A Stochastic Model Predictive Control Method for Tie-Line Power Smoothing under Uncertainty

Molin An, Xueshan Han () and Tianguang Lu
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Molin An: School of Electrical Engineering, Shandong University, Jinan 250061, China
Xueshan Han: School of Electrical Engineering, Shandong University, Jinan 250061, China
Tianguang Lu: School of Electrical Engineering, Shandong University, Jinan 250061, China

Energies, 2024, vol. 17, issue 14, 1-17

Abstract: With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method.

Keywords: tie-line power smoothing; data-driven; linear power flow; stochastic model predictive control; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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