Search Results for author: Mads R. Almassalkhi

Found 5 papers, 0 papers with code

Reach and hold flexibility characterization and trade-off analysis for aggregations of thermostatically controlled loads

no code implementations21 May 2024 Mazen Elsaadany, Mads R. Almassalkhi

In this paper, the dynamic behavior of a TCL fleet is modeled and used to characterize the set possible changes in aggregate demand that can be reached and the corresponding time for which the demand change can be held, for a given change in setpoint.

Optimization-based Framework for Selecting Under-frequency Load Shedding Parameters

no code implementations9 Jan 2024 Waheed Owonikoko, Mazen Elsaadany, Amritanshu Pandey, Mads R. Almassalkhi

High penetration of renewable resources results in a power system with lower inertia and higher frequency sensitivity to power imbalances.

Improving frequency response with synthetic damping available from fleets of distributed energy resources

no code implementations26 Jul 2023 Hani Mavalizadeh, Luis A. Duffaut Espinosa, Mads R. Almassalkhi

Furthermore, spectral analysis of historical frequency regulation data is used to produce a probabilistic bound on the expected available synthetic damping for primary frequency control from a fleet and the trade-off from concurrently providing secondary frequency control services.

Statistical Modeling and Forecasting of Automatic Generation Control Signals

no code implementations15 May 2022 Sarnaduti Brahma, Hamid R. Ossareh, Mads R. Almassalkhi

The performance of frequency regulating units for automatic generation control (AGC) of power systems depends on their ability to track the AGC signal accurately.

Model Predictive Control Time Series +1

Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid

no code implementations12 Apr 2022 Samuel Chevalier, Mads R. Almassalkhi

To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network.

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