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Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 811-813
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 31-05-2026
Modern distribution grids face unprecedented volatility due to the rapid integration of Renewable Energy Sources (RES) and the high-power demand of Electric Vehicle (EV) charging stations. The Solid-State Transformer (SST) is a pivotal solution for managing these bi-directional power flows while providing high-frequency isolation. This paper proposes a Model Predictive Control (MPC)-based energy management strategy for a three-stage SST. The MPC framework utilizes a finite-control-set approach to regulate the high-voltage DC (HVDC) and low-voltage DC (LVDC) links while simultaneously minimizing reactive power at the Point of Common Coupling (PCC). A multi-objective cost function is developed to prioritize seamless power transfer between solar PV arrays, EV batteries, and the utility grid. Simulation results on a modified IEEE 13- bus test system demonstrate that the MPC controller outperforms traditional cascaded PI loops, providing a 40% faster transient recovery during cloud-cover transients and EV plug-in events.
Solid-State Transformer (SST), Model Predictive Control (MPC), Renewable Energy, Electric Vehicles, Energy Management, Smart Grids.
Preeti Singh, Shailendra Turkar, Gurucharan Mashram, & Preeti Rinhayat. (2026). Model Predictive Control-Based Energy Management for Solid-State Transformers Enabling Seamless Integration of Renewable Energy Sources and Electric Vehicle Charging in Smart Distribution Grids. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 811-813.
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