46 citations · 54 across the 14 of their papers we have counts for
12 papers · 1 filter
Feasibility-Aware Security-Constrained Unit Commitment via Hybrid Soft Actor-Critic with Quantum-Sampled Features
George Dimas, Amin Masoumi, Mert Korkali
Security-constrained unit commitment (SCUC) couples binary commitment, economic dispatch, reserves, and network security over a multiperiod horizon, making an exact solution comput…
Feasibility-Aware Security-Constrained Unit Commitment via Hybrid Soft Actor-Critic with Quantum-Sampled Features
George Dimas, Amin Masoumi, Mert Korkali
Security-constrained unit commitment (SCUC) couples binary commitment, economic dispatch, reserves, and network security over a multiperiod horizon, which makes an exact solution e…
Quantum-Accelerated Deep Reinforcement Learning for Frequency Regulation Enhancement
Amin Masoumi, Mert Korkali
In modern power systems, frequency regulation is a fundamental prerequisite for ensuring system reliability and assessing the robustness of expansion projects. Conventional feedbac…
Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learning
Amin Masoumi, Mert Korkali
Dynamic security control (DSC) is considered a pivotal step for the future power grid, which is increasingly penetrated by inverter-based resources. However, the efficiency of such…
Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
Amin Masoumi, Mert Korkali
This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The pr…
Deep-Learning-Directed Preventive Dynamic Security Control via Coordinated Demand Response
Amin Masoumi, Mert Korkali
Unlike common faults, three-phase short-circuit faults in power systems pose significant challenges. These faults can lead to out-of-step (OOS) conditions and jeopardize the system…
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment
Benjamin M. Peter, Mert Korkali
The increasingly challenging task of maintaining power grid security requires innovative solutions. Novel approaches using reinforcement learning (RL) agents have been proposed to…
Fast Critical Clearing Time Calculation for Power Systems with Synchronous and Asynchronous Generation
Xuezao Wang, Yijun Xu, Wei Gu +4
The increasing penetration of renewables is replacing traditional synchronous generation in modern power systems with low-inertia asynchronous converter-interfaced generators (CIGs…
Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents
Benjamin M. Peter, Mert Korkali
Reinforcement learning (RL) agents are powerful tools for managing power grids. They use large amounts of data to inform their actions and receive rewards or penalties as feedback…
An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System
Yijun Xu, Jaber Valinejad, Mert Korkali +4
The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring…
Probabilistic Load-Margin Assessment using Vine Copula and Gaussian Process Emulation
Yijun Xu, Kiran Karra, Lamine Mili +3
The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of powe…
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation
Zhixiong Hu, Yijun Xu, Mert Korkali +3
The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a…