Studi Pengendalian Aliran Daya Pada Jaringan Listrik Tenaga Surya Terintegrasi Menggunakan Model Predictive Control - Raihan Fahrezi; Hamdani; Beni Satria (CV. Hawari, 2026)
The global energy transition towards sustainable sources has driven massive integration of Solar Power Plants (PLTS) into electricity systems, yet their intermittent nature presents significant operational challenges, particularly in low-voltage distribution networks. This research investigates the application of Model Predictive Control (MPC) for optimal power flow management in a 220V electrical network integrated with photovoltaic generation. The study addresses two main problems: designing an effective MPC strategy to control active and reactive power flow against solar irradiance variability, and quantitatively evaluating its performance compared to conventional Proportional-Integral (PI) control. Using MATLAB/Simulink simulation, a radial 5-bus 220V distribution network model with 10 kVA PV capacity was developed. The MPC algorithm was implemented with a prediction horizon of 15 steps, control horizon of 5 steps, and 1-second sampling time, optimizing voltage regulation and power loss minimization while respecting system constraints. Simulation results across three operational scenarios—irradiance step change, load variation, and combined disturbance—demonstrated MPC's superior performance. Specifically, MPC maintained voltage within ANSI C84.1 limits (±5%) with a Voltage Regulation Index of 0.021-0.025, achieved faster recovery times (8-12 seconds versus 18-25 seconds for PI), and reduced total power losses by 15-18%. The anticipatory control capability of MPC effectively mitigated voltage deviations caused by rapid irradiance fluctuations and load changes. These findings confirm that MPC offers a robust, proactive solution for enhancing stability and efficiency in low-voltage distribution networks with high solar penetration. The research contributes to renewable energy integration literature and provides practical insights for grid operators in managing power quality challenges.