Reverse Power Flow Protection in Microgrids Using Time-Series Neural Network Models

Chan-Ho Bae, Yeoung-Seok Song, Chul-Young Park, Seok-Hoon Hong, So-Haeng Lee and Byung-Lok Cho ()
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Chan-Ho Bae: Department of Electronic Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
Yeoung-Seok Song: R&D Team, JRI Co., Ltd., Inseo 8-gil, Gwangyang-eup, Gwangyang 57755, Republic of Korea
Chul-Young Park: Department of Artificial Intelligence Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
Seok-Hoon Hong: R&D Center, TEF Co., Ltd., 60-12 Suncheon-ro, Seo-Myeon, Suncheon 57906, Republic of Korea
So-Haeng Lee: Blockchain Platform Research Center, Pusan National University, Busan 46241, Republic of Korea
Byung-Lok Cho: Department of Electronic Engineering, Sunchon National University, Suncheon 57922, Republic of Korea

Energies, 2025, vol. 18, issue 22, 1-24

Abstract: Renewable energy sources provide environmental and economic benefits by replacing conventional energy sources. In Korea, photovoltaic (PV) systems are increasingly deployed in apartment complexes and residential buildings. In self-consumption PV systems, surplus generation exceeding local demand often leads to a reverse power flow. This phenomenon becomes more frequent in microgrid environments where multiple distributed energy resources are interconnected. Accordingly, inverter control strategies based on generation forecasting have emerged as critical challenges. In this paper, we propose an on-device artificial intelligence model for inverter control that integrates net power forecasting with time-series neural networks. Two novel forecasting methods were proposed and introduced: Prediction-to-Prediction (P–P) and Net-Power Prediction (N–P). Various neural network models were trained and evaluated using multiple performance metrics. A novel threshold adjustment mechanism based on the mean absolute error was designed for inverter control. The control scenarios were analyzed by comparing the actual power losses with the forecast-based power losses, and the energy savings were quantified by adjusting the correction factor. The proposed forecasting methods achieved a reduction of approximately 40–70% in energy losses compared with the actual loss levels. The threshold adjustment strategy enhances flexibility in balancing the number of on/off switching events and the power loss, contributing to improved energy efficiency and system stability.

Keywords: renewable energy; power generation; power consumption; microgrid; reverse power flow; inverter control; on-device AI; time-series neural network; PV system (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: 2025
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