Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam

Yeong-Nam Jeon and Jae-ha Ko ()
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Yeong-Nam Jeon: Department of AI Mechanical Convergence Engineering, Donggang University, 50 Dongmun-daero, Buk-gu, Gwangju 61200, Republic of Korea
Jae-ha Ko: Department of Electrical Engineering, Honam University, 417 Eodeung-daero, Gwangsan-gu, Gwangju 62399, Republic of Korea

Energies, 2025, vol. 18, issue 12, 1-29

Abstract: This study proposes a forecast-aided energy management strategy tailored for industrial microgrids operating in Vietnam’s tropical climate. The core novelty lies in the implementation of a converter-based EMS that enables bidirectional DC power exchange between multiple subsystems. To improve forecast accuracy, an artificial neural network (ANN) is used to model the relationship between electric load and localized meteorological features, including temperature, dew point, humidity, and wind speed. The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. The strategy is validated using real-site data from a Vietnamese industrial complex, where the proposed method demonstrates enhanced load prediction accuracy, cost-effective ESS operation, and multi-microgrid flexibility under weather variability. This integrated forecasting and control approach offers a scalable and climate-adaptive solution for EMS in emerging industrial regions.

Keywords: load forecasting; artificial neural networks; Vietnam; meteorological data; MAPE; time series; energy prediction; industrial forecasting; energy efficiency; PSO; EMS; converter-based control; microgrid; DC grid; energy trading; battery scheduling; forecast-aided EMS (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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