16 citations · 16 across the 5 of their papers we have counts for
7 papers
A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems
Ognjen Stanojev, Ognjen Kundacina, Uros Markovic +3
The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The…
A Stochastic-Robust Approach for Resilient Microgrid Investment Planning Under Static and Transient Islanding Security Constraints
Agnes Marjorie Nakiganda, Shahab Dehghan, Uros Markovic +2
When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unsc…
Improving Dynamic Performance of Low-Inertia Systems through Eigensensitivity Optimization
Ashwin Venkatraman, Uros Markovic, Dmitry Shchetinin +3
An increasing penetration of renewable generation has led to reduced levels of rotational inertia and damping in the system. The consequences are higher vulnerability to disturbanc…
Economic Valuation and Pricing of Inertia in Inverter-Dominated Power Systems
Matthieu Paturet, Uros Markovic, Stefanos Delikaraoglou +3
This paper studies the procurement and pricing of inertial response using a frequency-constrained unit commitment formulation, which co-optimizes the provision of energy and inerti…
MPC-Based Fast Frequency Control of Voltage Source Converters in Low-Inertia Power Systems
Ognjen Stanojev, Uros Markovic, Petros Aristidou +3
A rapid deployment of renewable generation has led to significant reduction in the rotational system inertia and damping, thus making frequency control in power systems more challe…
Towards Optimal System Scheduling with Synthetic Inertia Provision from Wind Turbines
Zhongda Chu, Uros Markovic, Gabriela Hug +1
The undergoing transition from conventional to converter-interfaced renewable generation leads to significant challenges in maintaining frequency stability due to declining system…