Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4407
Title: Frequency Management in Power Systems with Uncertain Loads and Generation: A Model Predictive Control Approach
Authors: Das, Anurag
Sengupta, Ananyo
Keywords: Load Frequency Control
Stochastic Load Frequency Control
Optimal Scheduling
Sensitivity Analysis
Renewable Energy Sources
Chance Constraints
Issue Date: Jan-2024
Citation: Third International Conference on Power, Control and Computing Technologies(ICPC²T –2024), NIT Raipur, India, Hybrid, 18-20 January 2024
Abstract: The primary purpose of Load Frequency Control (LFC) is to minimize the frequency and tie-line power deviations in a power system, by optimally rescheduling the generation settings. However, the increasing penetration of Renewable Energy Sources (RESs) in a power system makes the conventional LFC more challenging, since the power output from RESs is unpredictable or stochastic. In this paper, a novel MPC based Stochastic Load Frequency Control (SLFC) technique is proposed, that enables high amounts of RESs to be integrated while maintaining reliable and stable operation. A structure-preserving linear statespace model for power systems is derived which more precisely represents a practical power system behavior. The proposed controller calculates optimal generation settings by minimizing an optimization problem subject to a set of constraints. To model the stochastic nature of RES power outputs, frequency deviation is added as a chance constraint in the optimization problem, transforming it to a chance constrained optimization problem. This ensures that the probability of frequency deviation at the end of prediction horizon, lying outside a specific range, is always less than some predefined confidence level. The proposed MPC based stochastic control technique is then tested on IEEE 39-bus standard test system under different operating conditions and for 200 different scenarios.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4407
Appears in Collections:Conference Papers

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