Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4407
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dc.contributor.authorDas, Anurag-
dc.contributor.authorSengupta, Ananyo-
dc.date.accessioned2024-02-20T11:43:21Z-
dc.date.available2024-02-20T11:43:21Z-
dc.date.issued2024-01-
dc.identifier.citationThird International Conference on Power, Control and Computing Technologies(ICPC²T –2024), NIT Raipur, India, Hybrid, 18-20 January 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4407-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThe 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.en_US
dc.subjectLoad Frequency Controlen_US
dc.subjectStochastic Load Frequency Controlen_US
dc.subjectOptimal Schedulingen_US
dc.subjectSensitivity Analysisen_US
dc.subjectRenewable Energy Sourcesen_US
dc.subjectChance Constraintsen_US
dc.titleFrequency Management in Power Systems with Uncertain Loads and Generation: A Model Predictive Control Approachen_US
dc.typeArticleen_US
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