifies emerging trends poised to shape the future landscape of resilience enhancement. This paper provides an exhaustive review of existing research on the resilience of energy systems, introducing a visual framework for comparing different studies and facilitating easy understanding through multiple figures. Since uncertainties play a crucial role in decision-making within this field, this paper broadly explores methods for addressing them as presented in previous studies. Furthermore, the literature is meticulously classified to offer a clear and organized overview, highlighting the impact of HILP events, such as natural disasters and cyber-attacks, on energy systems resilience. This review underscores the critical need to fortify energy systems, emphasizing its importance as a crucial component of integrated energy systems in preparing for the continuous impact of natural disasters in future research. © 2013 IEEE.
